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Chen D, Lu T, Li G. A survey of methods for handling initial state shifts in iterative learning control. Heliyon 2023; 9:e22492. [PMID: 38046142 PMCID: PMC10686873 DOI: 10.1016/j.heliyon.2023.e22492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/12/2023] [Accepted: 11/14/2023] [Indexed: 12/05/2023] Open
Abstract
This paper introduces three types of controllers: a PID-type iterative learning controller, an adaptive iterative learning controller, and an optimal iterative learning controller, and reviews the history and research status of initial shifts rectifying algorithms. Initial state shifts have attracted research attention because they affect both the tracking performance and system stability. This study focuses on the current common initial shifts rectifying methods and analyzes the underlying mechanism in detail. To verify the effectiveness of the presented initial shifts rectifying algorithms, we simulated those using ideal first- and second-order systems. Finally, directions for the future development of iterative learning control (ILC) and some challenging topics related to initial shifts rectifying for ILC are presented. This article aims to introduce recent developments and advances in initial shifts rectifying algorithms and discuss the directions for their further exploration.
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Affiliation(s)
- Dongjie Chen
- Basic Courses Department, Zhejiang Police College, Hangzhou, 310053, China
| | - Tiantian Lu
- Basic Courses Department, Zhejiang Police College, Hangzhou, 310053, China
| | - Guojun Li
- Basic Courses Department, Zhejiang Police College, Hangzhou, 310053, China
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2
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Wang R, Zhuang Z, Tao H, Paszke W, Stojanovic V. Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems. ISA Transactions 2023; 142:123-135. [PMID: 37573187 DOI: 10.1016/j.isatra.2023.07.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 05/02/2023] [Accepted: 07/28/2023] [Indexed: 08/14/2023]
Abstract
This paper proposes a Q-learning based fault estimation (FE) and fault tolerant control (FTC) scheme under iterative learning control (ILC) framework. Due to the repetitive demands on control actuators for repetitive tasks, ILC is sensitive to actuator faults. Moreover, unknown faults varying with both time and trial axes pose a challenge to the control performance of ILC. This paper introduces Q-learning algorithm for FE to continuously adjust the estimator and adapt the changing faults. Then, FTC is designed by adopting the norm-optimal iterative learning control (NOILC) framework, where the controller is adjusted based on the FE results from Q-learning to counteract the influence of faults. Finally, the simulation on the plant of a mobile robot verifies the effectiveness of the proposed algorithm.
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Affiliation(s)
- Rui Wang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
| | - Zhihe Zhuang
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
| | - Hongfeng Tao
- Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.
| | - Wojciech Paszke
- Institute of Automation, Electronic and Electrical Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland
| | - Vladimir Stojanovic
- Faculty of Mechanical and Civil Engineering, Department of Automatic Control, Robotics and Fluid Technique, University of Kragujevac, Kraljevo 36000, Serbia.
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3
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Wen S, Li H, Tao R. A 2-dimensional model framework for blood glucose prediction based on iterative learning control architecture. Med Biol Eng Comput 2023; 61:2593-2606. [PMID: 37395886 DOI: 10.1007/s11517-023-02866-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/07/2023] [Indexed: 07/04/2023]
Abstract
The accurate, timely, and personalized prediction for future blood glucose (BG) levels is undoubtedly needed for further advancement of diabetes management technologies. Human inherent circadian rhythm and regular lifestyle resulting in similarity of daily glycemic dynamics play a positive role in the prediction of blood glucose. Inspired by the iterative learning control (ILC) method in the field of automatic control, a 2-dimensional (2-D) model framework is constructed to predict the future blood glucose levels by taking both the short-range information within a day (intra-day) and long-range information between days (inter-day) into account. In this framework, the radial basis function neural network was applied to capture nonlinear relationships in glycemic metabolism, that is, short-range temporal dependence and long-range contemporaneous dependence on previous days. We build models for each patient, and the models were tested on the in silico datasets at various prediction horizons (PHs). The learning model developed in the 2-D framework successfully increases the accuracy and reduces the delay of predictions. This modeling framework provides a new point of view for BG level prediction and contributes to the development of personalized glucose management, such as hypoglycemia warning and glycemic control.
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Affiliation(s)
- Shuang Wen
- College of Information Sciences and Engineering, Northeastern University, No. 11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People's Republic of China
| | - Hongru Li
- College of Information Sciences and Engineering, Northeastern University, No. 11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People's Republic of China.
| | - Rui Tao
- College of Information Sciences and Engineering, Northeastern University, No. 11 St. 3, Wenhua Road, Heping District, Shenyang, 110819, People's Republic of China
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4
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Xu X, Chen J, Lu J. Fractional-order iterative learning control for fractional-order systems with initialization non-repeatability. ISA Trans 2023:S0019-0578(23)00435-4. [PMID: 37827906 DOI: 10.1016/j.isatra.2023.09.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 09/24/2023] [Accepted: 09/25/2023] [Indexed: 10/14/2023]
Abstract
The effect of initialization non-repeatability on iterative learning control performance for fractional-order systems has not been sufficiently investigated. It is a hidden deficiency that leads directly to the breaking of perfect tracking conditions in both theoretical analysis and real-world applications. Therefore, under the framework of general fractional-order nonlinear systems, this paper proposes an open-close loop Dα-type iterative learning control scheme based on system preconditioning and strictly derives two convergence conditions. By applying the preconditioning optimization strategy based on the short-memory principle, the tracking error due to initialization nonrepetition can converge to any desired range. Compared with the existing results, the proposed iteration scheme fully considers the complexity of the initialization and initial conditions of fractional-order systems, and provides several practical preconditioning methods to improve the tracking efficiency. Two numerical examples are presented to validate the above conclusions.
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Affiliation(s)
- Xiaofeng Xu
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Jinshui Chen
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Jiangang Lu
- State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
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Xu J, Li D, Zhang J. Extended state observer based dynamic iterative learning for trajectory tracking control of a six-degrees-of-freedom manipulator. ISA Trans 2023:S0019-0578(23)00427-5. [PMID: 37839933 DOI: 10.1016/j.isatra.2023.09.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 08/21/2023] [Accepted: 09/16/2023] [Indexed: 10/17/2023]
Abstract
With the development of industrial automation comes an ever, broadening number of application scenarios for manipulators along with increasing demands for their precise control. However, manipulator trajectory tracking control schemes often exhibit problems such as those related to high levels of coupling, complex calculations, and in various difficulties in application for industrial environments. For the problems of low accuracy in control and poor robustness of multiple-jointed robotic trajectory tracking, iterative learning control (ILC) with model compensation (MC) based on extended state observer (ESO) has been proposed for the trajectory tracking control of six-degrees-of-freedom (six-DOF) manipulators. The scheme has excellent features to overcome uncertainties in repetitive tasks, including unknown bounded perturbations that are external to the model or dynamic perturbations that are internal to the model. The proposed control strategy combines ESO, iterative learning, and MC, for precise control of trajectory tracking. Here, ESO is used to estimate disturbances, iterative learning allows fast and accurate control in repeated tasks, and the model-compensated control algorithm alleviates the necessary for many inverse operations. The convergence of our proposed control scheme is proved through Lyapunov function and time-varying approximation theory. Simulation and experimental results verify the validity of the proposed scheme.
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Affiliation(s)
- Jiahui Xu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Dazi Li
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
| | - Jinhui Zhang
- School of Automation, Beijing Institute of Technology, Beijing 100081, China.
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Fu H, Hu C, Yu D, Zhu Y, Zhang M. Cascaded iterative learning motion control of precision maglev planar motor with experimental investigation. ISA Trans 2023; 139:463-474. [PMID: 37012166 DOI: 10.1016/j.isatra.2023.03.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 02/09/2023] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
Cascaded iterative learning control (CILC) is explored for a magnetically levitated (maglev) planar motor to achieve excellent tracking motion performance in this paper. The CILC control method is based on traditional iterative learning control (ILC) with deeper iterations. CILC solves the difficulty of ILC in constructing perfect learning filter and low-pass filter to obtain excellent accuracy. Specifically, in CILC, the traditional ILC strategy is implemented several times by the operation of feedforward signal registering and clearing in a cascaded structure, which makes the motion error reach an accuracy level superior to traditional ILC even though the filters are imperfect. The fundamental principle, convergence and stability of CILC strategy are explicitly presented and analyzed. Through the structure of CILC, the repetitive component of the convergence error can be completely eliminated in theory, while the non-repetitive component is accumulated but the sum is bounded. Simulation investigation and comparative experimental investigation on maglev planar motor are both conducted. The results consistently show that the CILC strategy is not only superior to PID and model-based feedforward control, but also obviously outperforms traditional ILC. The CILC investigations on maglev planar motor also provide a clue that CILC has appreciable application prospect for precision/ultra-precision systems requiring extreme motion accuracy.
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Affiliation(s)
- Hong Fu
- State Key Lab of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-Precision Manufacture Equipment and Control, Tsinghua University, Beijing, 100084, China
| | - Chuxiong Hu
- State Key Lab of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-Precision Manufacture Equipment and Control, Tsinghua University, Beijing, 100084, China.
| | - Dongdong Yu
- State Key Lab of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-Precision Manufacture Equipment and Control, Tsinghua University, Beijing, 100084, China
| | - Yu Zhu
- State Key Lab of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-Precision Manufacture Equipment and Control, Tsinghua University, Beijing, 100084, China
| | - Ming Zhang
- State Key Lab of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China; Beijing Key Lab of Precision/Ultra-Precision Manufacture Equipment and Control, Tsinghua University, Beijing, 100084, China
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7
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Lu S, Jingzhuo S. Adaptive PI control of ultrasonic motor using iterative learning methods. ISA Trans 2023; 139:499-509. [PMID: 37002033 DOI: 10.1016/j.isatra.2023.03.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 06/19/2023]
Abstract
PI (proportional integral) controller is widely utilized in ultrasonic motor control systems. However, PI controllers which use fixed parameters is often difficult to ensure that the control performance meets the expectations. Based on the idea of iterative learning control (ILC), a new online adaptive adjustment method for PI control parameters is proposed. Simple P (proportional) type and PD (proportional differential) type iterative learning controllers are designed to adjust the proportional and integral coefficients of PI controller online. Using this method, the coefficients of PI controller are adaptively adjusted with the change of motor characteristics to ensure the control performance meets expectations. This method can improve the nonlinear expression ability of the controller, so that its input-output relationship can better match the characteristics of the controlled object, thereby improving the control performance. Results of comparative experiments indicate the validity of the proposed control strategy.
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Affiliation(s)
- Song Lu
- Department of Electrical Engineering, Henan University of Science and Technology, No. 263, KaiYuan Street, LuoYang, 471023, PR China
| | - Shi Jingzhuo
- Department of Electrical Engineering, Henan University of Science and Technology, No. 263, KaiYuan Street, LuoYang, 471023, PR China.
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8
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Chen Y, Freeman CT. Iterative learning control for piecewise arc path tracking with validation on a gantry robot manufacturing platform. ISA Trans 2023; 139:650-659. [PMID: 37059672 DOI: 10.1016/j.isatra.2023.03.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 12/06/2022] [Accepted: 03/31/2023] [Indexed: 06/19/2023]
Abstract
The piecewise arc path tracking problem is a common feature of manufacturing systems operating in a repetitive mode, e.g. assembly production lines. Here, the system end-effector must follow a spatial path without any specific temporal tracking constraints, which makes the temporal profile not fixed a priori. The technique of iterative learning control (ILC) is well-suited to handle this problem, since compared to classical feedback control methods, ILC is capable of learning from previous trial information to minimize the tracking error over repeated trials. This paper extends the ILC task description to address piecewise arc path tracking tasks, and further formulates a more general design framework than existing spatial ILC approaches. A comprehensive ILC algorithm is designed to handle this class of piecewise arc path tracking problems, and practical implementation instructions are provided. Validation is conducted on a gantry robot manufacturing testbed to confirm its feasibility and efficiency in practice with a comparison to existing methods showing its higher path tracking accuracy.
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Affiliation(s)
- Yiyang Chen
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, 215137, China.
| | - Christopher T Freeman
- School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, United Kingdom.
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9
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Dong C, Xing L, Wang H, Yu X, Liu Y, Ni D. Iterative learning control for lane-changing trajectories upstream off-ramp bottlenecks and safety evaluation. Accid Anal Prev 2023; 183:106970. [PMID: 36669457 DOI: 10.1016/j.aap.2023.106970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/01/2023] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
This paper proposes an iterative learning control framework for lane changing to improve traffic operation and safety at a diverging area nearby a highway off-ramp in an environment with connected and automated vehicles (CAVs). This framework controls CAVs in the off-ramp bottlenecks by imitating the trajectories optimized by machine learning algorithms. Next Generation Simulation (NGSIM) dataset is utilized as the raw data and filtered by cost function. The traffic models, including lane-changing decision (LCD) models and lane-changing execution (LCE) models, are completed by Random Forest (RF) and Back Propagation Neural Network (BPNN) algorithms. Based on simulation results, simulation data satisfying the predetermined criterion will be added to dataset in the next iteration. Various metrics are considered to evaluate the proposed framework systematically from both lateral and longitudinal aspects, including time exposed time-to collision (TET), time integrated time-to-collision (TIT), rear-end collision risk indexes (RCRI) and lane-changing risk index (LCRI). The results present that the iterative framework can decrease the longitudinal risk of the system by a factor of two times, and can reduce the lateral risk by 28.7%. When the CAVs market penetration rate (MPR) reaches 100%, the longitudinal and lateral risk values of the off-ramp system can be reduced by 90% and 35%, respectively. However, it is worth noting that only when the CAVs MPR reaches 50% does the system's value at risk change significantly.
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Affiliation(s)
- Changyin Dong
- Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 210096, PR China
| | - Lu Xing
- School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, Hunan 410076, PR China
| | - Hao Wang
- Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 210096, PR China.
| | - Xinlian Yu
- Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 210096, PR China
| | - Yunjie Liu
- Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, School of Transportation, Southeast University, Nanjing 210096, PR China
| | - Daiheng Ni
- University of Massachusetts, Amherst, MA 01003, United States
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10
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Yin Y, Yu W, Bu X, Yu Q. Security data-driven iterative learning control for unknown nonlinear systems with hybrid attacks and fading measurements. ISA Trans 2022; 129:1-12. [PMID: 35125214 DOI: 10.1016/j.isatra.2022.01.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/10/2021] [Accepted: 01/14/2022] [Indexed: 06/14/2023]
Abstract
To achieve the stabilization objective of a class of nonlinear systems with unknown dynamics, this paper studies the security data-driven control problem under iterative learning schemes, where the faded channels are suffering from randomly hybrid attacks. The networked attacks try to obstruct the data transmission by injecting the false data. The plant is transformed into a dynamic data-model with the iteration-related linearization method. Then, two data-driven control methods, including a compensation scheme multiplied by increasing gains, are designed by using incomplete I/O signals. The effectiveness of the algorithms and the influence brought by stochastic issues are analyzed theoretically. Finally, a numerical simulation and a tracking example of agricultural vehicles illustrate the validity of the design.
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Affiliation(s)
- Yanling Yin
- Research Center for Energy Economics, School of Business Administration, Henan Polytechnic University, Jiaozuo 454003, China
| | - Wei Yu
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Xuhui Bu
- Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China.
| | - Qiongxia Yu
- Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China
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Wang S, Chen J, He X. An adaptive composite disturbance rejection for attitude control of the agricultural quadrotor UAV. ISA Trans 2022; 129:564-579. [PMID: 35177262 DOI: 10.1016/j.isatra.2022.01.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 01/08/2022] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
Aiming at the problem that agricultural quadrotor UAV is easily disturbed in ultra-low altitude phenotype remote sensing and precision hovering of spraying, an adaptive composite anti-disturbance attitude controller is proposed for ground effect and propeller failure disturbances rejection. The adaptive composite disturbance rejection control (ACDRC) is composed of active disturbance rejection control (ADRC) and disturbance observer (DO) based on nominal inverse model, which is used to estimate wind disturbance, payload disturbance and propeller failure disturbance in real time. For the bandwidth tuning of the extended state observer (ESO), an online tuning method based on iterative learning control (ILC) is proposed to realize the adaptive extended state observer (ESO). And the stability of the composite anti-disturbance controller is analyzed. In the experiments, the wind disturbance experiments under the side-down flow and the horizontal flow, the failure experiments under the single propeller failure and twin propeller failure, and the composite disturbances experiments under the simultaneous action of the wind disturbance, propeller failure and payload disturbance are carried out. The experimental results show that under wind disturbance, the anti-disturbance performance of ACDRC is increased by 82.5%; under the disturbance of propeller fault, the anti-disturbance performance of ACDRC is increased by 60%; under the composite disturbance, the anti-disturbance performance of ACDRC is increased by 50%. Finally, the effectiveness of ACDRC is further verified in vegetable and cotton fields.
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Affiliation(s)
- Shubo Wang
- College of Engineering, China Agricultural University, 17 Qinghua East Rd., Beijing 100083, China.
| | - Jian Chen
- College of Engineering, China Agricultural University, 17 Qinghua East Rd., Beijing 100083, China.
| | - Xiongkui He
- Centre for Chemicals Application Technology, College of Science, China Agricultural University, 2 Yuanmingyuan West Rd., Beijing 100193, China; Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, 69 Xinwen Rd., Shenzhen 518000, China.
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Huang M, Deng Y, Li H, Wang J. Torque ripple attenuation of PMSM using improved robust two-degree-of-freedom controller via extended sliding mode parameter observer. ISA Trans 2022; 129:558-571. [PMID: 35164961 DOI: 10.1016/j.isatra.2022.01.033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 01/25/2022] [Accepted: 01/29/2022] [Indexed: 06/14/2023]
Abstract
Torque ripple caused by flux harmonics, nonlinearity of inverter and current measurements decreases the accuracy of the servo control system, which limits the application of permanent magnet synchronous motor (PMSM) with high precision requirement. To reduce torque ripple, this paper proposes an improved robust two-degree-of-freedom controller (IR-2DOFC) based on an extended sliding-mode parameter observer (ESMPO) for a PMSM. The IR-2DOFC is constructed around the 2DOFC with iterative learning control (ILC) and a series-connecting structure, which not only suppresses unmodeled disturbances and periodic components, but also attenuates the negative impact of ILC on the dynamic response. Meanwhile, to improve the robust stability of the IR-2DOFC, ESMPO identifies the mechanical parameters so that they can be employed to further establish the IR-2DOFC parameters. Additionally, the observed disturbances can be regarded as a feed-forward compensation component to the IR-2DOFC, which enhances the disturbance-rejection performance. Simulations and experiments show that the IR-2DOFC with ESMPO has an improved dynamic response performance, which exhibits better robustness with respect to internal and external load disturbances and harmonics torque compared with proportional-integral (PI) and PI-ILC controllers.
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Affiliation(s)
- Mingfei Huang
- Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Science, Changchun 130033, China; The University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongting Deng
- Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Science, Changchun 130033, China.
| | - Hongwen Li
- Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Science, Changchun 130033, China
| | - Jianli Wang
- Changchun Institute of Optics, Fine Mechanics, and Physics, Chinese Academy of Science, Changchun 130033, China
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Hu J, Lai H, Chen Z, Ma X, Yao B. Desired compensation adaptive robust repetitive control of a multi-DoFs industrial robot. ISA Trans 2022; 128:556-564. [PMID: 34756577 DOI: 10.1016/j.isatra.2021.10.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 10/04/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
In the presence of system coupling and dynamic uncertainties, extensive research has been conducted on the precise motion control of industrial manipulators with general reference trajectories. Since repetitive operations are common tasks in industrial applications, it is an essential and practical problem to further improve the control accuracy by taking advantage of the periodicity of the reference trajectory. In this paper, a desired compensation adaptive robust repetitive control is proposed for multi-DoFs industrial manipulators to perform repetitive tasks. Specifically, the link dynamics identified offline is compensated directly to decouple the system and capture the main characteristics of the link effect. Then, the uncertain friction is dealt with through an online adaptation scheme, in which the desired compensation is utilized to avoid measurement noise and chattering at low speed. And periodic disturbances are approximated by Fourier series expansion with unknown Fourier coefficients, which will be learned online. Finally, the robust feedback is designed to guarantee transient control accuracy and robustness against dynamic uncertainties. Comparative experiments on an industrial manipulator show that the proposed controller possesses better transient and steady-state control accuracy and error convergence rate.
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Affiliation(s)
- Jinfei Hu
- The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Han Lai
- The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Zheng Chen
- The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China; The Ocean College, Zhejiang University, Hangzhou 316021, China; Ocean Research Center of Zhoushan, Zhejiang University, Zhoushan 316021, China.
| | - Xin Ma
- School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA
| | - Bin Yao
- School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907, USA
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Molazadeh V, Zhang Q, Bao X, Sharma N. An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton. IEEE Trans Control Syst Technol 2022; 30:1021-1036. [PMID: 36249864 PMCID: PMC9560042 DOI: 10.1109/tcst.2021.3089885] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions via FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this paper, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.
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Affiliation(s)
- Vahidreza Molazadeh
- Department of Mechanical Engineering and Materials Science at University of Pittsburgh, Pittsburgh, PA, USA
| | - Qiang Zhang
- Joint Department of Biomedical Engineering at North Carolina State University and the University of North Carolina Chapel-Hill, Raleigh, NC, USA
| | - Xuefeng Bao
- Department of Biomedical Engineering at Case Western Reserve University, Cleveland, OH, USA
| | - Nitin Sharma
- Joint Department of Biomedical Engineering at North Carolina State University and the University of North Carolina Chapel-Hill, Raleigh, NC, USA
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15
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Liu Y, Shen D. An efficient algorithm for collaborative learning model predictive control of nonlinear systems. ISA Trans 2022; 121:1-10. [PMID: 33845999 DOI: 10.1016/j.isatra.2021.03.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 03/18/2021] [Accepted: 03/26/2021] [Indexed: 06/12/2023]
Abstract
This paper contributes to an efficiently computational algorithm of collaborative learning model predictive control for nonlinear systems and explores the potential of subsystems to accomplish the task collaboratively. The collaboration problem in the control field is usually to track a given reference over a finite time interval by using a set of systems. These subsystems work together to find the optimal trajectory under given constraints in this study. We implement the collaboration idea into the learning model predictive control framework and reduce the computational burden by modifying the barycentric function. The properties, including recursive feasibility, stability, convergence, and optimality, are proved. The simulation is presented to show the system performance with the proposed collaborative learning model predictive control strategy.
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Affiliation(s)
- Yanze Liu
- Beijing University of Chemical Technology, Chaoyang District 100029, Beijing, China.
| | - Dong Shen
- Renmin University of China, Haidian District 100872, Beijing, China.
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16
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Gu P, Tian S. Iterative learning control with high-order internal model for first-order hyperbolic systems. ISA Trans 2022; 120:70-77. [PMID: 33745694 DOI: 10.1016/j.isatra.2021.03.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
This paper studies the iterative learning control (ILC) algorithm for first-order hyperbolic systems. Unlike most of the ILC literature of distributed parameter systems, in the iteration domain, that require identical desired trajectories. Here the desired trajectories are iteratively varying and described by a high-order internal model (HOIM). The HOIM-based P-type ILC design is firstly introduced in this paper to the first-order hyperbolic systems, which enable the systems to achieve the perfect tracking for the iteration-varying desired trajectories on L2 space. Meanwhile, the convergence theorem of the proposed algorithm is established for first-order time-delay hyperbolic systems. Finally, simulation results testify the validity of the algorithm.
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Affiliation(s)
- Panpan Gu
- School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China.
| | - Senping Tian
- School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China.
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17
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Fathollahi S, Kruisz J, Sacher S, Rehrl J, Escotet-Espinoza MS, DiNunzio J, Glasser BJ, Khinast JG. Development of a Controlled Continuous Low-Dose Feeding Process. AAPS PharmSciTech 2021; 22:247. [PMID: 34642863 PMCID: PMC8510936 DOI: 10.1208/s12249-021-02104-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 07/31/2021] [Indexed: 11/30/2022] Open
Abstract
This paper proposes a feed rate control strategy for a novel volumetric micro-feeder, which can accomplish low-dose feeding of pharmaceutical raw materials with significantly different powder properties. The developed feed-forward control strategy enables a constant feed rate with a minimum deviation from the set-point, even for materials that are typically difficult to accurately feed (e.g., due to high cohesion or low density) using conventional continuous feeders. Density variations observed during the feeding process were characterized via a displacement feed factor profile for each powder. The characterized effective displacement density profile was applied in the micro-feeder system to proactively control the feed rate by manipulating the powder displacement rate (i.e., computing the feed rate from the powder displacement rate). Based on the displacement feed factor profile, the feed rate can be predicted during the feeding process and at any feed rate set-point. Three pharmaceutically relevant materials were used for the micro-feeder evaluation: di-calcium phosphate (large-particle system, high density), croscarmellose sodium (small-particle system, medium density), and barium sulfate (very small-particle <10 μm, high density). A significant improvement in the feeding performance was achieved for all investigated materials. The feed rate deviation from the set-point and its relative standard deviation were minimal compared to operations without the control strategy.
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18
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Patan K, Patan M. Neural-network-based iterative learning control of nonlinear systems. ISA Trans 2020; 98:445-453. [PMID: 31493874 DOI: 10.1016/j.isatra.2019.08.044] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 08/13/2019] [Accepted: 08/28/2019] [Indexed: 06/10/2023]
Abstract
This work reports on a novel approach to effective design of iterative learning control of repetitive nonlinear processes based on artificial neural networks. The essential idea discussed here is to enhance the iterative learning scheme with neural networks applied for controller synthesis as well as for system output prediction. Consequently, an iterative control update rule is developed through efficient data-driven scheme of neural network training. The contribution of this work consists of proper characterization of the control design procedure and careful analysis of both convergence and zero error at convergence properties of the proposed nonlinear learning controller. Then, the resulting sufficient conditions can be incorporated into control update for the next process trial. The proposed approach is illustrated by two examples involving control design for pneumatic servomechanism and magnetic levitation system.
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Affiliation(s)
- Krzysztof Patan
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
| | - Maciej Patan
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
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19
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Müller P, Del Ama AJ, Moreno JC, Schauer T. Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment. J Neuroeng Rehabil 2020; 17:36. [PMID: 32111245 PMCID: PMC7048130 DOI: 10.1186/s12984-020-0640-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 12/31/2019] [Indexed: 11/26/2022] Open
Abstract
Background FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the well-known foot switch triggered drop foot neuroprosthesis, was extended to a multichannel full-leg support neuroprosthesis enabling improved support and rehabilitation. However, these neuroprostheses had to be manually tuned and could not adapt to the persons’ individual needs. In recent research, a learning controller was added to the drop foot neuroprosthesis, so that the full stimulation pattern during the swing phase could be adapted by measuring the joint angles of previous steps. Methods The aim of this research is to begin developing a learning full-leg supporting neuroprosthesis, which controls the antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi- and plantarflexion during all gait phases. A method was established that allows a continuous assessment of knee and foot joint angles with every step. This method can warp the physiological joint angles of healthy subjects to match the individual pathological gait of the subject and thus allows a direct comparison of the two. A new kind of Iterative Learning Controller (ILC) is proposed which works independent of the step duration of the individual and uses physiological joint angle reference bands. Results In a first test with four people with an incomplete SCI, the results showed that the proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants. The other participant was more severely affected and had to be excluded due to the resulting false triggering of the gait phase detection. For two of the three remaining participants, a slight improvement in the average foot angles could be observed, for one participant slight improvements in the averaged knee angles. These improvements where in the range of 4circat the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion. Conclusions Direct adaptation to the current gait of the participants could be achieved with the proposed method. The preliminary first test with people with a SCI showed that the neuroprosthesis can generate individual stimulation patterns. The sensitivity to the knee angle reset, timing problems in participants with significant gait fluctuations, and the automatic ILC gain tuning are remaining issues that need be addressed. Subsequently, future studies should compare the improved, long-term rehabilitation effects of the here presented neuroprosthesis, with conventional multichannel FES neuroprostheses.
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Affiliation(s)
| | | | - Juan C Moreno
- Instituto Cajal, Spanish National Research Council (CSIC), Madrid, Spain
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20
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Tao H, Paszke W, Rogers E, Yang H, Gałkowski K. Finite frequency range iterative learning fault-tolerant control for discrete time-delay uncertain systems with actuator faults. ISA Trans 2019; 95:152-163. [PMID: 31178034 DOI: 10.1016/j.isatra.2019.05.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 05/22/2019] [Accepted: 05/28/2019] [Indexed: 06/09/2023]
Abstract
The subject area considered is discrete linear time delay systems operating repetitively on a finite time interval with actuator faults, where the system resets at the end of each operation. Regulation of the dynamics is by iterative learning control and performance goals imposed over finite frequency intervals for the case of uncertainty in the dynamic model. To derive the results, the generalized Kalman-Yakubovich-Popov lemma is used. A simulation based case study is also given to demonstrate the applicability of the new results.
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Affiliation(s)
- Hongfeng Tao
- Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, PR China.
| | - Wojciech Paszke
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
| | - Eric Rogers
- Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom.
| | - Huizhong Yang
- Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, PR China.
| | - Krzysztof Gałkowski
- Institute of Control and Computation Engineering, University of Zielona Góra, ul. Szafrana 2, 65-516 Zielona Góra, Poland.
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21
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Simba KR, Bui BD, Msukwa MR, Uchiyama N. Robust iterative learning contouring controller with disturbance observer for machine tool feed drives. ISA Trans 2018; 75:207-215. [PMID: 29475606 DOI: 10.1016/j.isatra.2018.02.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 01/15/2018] [Accepted: 02/07/2018] [Indexed: 06/08/2023]
Abstract
In feed drive systems, particularly machine tools, a contour error is more significant than the individual axial tracking errors from the view point of enhancing precision in manufacturing and production systems. The contour error must be within the permissible tolerance of given products. In machining complex or sharp-corner products, large contour errors occur mainly owing to discontinuous trajectories and the existence of nonlinear uncertainties. Therefore, it is indispensable to design robust controllers that can enhance the tracking ability of feed drive systems. In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed. The proposed controller was evaluated experimentally by using a typical sharp-corner trajectory, and its performance was compared with that of conventional controllers. The results revealed that the maximum contour error can be reduced by about 37% on average.
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Affiliation(s)
- Kenneth Renny Simba
- Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan; Directorate of Nuclear Technology, Tanzania Atomic Energy Commission, Arusha, Tanzania.
| | - Ba Dinh Bui
- Academy for Safety Intelligence, Graduate School of Engineering, Nagoya University, Nagoya, Aichi, 464-0814, Japan.
| | - Mathew Renny Msukwa
- Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan; Department of Electrical Engineering, University of Dar es Salaam, Dar es Salaam, Tanzania.
| | - Naoki Uchiyama
- Department of Mechanical Engineering, Toyohashi University of Technology, Toyohashi, Aichi, 441-8580, Japan.
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22
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Liu H, Li Y, Zhang Y, Chen Y, Song Z, Wang Z, Zhang S, Qian J. Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy. Micron 2018; 104:26-36. [PMID: 29054026 DOI: 10.1016/j.micron.2017.09.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Revised: 09/24/2017] [Accepted: 09/24/2017] [Indexed: 11/17/2022]
Abstract
Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance.
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Affiliation(s)
- Hui Liu
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China
| | - Yingzi Li
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China.
| | - Yingxu Zhang
- School of Instrumentation Science and Opto-electronics Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China
| | - Yifu Chen
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China
| | - Zihang Song
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China
| | - Zhenyu Wang
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China
| | - Suoxin Zhang
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China
| | - Jianqiang Qian
- School of Physics and Nuclear Energy Engineering, Beihang University,Xueyuan Road No.37, Haidian District, Beijing, 100191, China; Key Laboratory of Micro-nano Measurement-manipulation and Physics (Ministry of Education), Beihang University, Xueyuan Road No.37, Haidian District, Beijing, 100191, China.
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Yan B, Ren J, Zheng X, Liu Y, Zou Q. High-speed broadband monitoring of cell viscoelasticity in real time shows myosin-dependent oscillations. Biomech Model Mechanobiol 2017; 16:1857-1868. [PMID: 28597224 DOI: 10.1007/s10237-017-0924-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 06/01/2017] [Indexed: 10/19/2022]
Abstract
Study of the dynamic evolutions of cell viscoelasticity is important as during cell activities such as cell metastasis and invasion, the rheological behaviors of the cells also change dynamically, reflecting the biophysical and biochemical connections between the outer cortex and the intracellular structures. Although the time variations of the static modulus of cells have been investigated, few studies have been reported on the dynamic variations of the frequency-dependent viscoelasticity of cells. Measuring and monitoring such dynamic evolutions of cells at nanoscale can be challenging as the measurement needs to meet two objectives inherently contradictory to each other-the measurement must be broadband (to cover a large frequency spectrum) but also rapid (to capture the time-elapsed changes). In this study, we exploited a recently developed control-based nanomechanical protocol of atomic force microscope to monitor in real time the dynamic evolutions of the viscoelasticity of live human prostate cancer cells (PC-3 cells) and study its dependence on myosin activities. We found that the viscoelasticity of PC-3 cells, followed the power law, and oscillated at a period of about 200 s. Both the amplitude and the frequency of the oscillation strongly depended on the intracellular calcium and blebbistatin-sensitive motor proteins.
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Affiliation(s)
- Bo Yan
- School of Electrical and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Juan Ren
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Xi Zheng
- Department of Biochemical Biology, Rutgers University, Piscataway, NJ, USA
| | - Yue Liu
- Department of Biochemical Biology, Rutgers University, Piscataway, NJ, USA
| | - Qingze Zou
- Mechanical and Aerospace Engineering Department, Rutgers University, 98 Brett Road, Piscataway, NJ, 08854, USA.
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24
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Seel T, Werner C, Schauer T. The adaptive drop foot stimulator - Multivariable learning control of foot pitch and roll motion in paretic gait. Med Eng Phys 2016; 38:1205-1213. [PMID: 27396367 DOI: 10.1016/j.medengphy.2016.06.009] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2016] [Revised: 04/26/2016] [Accepted: 06/07/2016] [Indexed: 10/21/2022]
Abstract
Many stroke patients suffer from the drop foot syndrome, which is characterized by a limited ability to lift (the lateral and/or medial edge of) the foot and leads to a pathological gait. In this contribution, we consider the treatment of this syndrome via functional electrical stimulation (FES) of the peroneal nerve during the swing phase of the paretic foot. A novel three-electrodes setup allows us to manipulate the recruitment of m. tibialis anterior and m. fibularis longus via two independent FES channels without violating the zero-net-current requirement of FES. We characterize the domain of admissible stimulation intensities that results from the nonlinearities in patients' stimulation intensity tolerance. To compensate most of the cross-couplings between the FES intensities and the foot motion, we apply a nonlinear controller output mapping. Gait phase transitions as well as foot pitch and roll angles are assessed in realtime by means of an Inertial Measurement Unit (IMU). A decentralized Iterative Learning Control (ILC) scheme is used to adjust the stimulation to the current needs of the individual patient. We evaluate the effectiveness of this approach in experimental trials with drop foot patients walking on a treadmill and on level ground. Starting from conventional stimulation parameters, the controller automatically determines individual stimulation parameters and thus achieves physiological foot pitch and roll angle trajectories within at most two strides.
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Affiliation(s)
- Thomas Seel
- Control Systems Group, Technische Universität Berlin, Germany.
| | - Cordula Werner
- Neurological Rehabilitation, Charité Universitätsmedizin Berlin, Germany
| | - Thomas Schauer
- Control Systems Group, Technische Universität Berlin, Germany
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25
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Kutlu M, Freeman CT, Hallewell E, Hughes AM, Laila DS. Upper-limb stroke rehabilitation using electrode-array based functional electrical stimulation with sensing and control innovations. Med Eng Phys 2016; 38:366-79. [PMID: 26947097 DOI: 10.1016/j.medengphy.2016.01.004] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 01/11/2016] [Accepted: 01/31/2016] [Indexed: 10/22/2022]
Abstract
Functional electrical stimulation (FES) has shown effectiveness in restoring upper-limb movement post-stroke when applied to assist participants' voluntary intention during repeated, motivating tasks. Recent clinical trials have used advanced controllers that precisely adjust FES to assist functional reach and grasp tasks with FES applied to three muscle groups, showing significant reduction in impairment. The system reported in this paper advances the state-of-the-art by: (1) integrating an FES electrode array on the forearm to assist complex hand and wrist gestures; (2) utilising non-contact depth cameras to accurately record the arm, hand and wrist position in 3D; and (3) employing an interactive touch table to present motivating virtual reality (VR) tasks. The system also uses iterative learning control (ILC), a model-based control strategy which adjusts the applied FES based on the tracking error recorded on previous task attempts. Feasibility of the system has been evaluated in experimental trials with 2 unimpaired participants and clinical trials with 4 hemiparetic, chronic stroke participants. The stroke participants attended 17, 1 hour training sessions in which they performed functional tasks, such as button pressing using the touch table and closing a drawer. Stroke participant results show that the joint angle error norm reduced by an average of 50.3% over 6 attempts at each task when assisted by FES.
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Affiliation(s)
- M Kutlu
- Electronics and Computer Science, Faculty of Physical Sciences and Engineering, University of Southampton, UK.
| | - C T Freeman
- Electronics and Computer Science, Faculty of Physical Sciences and Engineering, University of Southampton, UK.
| | - E Hallewell
- Faculty of Health Sciences, University of Southampton, UK; Faculty of Health and Social Science, Bournemouth University, UK.
| | - A-M Hughes
- Faculty of Health Sciences, University of Southampton, UK.
| | - D S Laila
- Faculty of Engineering and the Environment, University of Southampton, UK.
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26
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Shan B, Wang J, Deng B, Wei X, Yu H, Li H. UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model. Cogn Neurodyn 2015; 9:31-40. [PMID: 26052360 PMCID: PMC4454128 DOI: 10.1007/s11571-014-9306-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2014] [Revised: 07/19/2014] [Accepted: 08/13/2014] [Indexed: 11/26/2022] Open
Abstract
A novel closed loop control framework is proposed to inhibit epileptiform wave in a neural mass model by external electric field, where the unscented Kalman filter method is used to reconstruct dynamics and estimate unmeasurable parameters of the model. Specifically speaking, the iterative learning control algorithm is introduced into the framework to optimize the control signal. In the proposed method, the control effect can be significantly improved based on the observation of the past attempts. Accordingly, the proposed method can effectively suppress the epileptiform wave as well as showing robustness to noises and uncertainties. Lastly, the simulation is carried out to illustrate the feasibility of the proposed method. Besides, this work shows potential value to design model-based feedback controllers for epilepsy treatment.
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Affiliation(s)
- Bonan Shan
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Jiang Wang
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Bin Deng
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Xile Wei
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Haitao Yu
- />School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072 People’s Republic of China
| | - Huiyan Li
- />School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222 People’s Republic of China
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