Adaptive Tracking Control of State Constraint Systems Based on Differential Neural Networks: A Barrier Lyapunov Function Approach

State-restricted adaptive control of a multilevel rotating electromagnetic mechanical flexible device using electromagnetic actuators

This work presents the development of a multilevel electromagnetic actuation system that controls the shape of a flexible rotatory robotic structure. An array of electromagnets is used as the set of actuators that regulate the position of permanent magnets within the flexible device. The primary outcome of this study is the design and experimental validation of the multilevel rotating device. In addition, the theoretical description of the system motion under electromagnetic actuation is formulated using Euler-Lagrange and electromagnetic theories. Given the developed model, a theoretical study leads to designing an adaptive control that considers motion restrictions in the flexible device. The controller aims to modify the current applied to the electromagnets, which changes the interaction forces between the electromagnet and the permanent magnets in the robotic flexible structure. A set of numerical simulations confirms the proposed controller's effectiveness compared to the traditional state feedback approach that does not consider the state restrictions, which is implemented in devices that also operate under an electromagnetic approach. Furthermore, an experimental version of the flexible device allows for testing of the developed controller. The experimental results show the suitability of the proposed control to generate non-oscillatory controlled motion during the regulation of the flexible mechanic device shape.

Neural Networks for Portfolio Analysis in High-Frequency Trading

High-frequency trading proposes new challenges to classical portfolio selection problems. Especially, the timely and accurate solution of portfolios is highly demanded in financial market nowadays. This article makes progress along this direction by proposing novel neural networks with softmax equalization to address the problem. To the best of our knowledge, this is the first time that softmax technique is used to deal with equation constraints in portfolio selections. Theoretical analysis shows that the proposed method is globally convergent to the optimum of the optimization formulation of portfolio selection. Experiments based on real stock data verify the effectiveness of the proposed solution. It is worth mentioning that the two proposed models achieve 5.50% and 5.47% less cost, respectively, than the solution obtained by using MATLAB dedicated solvers, which demonstrates the superiority of the proposed strategies.

Resource-Aware Distributed Differential Evolution for Training Expensive Neural-Network-Based Controller in Power Electronic Circuit

The neural-network (NN)-based control method is a new emerging promising technique for controller design in a power electronic circuit (PEC). However, the optimization of NN-based controllers (NNCs) has significant challenges in two aspects. The first challenge is that the search space of the NNC optimization problem is such complex that the global optimization ability of the existing algorithms still needs to be improved. The second challenge is that the training process of the NNC parameters is very computationally expensive and requires a long execution time. Thus, in this article, we develop a powerful evolutionary computation-based algorithm to find a high-quality solution and reduce computational time. First, the differential evolution (DE) algorithm is adopted because it is a powerful global optimizer in solving a complex optimization problem. This can help to overcome the premature convergence in local optima to train the NNC parameters well. Second, to reduce the computational time, the DE is extended to distribute DE (DDE) by dispatching all the individuals to different distributed computing resources for parallel computing. Moreover, a resource-aware strategy (RAS) is designed to further efficiently utilize the resources by adaptively dispatching individuals to resources according to the real-time performance of the resources, which can simultaneously concern the computing ability and load state of each resource. Experimental results show that, compared with some other typical evolutionary algorithms, the proposed algorithm can get significantly better solutions within a shorter computational time.

Smooth-Switching Gain Based Adaptive Neural Network Control of n-Joint Manipulator with Multiple Constraints

Modeling errors, external loads and output constraints will affect the tracking control of the n-joint manipulator driven by the permanent magnet synchronous motor. To solve the above problems, the smooth-switching for backstepping gain control strategy based on the Barrier Lyapunov Function and adaptive neural network (BLF-ANBG) is proposed. First, the adaptive neural network method is established to approximate modeling errors, unknown loads and unenforced inputs. Then, the gain functions based on the error and error rate of change are designed, respectively. The two gain functions can respectively provide faster response speed and better tracking stability. The smooth-switching for backstepping gain strategy based on the Barrier Lyapunov Function is proposed to combine the advantages of both gain functions. According to the above strategy, the BLF-ANBG strategy is proposed, which not only solves the influence of multiple constraints, unknown loads and modeling errors, but also enables the manipulator system to have better dynamic and steady-state performances at the same time. Finally, the proposed controller is applied to a 2-DOF manipulator and compared with other commonly used methods. The simulation results show that the BLF-ANBG strategy has good tracking performance under multiple constraints and model errors.

Automated and Controlled System for Analysis of Residual Limbs Thermograms of Transtibial Amputees

This work describes the development of a controlled cabin for capturing and analyzing thermal images. The motivation of such a device is to aid in the thermal image acquisition process within a confined space. The thermograms generated provide helpful information for analyzing the residual human limb in subjects with transtibial amputation. Such a study proposes a non-intrusive method to study the thermal activity on the amputee residual limb and seek a correlation to the quality of the socket. The proposed cabin ensures the repeatability of the thermograms acquisition process and provides an isolated workspace, thus improving the quality of the samples. The methodology consists of the design of the mechanical elements and parts of the system on computer-aided design software, the electronic instrumentation, a graphic user interface, and the control algorithm based on a barrier Lyapunov function to solve the trajectory tracking for the camera movements, and numerical simulations to illustrate the functionality and the manufacture of a prototype. The results obtained by implementing the control design on the automated cabin reveal that the thermal image acquisition process is completed following the desired trajectory with a mean squared tracking error of 0.0052. In addition, an example of the thermal images of two subjects and the results processing this class of pictures using the designed interface is shown.

Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion

Dynamic motion simulators cannot provide the same stimulation of sensory systems as real motion. Hence, only a subset of human senses should be targeted. For simulators providing vestibular stimulus, an automatic bodily function of vestibular–ocular reflex (VOR) can objectively measure how accurate motion simulation is. This requires a model of ocular response to enforced accelerations, an attempt to create which is shown in this paper. The proposed model corresponds to a single-layer spiking differential neural network with its activation functions are based on the dynamic Izhikevich model of neuron dynamics. An experiment is proposed to collect training data corresponding to controlled accelerated motions that produce VOR, measured using an eye-tracking system. The effectiveness of the proposed identification is demonstrated by comparing its performance with a traditional sigmoidal identifier. The proposed model based on dynamic representations of activation functions produces a more accurate approximation of foveal motion as the estimation of mean square error confirms.

Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

Control of differential-drive mobile robots for soft object deformation

The aim of this work is a control scheme implementation to deform a nonrigid object in which deformation dynamics are modeled by the finite element method. The deformation of a soft object is highly difficult to model because of its non-linearity, time-dependency, and material-response characteristics. Thus, the control implementation for Differential Drive Mobile Robots (DDMR) to deform an elastic object, is a challenge. The proposed steps to solve it are: Position-control designed over DDMR kinematics. Alignment-control applied for DDMRs orientation. The desired shape of the object is achieved using two contact points as the control nodes. A centralized vision algorithm was employed in each stage to obtain positions. To show the usefulness of the proposed scheme, numerical simulation, and real-time implementation were carried out.

Command filtering-based adaptive control for chaotic permanent magnet synchronous motors considering practical considerations

In this paper, an efficient adaptive control is designed for chaotic Permanent Magnet Synchronous Motors (PMSMs) with full-state asymmetric time-varying constraints in the input saturation presence. The strategy that is suggested in this work is equipped with the command filtering for addressing the problem of the "explosion of complexity" available in the common backstepping method. In addition, the filtering errors are incorporated into the control design procedure for improving the control system performance. During the control design, the asymmetric barrier Lyapunov functions (BLFs) are employed so that the restriction of state variables in the given intervals is guaranteed. In the suggested control method, for approximating unknown nonlinear functions, the Bessel series is utilized as a simple but effective function approximation approach as a universal approximator. The presented design provides this advantage that by considering practical considerations, a reduced-order observer is also designed so that there is no need to mount the physical sensors to measure the position and the velocity of the chaotic PMSM. The Lyapunov stability theory is used to establish the boundedness of all the closed-loop signals. According to the comparative results obtained with neural networks, the presented control design is able to suppress the chaotic behavior of the PMSM drive system while ensuring an excellent tracking performance.

Dynamic Neural Network-Based Adaptive Tracking Control for an Autonomous Underwater Vehicle Subject to Modeling and Parametric Uncertainties

This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.