Fault-tolerant prescribed performance control of nonlinear systems with process faults and actuator failures

This paper investigates the fault-tolerant prescribed performance control problem for a class of multiple-input single-output unknown nonlinear systems subject to process faults and actuator failures. In contrast to the related works, we consider a general class of nonlinear systems with both multiplicative nonlinearities and additive nonlinearities corrupted by the process faults; only the boundedness of the process faults and the continuity of the nonlinear functions are required, without the explicit or fixed structures of the fault functions. To conquer this problem, a less-demanding and low-complexity fault-tolerant prescribed performance control approach is proposed. The controller is independent of the specific information of faults or the system model and does not invoke fault diagnosis or neural/fuzzy approximation to acquire such knowledge. It achieves the reference tracking with the predefined rate and accuracy. A comparative simulation on a single-link robot is conducted to illustrate the effectiveness and superiority of the proposed approach.

Performance Optimization and Fault-Tolerance of Highly Dynamic Systems Via Q-Learning With an Incrementally Attached Controller Gain System

High-performance and reliable control of systems that are highly dynamic and open-loop unstable is challenging but of considerable practical interest. Thus, this article investigates the performance optimization and fault tolerance of highly dynamic systems. First, an incremental control structure is proposed, where a controller gain system is attached to the predesigned controller, and by reconfiguring the controller gain system, the performance can be equivalently optimized as configuring the predesigned one. The incremental attachment of the controller gain system does not modify the existing control system, and it can be easily attached via various communication channels. Second, a structure integrating fault-tolerance strategy and hardware redundancy is proposed. Under this structure, command fusion and fault-tolerance strategies are developed where the control commands from different control units are optimally fused, and each control unit can be reconfigured w.r.t. the performance of the other ones. Furthermore, Q -learning algorithms are developed to realize the proposed structures and strategies in real-time model-freely. As such, varying operational conditions of the highly dynamic system can be tackled. Finally, the proposed structures and algorithms are validated case by case to show their effectiveness.

Event-Triggered Cooperative Model-Free Adaptive Iterative Learning Control for Multiple Subway Trains With Actuator Faults

This article investigates the issue of speed tracking and dynamic adjustment of headway for the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the repeatable nonlinear subway train system is transformed into an iteration-related full-form dynamic linearization (IFFDL) data model. Then, the event-triggered cooperative model-free adaptive iterative learning control (ET-CMFAILC) scheme based on the IFFDL data model for MSTs is designed. The control scheme includes the following four parts: 1) the cooperative control algorithm is derived by the cost function to realize cooperation of MSTs; 2) the radial basis function neural network (RBFNN) algorithm along the iteration axis is constructed to compensate the effects of iteration-time-varying actuator faults; 3) the projection algorithm is employed to estimate unknown complex nonlinear terms; and 4) the asynchronous event-triggered mechanism operated along the time domain and iteration domain is applied to lessen the communication and computational burden. Theoretical analysis and simulation results show that the effectiveness of the proposed ET-CMFAILC scheme, which can ensure that the speed tracking errors of MSTs are bounded and the distances of adjacent subway trains are stabilized in the safe range.

Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault

A data-driven distributed formation control algorithm is proposed for an unknown heterogeneous non-affine nonlinear discrete-time MIMO multi-agent system (MAS) with sensor fault. For the considered unknown MAS, the dynamic linearization technique in model-free adaptive control (MFAC) theory is used to transform the unknown MAS into an equivalent virtual dynamic linearization data model. Then using the virtual data model, the structure of the distributed model-free adaptive controller is constructed. For the incorrect signal measurements due to the sensor fault, the radial basis function neural network (RBFNN) is first trained for the MAS under the fault-free case, then using the outputs of the well-trained RBFNN and the actual outputs of MAS under sensor fault case, the estimation laws of the unknown fault and system parameters in the virtual data model are designed with only the measured input-output (I/O) data information. Finally, the boundedness of the formation error is analyzed by the contraction mapping method and mathematical induction method. The effectiveness of the proposed algorithm is illustrated by simulation examples.

Data-Driven Designs of Fault Detection Systems via Neural Network-Aided Learning

With the aid of neural networks, this article develops two data-driven designs of fault detection (FD) for dynamic systems. The first neural network is constructed for generating residual signals in the so-called finite impulse response (FIR) filter-based form, and the second one is designed for recursively generating residual signals. By theoretical analysis, we show that two proposed neural networks via self-organizing learning can find their optimal architectures, respectively, corresponding to FIR filter and recursive observer for FD purposes. Additional contributions of this study lie in that we establish bridges that link model- and neural-network-based methods for detecting faults in dynamic systems. An experiment on a three-tank system is adopted to illustrate the effectiveness of two proposed neural network-aided FD algorithms.

Incipient Fault Diagnosis for High-Speed Train Traction Systems via Stacked Generalization

Diagnosing the fault as early as possible is significant to guarantee the safety and reliability of the high-speed train. Incipient fault always makes the monitored signals deviate from their normal values, which may lead to serious consequences gradually. Due to the obscure early stage symptoms, incipient faults are difficult to detect. This article develops a stacked generalization (stacking)-based incipient fault diagnosis scheme for the traction system of high-speed trains. To extract the fault feature from the faulty data signals, which are similar to the normal ones, the extreme gradient boosting (XGBoost), random forest (RF), extra trees (ET), and light gradient boosting machine (LightGBM) are chosen as the base estimators in the first layer of the stacking. Then, the logistic regression (LR) is taken as the meta estimator in the second layer to integrate the results from the base estimators for fault classification. Thanks to the generalization ability of stacking, the incipient fault diagnosis performance of the proposed stacking-based method is better than that of the single model (XGBoost, RF, ET, and LightGBM), although they can be used to detect the incipient faults, separately. Moreover, to find out the optimal hyperparameters of the base estimators, a swarm intelligent optimization algorithm, pigeon-inspired optimization (PIO), is employed. The proposed method is tested on a semiphysical platform of the CRH2 traction system in CRRC Zhuzhou Locomotive Company Ltd. The results show that the fault diagnosis rate of the proposed scheme is over 96%.

Cooperative Adaptive Iterative Learning Fault-Tolerant Control Scheme for Multiple Subway Trains

In this article, a cooperative adaptive iterative learning fault-tolerant control (CAILFTC) algorithm with the radial basis function neural network (RBFNN) is proposed for multiple subway trains subject to the time-iteration-dependent actuator faults by using the multiple-point-mass dynamics model. First, an RBFNN is utilized to cope with the unknown nonlinearity of the subway train system. Next, a composite energy function (CEF) technique is applied to obtain the convergence property of the presented CAILFTC, which can guarantee that all train speed tracking errors are asymptotic convergence along the iteration axis; meanwhile, the headway distances of neighboring subway trains are kept in a safety range. Finally, the effectiveness of theoretical studies is verified through a subway train simulation.

Optimization of Urban Rail Automatic Train Operation System Based on RBF Neural Network Adaptive Terminal Sliding Mode Fault Tolerant Control

Aiming at the problem of the large tracking error of the desired curve for the automatic train operation (ATO) control strategy, an ATO control algorithm based on RBF neural network adaptive terminal sliding mode fault-tolerant control (ATSM-FTC-RBFNN) is proposed to realize the accurate tracking control of train operation curve. On the one hand, considering the state delay of trains in operation, a nonlinear dynamic model is established based on the mechanism of motion mechanics. Then, the terminal sliding mode control principle is used to design the ATO control algorithm, and the adaptive mechanism is introduced to enhance the adaptability of the system. On the other hand, RBFNN is used to adaptively approximate and compensate the additional resistance disturbance to the model so that ATO control with larger disturbance can be realized with smaller switching gain, and the tracking performance and anti-interference ability of the system can be enhanced. Finally, considering the actuator failure and the control input limitation, the fault-tolerant mechanism is introduced to further enhance the fault-tolerant performance of the system. The simulation results show that the control can compensate and process the nonlinear effects of control input saturation, delay, and actuator faults synchronously under the condition of uncertain parameters, external disturbances of the system model and can achieve a small error tracking the desired curve.

Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction

The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric based models may have poor performances due to unrealistic assumptions and changeable environments. In this paper, a long short-term memory network is carefully developed to build the train dynamic model in a nonparametric way. By optimizing the hyperparameters of the proposed model, more accurate outputs can be obtained with the same inputs of the parametric approaches. The proposed model was compared with two parametric methods using actual data. Experimental results suggest that the model performance is better than those of traditional models due to the strong learning ability. By exploring a detailed feature engineering process, the proposed long short-term memory network based algorithm was extended to predict train speed for multiple steps ahead.

Parallel Intelligent Systems for Integrated High-Speed Railway Operation Control and Dynamic Scheduling

The information exchange gap between current operation control and dynamic scheduling in high-speed railway systems (HRSs) still exists, and this gap has hindered the further integrative improvement of HRSs. This paper aims to explore a feasible solution to bridging the information exchange gap for further improving the efficiency of HRSs, with the parallel intelligent systems for integrated HRS operation control and dynamic scheduling first analyzed and constructed using the ACP approach, that is, "artificial systems" (A), "computational experiments," (C) and "parallel execution" (P). Then, on the basis of the constructed parallel intelligent systems, experiments on several typical scenarios in HRSs are conducted to achieve a set of control and management strategies for actual HRSs. Experimental results show that a number of powerful tools provided by the proposed parallel intelligent systems can be utilized not only to study the current HRSs, but also to further undertake research on integrated operation control and dynamic scheduling for HRSs.

Integral Sliding Mode Fault-Tolerant Control for Uncertain Linear Systems Over Networks With Signals Quantization

In this paper, a new robust fault-tolerant compensation control method for uncertain linear systems over networks is proposed, where only quantized signals are assumed to be available. This approach is based on the integral sliding mode (ISM) method where two kinds of integral sliding surfaces are constructed. One is the continuous-state-dependent surface with the aim of sliding mode stability analysis and the other is the quantization-state-dependent surface, which is used for ISM controller design. A scheme that combines the adaptive ISM controller and quantization parameter adjustment strategy is then proposed. Through utilizing H_∞ control analytical technique, once the system is in the sliding mode, the nature of performing disturbance attenuation and fault tolerance from the initial time can be found without requiring any fault information. Finally, the effectiveness of our proposed ISM control fault-tolerant schemes against quantization errors is demonstrated in the simulation.

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

Robust adaptive cruise control of high speed trains

The cruise control problem of high speed trains in the presence of unknown parameters and external disturbances is considered. In particular a Lyapunov-based robust adaptive controller is presented to achieve asymptotic tracking and disturbance rejection. The system under consideration is nonlinear, MIMO and non-minimum phase. To deal with the limitations arising from the unstable zero-dynamics we do an output redefinition such that the zero-dynamics with respect to new outputs becomes stable. Rigorous stability analyses are presented which establish the boundedness of all the internal states and simultaneously asymptotic stability of the tracking error dynamics. The results are presented for two common configurations of high speed trains, i.e. the DD and PPD designs, based on the multi-body model and are verified by several numerical simulations.