This article investigates the adaptive fault-tolerant tracking control problem for a class of discrete-time multiagent systems via a reinforcement learning algorithm. The action neural networks (NNs) are used to approximate unknown and desired control input signals, and the critic NNs are employed to estimate the cost function in the design procedure. Furthermore, the direct adaptive optimal controllers are designed by combining the backstepping technique with the reinforcement learning algorithm. Comparing the existing reinforcement learning algorithm, the computational burden can be effectively reduced by using the method of less learning parameters. The adaptive auxiliary signals are established to compensate for the influence of the dead zones and actuator faults on the control performance. Based on the Lyapunov stability theory, it is proved that all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Finally, some simulation results are presented to illustrate the effectiveness of the proposed approach.

Adaptive Fault-Tolerant Tracking Control for Discrete-Time Multiagent Systems via Reinforcement Learning Algorithm

This paper investigates the adaptive event-triggered control problem for aclass of nonlinear systems subject to periodic disturbances. To reduce thecommunication burden, a reliable relative threshold strategy is proposed.Fourier series expansion and radial basis function neural network arecombined into a function approximator to model suitable time-varyingdisturbed function of known periods in strict-feedback systems. By combiningthe Lyapunov stability theory and the backstepping technique, the proposedadaptive control approach ensures that all the signals in the closed-loopsystem are bounded, and the tracking error can be regulated to a compact setaround zero in finite time. Finally, simulation resultsare presented to verify the effectiveness of the theoretical results.

Adaptive event-triggered control for a class of nonlinear systemswith periodic disturbances

An adaptive neural network (NN) event-triggered control scheme is proposed for nonlinear nonstrict-feedback multiagent systems (MASs) against input saturation, unknown disturbance, and sensor faults. Mean-value theorem and Nussbaum-type function are invoked to transform the structure of the input saturation and overcome the difficulty of unknown control directions, respectively. On the basis of the universal approximation property of NNs, a nonlinear disturbance observer is designed to estimate the unknown compounded disturbance composed of external disturbance and the residual term of input saturation. According to the measurement error defined by control signal, an event-triggered mechanism is developed to save network transmission resource and reduce the number of controller update. Then, an adaptive NN compensation control approach is proposed to tackle the problem of sensor faults via the dynamic surface control (DSC) technique. It is proved that all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Finally, simulation results demonstrate the effectiveness of the presented control strategy.

Event-Triggered Control for Multiagent Systems With Sensor Faults and Input Saturation

This article investigates an adaptive reinforcement learning (RL) optimal control design problem for a class of nonstrict-feedback discrete-time systems. Based on the neural network (NN) approximating ability and RL control design technique, an adaptive backstepping RL optimal controller and a minimal learning parameter (MLP) adaptive RL optimal controller are developed by establishing a novel strategic utility function and introducing external function terms. It is proved that the proposed adaptive RL optimal controllers can guarantee that all signals in the closed-loop systems are semiglobal uniformly ultimately bounded (SGUUB). The main feature is that the proposed schemes can solve the optimal control problem that the previous literature cannot deal with. Furthermore, the proposed MPL adaptive optimal control scheme can reduce the number of adaptive laws, and thus the computational complexity is decreased. Finally, the simulation results illustrate the validity of the proposed optimal control schemes.

NN Reinforcement Learning Adaptive Control for a Class of Nonstrict-Feedback Discrete-Time Systems

This article studies the bipartite containment control problem for nonlinear multiagent systems (MASs) with input quantization over a signed digraph. The design objective is to provide an appropriate distributed protocol such that the followers converge to a convex hull containing each leader's trajectory as well as its opposite trajectory different in sign. Based on a nonlinear decomposition approach of input quantization, an event-triggered control scheme is developed via backstepping technique. A fuzzy observer is constructed to estimate unmeasurable states. Moreover, the bipartite containment control scheme for nonlinear MASs is designed. It is demonstrated that all signals in the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior is excluded. Finally, a simulation example is given to verify the validity of the designed method.

Observer-Based Event-Triggered Fuzzy Adaptive Bipartite Containment Control of Multiagent Systems With Input Quantization

In this paper, an adaptive neural network (NN) control problem is investigated for discrete-time nonlinear systems with input saturation. Radial-basis-function (RBF) NNs, including critic NNs and action NNs, are employed to approximate the utility functions and system uncertainties, respectively. In the previous works, a gradient descent scheme is applied to update weight vectors, which may lead to local optimal problem. To circumvent this problem, a multigradient recursive (MGR) reinforcement learning scheme is proposed, which utilizes both the current gradient and the past gradients. As a consequence, the MGR scheme not only eliminates the local optimal problem but also guarantees faster convergence rate than the gradient descent scheme. Moreover, the constraint of actuator input saturation is considered. The closed-loop system stability is developed by using the Lyapunov stability theory, and it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed approach is further validated via some simulation results.

Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation

This paper focuses on the design of a reduced adaptive fuzzy tracking controller for a class of high-order stochastic nonstrict feedback nonlinear systems with full-state constraints. In the proposed approach, reduced fuzzy systems are used to approximate uncertain functions which involve all state variables and a high-order tan-type barrier Lyapunov function (BLF) is considered to deal with full-state constraints of the controlled system. With this BLF and a combination of the reduced fuzzy control and adding a power integrator, a novel control scheme is constructed to ensure that tracking error is within a very small range of the origin almost surely, meanwhile, the constraints on the system states are not breached almost surely during the operation. Two examples are proposed to show the effectiveness of the design scheme.

Weiwei Bai

Papers

Adaptive Reinforcement Learning Neural Network Control for Uncertain Nonlinear System With Input Saturation

Reduced Adaptive Fuzzy Tracking Control for High-Order Stochastic Nonstrict Feedback Nonlinear System With Full-State Constraints

Event‐triggered guaranteed cost fault‐tolerant optimal tracking control for uncertain nonlinear system via adaptive dynamic programming

Multigradient recursive reinforcement learning NN control for affine nonlinear systems with unmodeled dynamics

Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints