Guozeng Cui

Adaptive decentralized NN control of large-scale stochastic nonlinear time-delay systems with unknown dead-zone inputs

In this paper, the problem of adaptive decentralized neural network (NN) control for a class of large-scale stochastic nonlinear time-delay systems with unknown dead-zone inputs is investigated. Neural networks are utilized to approximate unknown nonlinear functions, and an adaptive decentralized controller is constructed by incorporating the minimal learning parameters algorithm into backstepping design procedure. It is proved that the proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability. Finally, a numerical example is provided to demonstrate the effectiveness of the present results.

Adaptive tracking control for uncertain switched stochastic nonlinear pure-feedback systems with unknown backlash-like hysteresis

Abstract In this paper, an adaptive tracking control problem is studied for a class of switched stochastic nonlinear pure-feedback systems with unknown backlash-like hysteresis under arbitrary switching. The mean-value theorem is used to overcome the difficulty arising from the pure-feedback structure. Based on neural networks’ approximation capability, an adaptive tracking control approach is developed via the adaptive backstepping technique and common Lyapunov function method. It is proved that the proposed control scheme can guarantee that all signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability and the tracking error converges to an adjustable neighborhood of the origin. Finally, a simulation example further shows the effectiveness of the presented control scheme.

State feedback control for a class of stochastic high-order feedforward nonlinear systems with time-varying delays: a homogeneous domination approach

In this paper, the problem of global state feedback stabilization for a class of stochastic high-order feedforward nonlinear systems with different power orders and multiple time delays is investigated. A distinct property of the system to be investigated is that the control coefficients are not restricted to 1. By adding one power integrator technique and homogeneous domination approach, a state feedback controller design is recursively proposed, which ensures the global asymptotical stability in probability of the closed-loop system. Finally, a simulation example is given to illustrate the effectiveness of our results obtained in this paper.

Neural-network-based adaptive tracking control for a class of pure-feedback stochastic nonlinear systems with backlash-like hysteresis

In this paper, we are concerned with the problem of adaptive neural network tracking control for a class of pure-feedback stochastic nonlinear systems with backlash-like hysteresis. Unlike some existing control schemes, an affine variable at each step is constructed without using the mean value theorem, and neural networks are used to approximate the unknown and desired control input signals. By introducing the additional first-order low-pass filter for the actual control input signal, the algebraic loop problem arising in pure-feedback stochastic nonlinear systems with backlash-like hysteresis is addressed. It is shown that the proposed controller guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability while the tracking error converges to a small neighborhood of the origin in the sense of four-moment. Finally, a simulation example is given to verify the effectiveness of the proposed scheme.

Prescribed performance distributed consensus control for nonlinear multi-agent systems with unknown dead-zone input

ABSTRACT In this paper, the problem of prescribed performance distributed output consensus for higher-order non-affine nonlinear multi-agent systems with unknown dead-zone input is investigated. Fuzzy logical systems are utilised to identify the unknown nonlinearities. By introducing prescribed performance, the transient and steady performance of synchronisation errors are guaranteed. Based on Lyapunov stability theory and the dynamic surface control technique, a new distributed consensus algorithm for non-affine nonlinear multi-agent systems is proposed, which ensures cooperatively uniformly ultimately boundedness of all signals in the closed-loop systems and enables the output of each follower to synchronise with the leader within predefined bounded error. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed control scheme.

Command filtered backstepping tracking control of uncertain nonlinear strict-feedback systems under a directed graph

This paper studies the distributed consensus tracking control problem of multiple uncertain non-linear strict-feedback systems under a directed graph. The command filtered backstepping approach is utilised to alleviate computation burdens and construct distributed controllers, which involves compensated signals eliminating filtered error effects in the design procedure. Neural networks are employed to estimate uncertain non-linear items. Using a Lyapunov stability theorem, it is proved that all signals in the closed-looped systems are semi-globally uniformly ultimately bounded. In addition, consensus errors converge to a small neighbourhood of the origin by adjusting the appropriate design parameters. Finally, simulation results are presented to demonstrate the effectiveness of the developed control design approach.

Neural-network-based distributed adaptive synchronization for nonlinear multi-agent systems in pure-feedback form

In this paper, the problem of distributed adaptive synchronization for unknown nonlinear multi-agent systems in pure-feedback form is studied under a directed graph. Neural networks are used to approximate the unknown nonlinear dynamics, and by incorporating the dynamic surface control (DSC) technique into backstepping design procedure, distributed adaptive consensus controllers are developed. A novel method is given for reducing the burden of networked communication. It is shown that the proposed distributed consensus controllers guarantee that all signals in the closed-loop system are cooperatively semi-globally uniformly ultimately bounded, and the consensus errors converge to a small neighborhood of the origin. Finally, a simulation example is given to show the effectiveness of the designed control scheme.

Output-Feedback Control for a Class of Stochastic High-Order Feedforward Nonlinear Systems with Delay

The problem of global output-feedback stabilization for a class of stochastic high-order time-delay feedforward nonlinear systems with different power orders is investigated. By combining the adding one power integrator technique with the homogeneous domination approach, an output-feedback controller design is proposed, which ensures the global asymptotical stability in probability of the closed-loop system.

Command-filter-based distributed containment control of nonlinear multi-agent systems with actuator failures

ABSTRACT In this paper, the problem of distributed containment fault-tolerant control for a class of nonlinear multi-agent systems in strict-feedback form is studied. The considered nonlinear multi-agent systems are subject to unknown nonlinear functions and actuator faults with loss of effectiveness and lock-in-place. By resorting to the universal approximation capability of fuzzy logical systems, the command filtered backstepping technique and nonlinear fault-tolerant control theory, distributed controllers are designed recursively. From the Lyapunov stability theory, it is proved that all signals of the resulting closed-loop systems are cooperatively semi-globally uniformly ultimately bounded and the containment errors converge to a small neighbourhood of origin by properly tuning the design parameters. Finally, a numerical example is provided to show the effectiveness of the proposed control method.