Zhengqiang Zhang

Extended dissipativity-based synchronization of uncertain chaotic neural networks with actuator failures

Abstract This paper addresses the problem of extended dissipativity-based synchronization for chaotic neural networks with polytopic-type uncertainties and actuator failures. By using a parameter-dependent Lyapunov function and a novel extended dissipation inequality, a criterion is proposed to guarantee that the synchronization error system is extended ( X , Y , Z ) -dissipative for all admissible uncertainties and actuator failures. Based on this criterion, a desired fault-tolerant controller is designed, which takes the possible actuator failures into account. A numerical example is presented to show the effectiveness of our proposed design method.

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.

Exponential synchronization of Genesio–Tesi chaotic systems with partially known uncertainties and completely unknown dead-zone nonlinearity

Abstract This paper is concerned with the problem of synchronization controller design for Genesio–Tesi chaotic systems with plant uncertainties and dead-zone input. The upper bounds of plant uncertainties are partially known, while dead-zone nonlinearity is completely unknown. The prior knowledge on the parameters in the uncertainties and dead-zone is not required to be known in advance. A novel control law with adaptive methodology is proposed to compensate for input nonlinearity. An adaptive law with exponent function is designed to estimate the unknown lumped parameters. It is shown that complete synchronization between two identical Genesio–Tesi chaotic systems is achieved and the synchronization errors converge to zero exponentially. Numerical studies are provided to illustrate the effectiveness of the presented scheme.

Globally adaptive asymptotic tracking control of nonlinear systems using nonlinearly parameterized fuzzy approximator

Abstract In this paper, the problem of globally stable adaptive fuzzy tracking control is addressed for a class of uncertain nonlinear systems in the canonical form. Instead of linearly parameterized fuzzy logic system (FLS), nonlinearly parameterized FLS is used to approximate the unknown nonlinear function. By combining the new parametrization of Gaussian membership function and the signal replacement approach, a novel adaptive fuzzy controller is designed. It is shown that all signals in the closed-loop system are globally bounded and that the tracking error converges to zero asymptotically. A simulation example is included to illustrate the effectiveness of the proposed approach.

Zero-error tracking control of uncertain nonlinear systems in the presence of actuator hysteresis

In this paper, the problem of adaptive tracking control is addressed for a class of nonlinear systems with unknown constant parameters and unknown actuator nonlinearity. The actuator nonlinearity is modelled as the backlash-like hysteresis, which is described by a differential model. The prior knowledge on the control gain sign is not required, and only the assumption on the reference signal is made. By combining the adaptive backstepping technique with the Nussbaum gain approach, an adaptive compensation controller design approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking error can converge to zero asymptotically despite the presence of the actuator hysteresis. Two simulation examples are included to illustrate the effectiveness of the proposed approach.

Adaptive control for a class of nonlinear time-delay systems with dead-zone input

Abstract In this paper, we consider the same class of systems as in a previous paper, i.e. a class of uncertain nonlinear systems with state time-varying delays and nonsymmetric dead-zone input. The dead-zone parameters are unknown, and the delay-related nonlinearity is bounded by the partially known nonlinear functions. No knowledge is assumed on the slope sign of the dead-zone nonlinearity and the upper bounds of the time delays and their derivatives. By employing the Nussbaum gain approach and choosing a proper Lyapunov–Krasovskii functional, an adaptive compensation control algorithm is developed. The explicit expression of the control gain function is constructed. The boundedness of all the closed-loop signals is established, and the desired tracking performance is guaranteed by choosing the appropriate design parameters. The effectiveness of the proposed control algorithm is demonstrated by two simulation examples.

Adaptive Output Feedback Control of Nonlinear Time-Delay Systems With Application to Chemical Reactor Systems

This paper considers the problem of global adaptive stabilization for strict feedback nonlinear systems with time delay and parameter uncertainty. By designing a delay-independent reduced-order observer with two dynamic gains, we propose a universal adaptive control strategy to reduce the conservatism of the growth conditions imposed on nonlinearities. With the help of Lyapunov–Krasovskii functionals, stability analysis is subtly conducted. Finally, the proposed controller is applied to a chemical reactor system to illustrate the usefulness of our results.

Adaptive neural control of switched nonstrict-feedback nonlinear systems with multiple time-varying delays

Abstract This paper focuses on the problem of adaptive neural tracking control for a class of uncertain switched nonlinear time-varying delay systems in nonstrict-feedback form with arbitrary switchings. Radial basis function neural networks are used to model the unknown redefined continuous functions derived from Young’s inequalities. By combining bounding functions’ monotonously increasing property and variable separation technique, the uncertain system functions with nonstrict-feedback structure are dealt with such that iterative adaptive backstepping approach can be carried out. A newly developed Lyapunov–Krasovskii functional is utilized to compensate for the uncertainties of multiple time-varying delays, which makes the delay nonlinearities free from any assumptions. By introducing novel continuous functions, the problem of circular construction of controller is overcome deduced from employing one common tuning law. It is proved that the tracking error of adaptive neural control systems is semi-globally uniformly ultimately bounded with common Lyapunov function method. Finally, a simulation example is presented to show the effectiveness of the suggested control scheme.

Leader-following rendezvous for uncertain Euler–Lagrange multi-agent systems by output feedback

Abstract In this paper, we investigate the problem of output feedback tracking for a class of Euler–Lagrange multi-agent systems with unmeasurable velocity and input disturbances. By proposing a novel dynamic velocity observer, an adaptive output feedback consensus algorithm is proposed such that the tracking errors of all agents can converge to an arbitrarily small neighborhood of zero by tuning the design parameters. A numerical example is presented to illustrate the effectiveness of the controller.

Neural networks-based adaptive output feedback control for a class of uncertain nonlinear systems with input delay and disturbances

Abstract This paper focuses on the problem of adaptive output feedback control for a class of uncertain nonlinear systems with input delay and disturbances. Radial basis function neural networks (NNs) are employed to approximate the unknown functions and an NN observer is constructed to estimate the unmeasurable system states. Moreover, an auxiliary system is introduced to compensate for the effect of input delay. With the aid of the backstepping technique and Lyapunov stability theorem, an adaptive NN output feedback controller is designed which can guarantee the boundedness of all the signals in the closed-loop systems. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.