Dynamic Event-Triggered Adaptive Neural Practical Predefined-Time Control for Uncertain Nonlinear Systems With Unknown Control Directions

In this paper, an adaptive dynamic event-triggered output-feedback control scheme is proposed for a class of uncertain nonlinear systems. Specifically, a parameter estimator and a Nussbaum function are incorporated into the dynamic event-triggered control scheme to compensate for serious uncertainties, including the completely unknown parameter allowed in system nonlinearities and the unknown control coefficients (both signs and magnitudes are unknown). Importantly, an additional internal dynamic variable is introduced into the event-triggering mechanism, enabling the control scheme to save network resources more efficiently compared with the static ones in the related works. In addition, by skillfully designing the dynamics of this variable, the influence of the execution error can be fully counteracted. It is shown that, with the proposed control scheme, the system states can asymptotically converge to zero, instead of to a residual set, while excluding the Zeno phenomenon. To validate the effectiveness and superiority of the proposed scheme, two simulation examples are provided.

Event-triggered adaptive neural network controller for uncertain nonlinear system

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

In this article, the adaptive event-triggered fault-tolerant control (FTC) issue of uncertain nonlinear systems suffering from sensor and actuator faults as well as external disturbances is studied. A disturbance observer (DO) is constructed to compensate for external disturbances and approximation errors generated by neural networks (NNs). Then, switching event-triggered control and command filtering techniques are introduced to balance the communication frequency and tracking performance of the controlled systems while avoiding “explosion of complexity” issue caused by iterative derivations of virtual controllers. Furthermore, compensation signals are designed to eliminate filtered errors. Finally, it is proven that the developed FTC scheme can esure that all signals are bounded and free from Zeno phenomenon. Simulation results of a spring damping mechanical system verifies the merits of the designed control algorithm.

Dynamic event-triggered neuro-optimal control for uncertain nonlinear systems with unknown dead-zone constraint

This paper deals with an adaptive robust tracking control using a multilayer neural network (NN) for a class of nonlinear dynamic systems with unknown time varying state delays. Typical adaptive NN backstepping controllers for uncertain nonlinear systems with time-delay give rise to computation complexity caused by the the repeated derivatives of virtual controllers and nonlinear functions. Moreover, the combined techniques usually result in only uniformly ultimately bounded (UUB) stability caused by the inherent NN approximation error. This paper presents a control scheme that uses an integral sliding mode control as a feedback term and an adaptive neural controller as a feedforward term based on the desired compensation adaptive law (DCAL) technique. First, we develop a new DCAL formulation which avoids the explosion of complexity caused by the general NN backstepping scheme to compensate for nonlinear system uncertainties, bounded system disturbances, and unknown state time delays. Then, using a Lyapunov-Krasovskii (LK) functional, it is shown that the proposed controller renders the class of uncertain nonlinear time-delay systems asymptotically stable.

Event-triggered adaptive fixed-time fuzzy control for uncertain nonlinear systems with unknown actuator faults

This study reports adaptive quantized control for a class of uncertain strict‐feedback nonlinear systems with unknown control directions. Combining backstepping technique and Lyapunov stability theory, a systematic analysis method is designed. Based on the disintegration of the hysteresis quantizer, a Nussbaum‐based scheme can be developed to get over the obstacle of the quantized input signal and unknown control directions. Moreover, the number of adaptive laws is small, thereby reducing the computational burden. Then we testify the boundedness of all signals in the closed‐loop system. Besides, the tracking error can converge to an arbitrarily small domain of origin. Finally, a simulation example is provided to verify the feasibility of the control scheme.

Event-triggered-based adaptive asymptotic tracking control for stochastic nonlinear time-delay systems with actuator and sensor faults

Adaptive fuzzy dynamic event-triggered control of uncertain nonlinear systems with sensor faults

Decentralized neural identification and control for uncertain nonlinear systems: Application to planar robot

The problem of adaptive robust control for uncertain nonlinear systems with time-varying delay is considered in this paper. The upper bound of the uncertainty is assumed to be nonlinear functions with unknown coefficients. By using adaptive control method, a class of adaptive robust state feedback controller is proposed. Based on Lyapunov-Krasovskii method and Lyapunov stability theory, it is shown that the controller designed can render the uniform asymptotic stability of the system. Finally, a numerical example is given to show the controller is valid.

Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems

Event-triggered adaptive tracking control of uncertain nonlinear systems with prescribed performance and actuator failures

In this article, the problem of event‐triggered adaptive asymptotic tracking control (ATC) for stochastic nonlinear systems with unknown control directions (UCDs) and full state constraints is concerned. It must be said that the controller design and system analysis is more complex and difficult since the existence of stochastic disturbances, UCDs and full state constraints simultaneously. By introducing the lower bound of the UCDs into the barrier Lyapunov functions, an event‐triggered adaptive MTN ATC scheme is proposed based on the boundary estimation method and a new event‐triggered control (ETC) strategy, which can achieve satisfactory asymptotic tracking performance and control performance of the system, while reduce the occupation of network resources. The simulation results not only verify the effectiveness of the proposed control scheme, but also present different tracking performances between three ETC strategies for comparison, further confirming the superiority of the proposed ETC strategy in achieving asymptotic tracking performance.