Adaptive Neural Control of Constrained MIMO Nonlinear Systems With Asymmetric Input Saturation and Dead Zone.

In this article, the adaptive neural control is studied for multiple-input-multiple-output (MIMO) nonlinear systems with asymmetric input saturation, dead zone, and full state-function constraints. A suitable transformation is introduced to overcome the dead zone and saturation nonlinearity, and radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions. What is more, we apply the Nussbaum function and time-varying barrier Lyapunov function (BLF) to deal with the unknown control gains and full state-function constraints, respectively. Based on the backstepping method, a universal adaptive neural control scheme is presented such that not only the state-function constraints of the closed-loop system cannot be violated and all signals of the closed-loop systems are bounded, but also the tracking error converges to a small neighborhood containing the origin. The effectiveness of the proposed control scheme is verified by an application to the mass-spring-damper system and a numerical example.

Decentralized output feedback stabilization for switched stochastic high-order nonlinear systems with time-varying state/input delays.

This paper investigates decentralized output feedback stabilization problem for a class of switched stochastic high-order systems with time-varying state/input delays. With the help of coordinate transformations, a scaling gain is incorporated into the observers and controllers for the nominal system. Based on the homogeneous domination approach and stochastic Lyapunov-Krasovskii stability theorem, it is shown that global asymptotic stability in probability of the closed-loop system can be implemented by tuning the scaling gain. Two examples are given to demonstrate the feasibility of the proposed control method.

Adaptive output-feedback control for switched stochastic uncertain nonlinear systems with time-varying delay.

This paper addresses the problem of adaptive output-feedback control for a class of switched stochastic time-delay nonlinear systems with uncertain output function, where both the control coefficients and time-varying delay are unknown. The drift and diffusion terms are subject to unknown homogeneous growth condition. By virtue of adding a power integrator technique, an adaptive output-feedback controller is designed to render that the closed-loop system is bounded in probability, and the state of switched stochastic nonlinear system can be globally regulated to the origin almost surely. A numerical example is provided to demonstrate the validity of the proposed control method.