Adaptive Neural Network Control for a Class of MIMO Uncertain Pure-Feedback Nonlinear Systems
نویسندگان
چکیده
In this paper, robust adaptive neural network control is investigated for a class of multi-input-multi-output (MIMO) pure-feedback nonlinear system with unknown nonlinearities. The unknown nonlinearities could be come from unmodeled dynamics, modeling errors, or nonlinear time-varying uncertainties. Based on the backstepping design technique and the universal approximation property of the neural network (NN), robust adaptive control is synthesized by employing a single NN to approximate the lumped uncertain nonlinearities. The proposed control can eliminate the circularity problem completely, and guarantees semiglobal uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop and convergence of the tracking error to an arbitrarily small residual set.
منابع مشابه
Adaptive Leader-Following and Leaderless Consensus of a Class of Nonlinear Systems Using Neural Networks
This paper deals with leader-following and leaderless consensus problems of high-order multi-input/multi-output (MIMO) multi-agent systems with unknown nonlinear dynamics in the presence of uncertain external disturbances. The agents may have different dynamics and communicate together under a directed graph. A distributed adaptive method is designed for both cases. The structures of the contro...
متن کاملAdaptive Neural Network Method for Consensus Tracking of High-Order Mimo Nonlinear Multi-Agent Systems
This paper is concerned with the consensus tracking problem of high order MIMO nonlinear multi-agent systems. The agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. The communication network topology of agents is assumed to be a fixed undirected graph. A distributed adaptive control method is proposed to solve the consensus problem utilizing re...
متن کاملAdaptive Consensus Control for a Class of Non-affine MIMO Strict-Feedback Multi-Agent Systems with Time Delay
In this paper, the design of a distributed adaptive controller for a class of unknown non-affine MIMO strict-feedback multi agent systems with time delay has been performed under a directed graph. The controller design is based on dynamic surface control method. In the design process, radial basis function neural networks (RBFNNs) were employed to approximate the unknown nonlinear functions. S...
متن کاملAdaptive Distributed Consensus Control for a Class of Heterogeneous and Uncertain Nonlinear Multi-Agent Systems
This paper has been devoted to the design of a distributed consensus control for a class of uncertain nonlinear multi-agent systems in the strict-feedback form. The communication between the agents has been described by a directed graph. Radial-basis function neural networks have been used for the approximation of the uncertain and heterogeneous dynamics of the followers as well as the effect o...
متن کاملAbstract—An adaptive fuzzy neural networks (FNN) output feedback control approach is proposed for a class of multi-input and multi-output (MIMO) pure-feedback nonlinear systems with unknown backlash-like hysteresis and immeasurable
An adaptive fuzzy neural networks (FNN) output feedback control approach is proposed for a class of multi-input and multi-output (MIMO) pure-feedback nonlinear systems with unknown backlash-like hysteresis and immeasurable states. The state observers are designed to estimate the unmeasured states, the filtered signals are introduced to circumvent algebraic loop problem encountered in the implem...
متن کامل