Adaptive Multiple-Model Control of A Class of Nonlinear Systems

Adaptive 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...

ADAPTIVE FUZZY TRACKING CONTROL FOR A CLASS OF NONLINEAR SYSTEMS WITH UNKNOWN DISTRIBUTED TIME-VARYING DELAYS AND UNKNOWN CONTROL DIRECTIONS

In this paper, an adaptive fuzzy control scheme is proposed for a class of perturbed strict-feedback nonlinear systems with unknown discrete and distributed time-varying delays, and the proposed design method does not require a priori knowledge of the signs of the control gains.Based on the backstepping technique, the adaptive fuzzy controller is constructed. The main contributions of the paper...

Robust Model Predictive Control for a Class of Discrete Nonlinear systems

This paper presents a robust model predictive control scheme for a class of discrete-time nonlinear systems subject to state and input constraints. Each subsystem is composed of a nominal LTI part and an additive uncertain non-linear time-varying function which satisfies a quadratic constraint. Using the dual-mode MPC stability theory, a sufficient condition is constructed for synthesizing the ...

On-line Multiple-model Based Adaptive Control Reconfiguration for a Class of Nonlinear Control Systems

A New Adaptive Extended Kalman Filter for a Class of Nonlinear Systems

This paper proposes a new adaptive extended Kalman filter (AEKF) for a class of nonlinear systems perturbed by noise which is not necessarily additive. The proposed filter is adaptive against the uncertainty in the process and measurement noise covariances. This is accomplished by deriving two recursive updating rules for the noise covariances, these rules are easy to implement and reduce the n...