Adaptive fuzzy output-feedback control of uncertain SISO nonlinear systems

Abstract

The output-feedback control problem of a class of uncertain SISO nonlinear systems is investigated based on an indirect adaptive fuzzy approach. Because the system states are not required to be available for measurement, an observer is designed to estimate the system states. Compared with the existing results in the observer design, the main advantages of the proposed adaptive fuzzy output-feedback control approach are as follows: (1) It does not require to assume that the sign of the control gain coefficient is known and Nussbaum-gain technique is utilized to control the nonlinear systems with both the unknown control direction and the unmeasured states; (2) The observer in this paper is designed for the states rather than the tracking errors, then it is convenient to compute; (3) The controller singularity problem is perfectly avoided. The stability of the closed-loop system is analyzed by using Lyapunov method. A simulation example is given to verify the feasibility of the proposed approach.

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

“…There are, of course, unavoidable model uncertainties for any practical system, which lead the degradation of controller performance [3]. In these cases, the conventional control approaches are not applicable, and usually the universal functions approximators (UFAs)-based adaptive approaches are suggested to address this issue [4][5][6][7][8]. Therefore, the analytical study of adaptive control of uncertain nonlinear systems using UFA has received much attention during last decade.…”

Adaptive Neural Control with Prespecified Tracking Accuracy for a Class of Switched Systems Subject to Input Delay

“…Thus, an important problem is to overcome the foregoing uncertainties. In recent years, some approaches have been proposed to overcome the restriction and a large amount of encouraging results have been reported [11][12][13][14][15][16][17][18][19][20][21][22][23][24]. When the considered systems are unknown, neural networks (NNs)/fuzzy logic systems are often used to approximate the uncertain function to construct a controller.…”

Observer‐based adaptive neural control of uncertain MIMO nonlinear systems with unknown control direction

“…On the other hand, it is difficult to exploit mathematical models of many systems in the real world. In this case, the conventional control methods may not be used, and it is desirable to use the adaptive approach based on universal function approximator (UFA) [2][3][4][5][6][7][8][9][10][11].…”

“…If the system states are unavailable, these results are not applicable in practice, and the UAF-based adaptive control using estimated states is then required [3,5,7,8,10,11]. In [9], the adaptive fuzzy output feedback control for a class of uncertain SISO nonlinear with unknown control gain has been presented. In this paper, observer-based adaptive NN control of a class of uncertain MIMO nonlinear systems with unknown control direction is developed.…”