Direct Adaptive Output Feedback Control Research Articles

Direct Adaptive Fuzzy Output Feedback Control for a Class of SISO Nonlinear Systems with Unknown Control Direction

AbstractIn this paper, a direct adaptive output feedback control scheme using fuzzy systems is presented for a class of uncertain SISO nonlinear systems with unknown control direction. Within this scheme, a high-gain observer is employed to estimate the unavailable system states, a fuzzy system is employed to generate directly the control input signal with only output feedback, and the Nussbaum-type function is used to deal with the unknown control direction. The stability of the closed-loop system is performed using a Lyapunov approach. Simulation results are provided to verify the effectiveness of the proposed design.

Direct adaptive output-feedback fuzzy controller for a nonaffine nonlinear system

A direct adaptive output-feedback controller for highly nonlinear systems is proposed by considering uncertain or ill-defined nonaffine nonlinear systems and employing a static fuzzy logic system (FLS) with flexible structure, i.e. online variation of the number of fuzzy rules. A linear error observer whose dynamic order is the same as that of the controlled system is employed and the FLS approximates and adaptively cancels an unknown plant nonlinearity using the observed state variables. A control law and adaptive laws for unknown fuzzy parameters and bounding constant are established so that the whole closed-loop system is stable in the sense of Lyapunov. The state observation error and tracking error are guaranteed to be uniformly ultimately bounded. No a priori knowledge of an upper bound on a lumped uncertainty is required.

Adaptive output feedback control of nonlinear systems using neural networks

A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. In particular, we consider single-input/single-output nonlinear systems, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded. Numerical simulations of an output feedback controlled van der Pol oscillator, coupled with a linear oscillator, is used to illustrate the practical potential of the theoretical results.