Design of Fuzzy Adaptive Output-feedback Controllers and Chain Observers for a Class of Siso Nonlinear Delayed Systems

In this thesis, a fuzzy adaptive output feedback controllers and chain observers for a class of nonlinear delayed systems is proposed. The proposed approach do not need the availability of the state variables. The linearization approach is used to transform a nonlinear system into a linear system through Lie derivative method. We adopt an fuzzy adaptive chain observer in which no strictly positive real (SPR) condition is needed. Further, we combine a self-structuring scheme with an on-line estimation of the fuzzy parameters to reduce the dynamic order of the adaptive output-feedback fuzzy control system. The proposed observer consists of a chain of observation algorithm to reconstruct the system states at different delayed time instants (chain observer) for state prediction. Based on the Lyapunov synthesis approach, a global uniform ultimate boundedness property (UUB) of the state estimation errors and tracking errors in the closed-loop system can be achieved. According to some conditions, global exponential convergence to a bounded value for the observation error is ensured for any given delay in the measurements. Finally, an inverted pendulum system is used to illustrate the effectiveness of the proposed method.