Adaptive fuzzy nonlinear internal model control strategy



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Texas Tech University


Proportional-Integratal Derivative like Fuzzy Logic Controllers (PID-FLCs), have been used for a variety of nonlinear control problems. Basically, a PID-FLC contains a control algorithm in the form of linguistic fuzzy rules. The problem with PID-FLCs is that there is no systematic design for developing fuzzy rules. It is also difficult to develop the controllers to meet specific requirements on control performances.

In this dissertation, a nonlinear internal model control (NIMC) structure and an adaptive fuzzy NIMC strategy have been proposed to overcome the problems of PIDFLCs. One of the attractive features of the NIMC structure is that the relations between some designed parameters and the performance of the control system can be found explicitly. Thus, this control structure allows designers to systematically construct the fuzzy control. An adaptive fuzzy NIMC strategy has been proposed. The proposed strategy has two attractive features. First, the strategy provides an on-line adaptation to improve control performance and to keep the closed-loop system stable. Second, a fuzzy basis function (FBF) expansion is used to implement the controller. The use of the FBF expansion enhances the ability of the strategy to control practical nonUnear systems whose exact mathematical models are difficult to obtain.

Finally, Simulation studies of controlling four nonlinear systems (e.g., a pendulum, an inverted pendulum, a forced Van der Pol equation, and a two-link cylindrical robot manipulator) have been conducted. The simulation results show that the proposed strategy has successfully controlled the four nonlinear systems.



Computational intelligence, Adaptive control systems, Nonlinear control theory, Fuzzy logic -- Industrial applications