This thesis presents a systematic approach to fuzzy-logic modeling and control of complex systems. In the proposed methodology, the fuzzy model of the system and control rules are obtained from input-output data with no need of a priori information.

The proposed fuzzy modeling methodology has three significant features:​ (i) a unified parameterized reasoning formulation;​ (ii) an improved fuzzy clustering algorithm, and (iii) an efficient strategy of selecting significant system inputs and their membership functions.

The proposed fuzzy control structure consists of a fuzzy model of the system and robust fuzzy rules in order to ensure stability and satisfactory system performance. We develop a generalized formulation of sliding mode control for a class of nonlinear multi-input multi-output systems. This formulation has two distinguish features:​ (i) it is applicable to "black box" systems with no need to identify internal parameters or to assume specific properties;​ (ii) it is possible to design the robust control command for each system state independently while the stability and robustness of the entire system is guaranteed. We apply the generalized formulation to analysis of the stability and robustness of the proposed fuzzy-logic control system. We also derive guidelines for designing the robust fuzzy control rules.

We apply the methodology to modeling and trajectory control of a four degree-of-freedom robot manipulator. Results of the proposed fuzzy-logic methodology are compared with those of a complete analytical simulation and a heuristic fuzzy modeling technique. A superior modeling performance in terms of accuracy and simplicity is obtained. The control performance is also compared with high-gain servo controllers for different trajectories, and a higher performance is achieved.