The objective of the proposed research is to incorporate neural networks, fuzzy systems and genetic algorithms into the design of sliding mode controllers, sliding mode state estimators and sliding mode identifiers of uncertain or nonlinear dynamical systems. A paradigm for fuzzy modeling will be provided that allows for systematic construction of fuzzy models for the purpose of the controllers and state estimators' design. The controllers and state estimators' stability and their guaranteed performance will be analyzed and then tested on a simulation model of a ground vehicle. Optimization of the controllers and estimators' parameters will be achieved using genetic algorithms. In practice, the controller as well as the plant are subject to various nonlinear constraints like hard bounds on gains, limited energy, or finite switching speeds that must be taken into account in a realistic controller design. In addition, due to lack of knowledge of parameter values or inaccuracies in the modeling process, the designer must cope with uncertainties in the plant model. In this project, a deterministic approach to the control, identification and state estimation of uncertain dynamical systems is taken. Adaptation algorithms for continuous-time sliding mode neural identifiers will be studied and novel variable structure sliding mode fuzzy controllers and state estimators will be developed. Then, the proposed structures will be integrated into self-organizing fuzzy-neural sliding mode tracking controllers. Neural network and fuzzy logic controllers have been used with considerable success in closed-loop applications. However, these applications, though very successful, have no proofs of guaranteed stability for uncertain systems with control variables limited in amplitude. In the proposed research, the direct method of Lyapunov, Hahn's extensions of the Lyapunov method and LaSalle's Invariance Principle will be used in the stability and guaranteed performance analyses of fuzzy-neural sliding mode control and identification structures. The proposed controllers, estimators and identifiers will be tested on the recently developed ground vehicle model that includes lateral weight transfer and tires' models. This model is suitable for the evaluation of the vehicle dynamical behavior in real-time. The results of the proposed research will contribute to the basic control theory as well as to the intelligent vehicle control systems.
