ABSTRACT
One of the biggest challenges of any control paradigm is being able to handle large complex systems under unforeseen situations. A system may be called com plex here if its dimension (order) is too high; its model (if available) is nonlinear, time-delayed, and interconnected; and information on the system is uncertain such that classical techniques cannot easily handle the problem. Soft computing, a col lection of fuzzy logic, neurocomputing, genetic algorithms, and genetic program ming has proven to be a powerful tool for adding autonomy to many complex systems. For such systems the rule base size of soft computing control architecture will be nearly infinite. Examples of complex systems are power networks, national air traffic control system, and an integrated manufacturing plant. In this chapter a rule base reduction approach is suggested to manage large inference engines. No tions of rule hierarchy and sensor data fusion are introduced and combined to achieve desirable goals. New paradigms using soft computing approaches are utilized to design autonomous controllers for a number of applications which are presented briefly.
