ABSTRACT
Intelligent control, the discipline where control algorithms are developed by emulating certain characteristics of intelligent biological systems, is an emerging area of control that is being fueled by the advancements in computing technology (Antsaklis and Passino, 1993; Passino, 2005). For instance, software development and validation tools for expert systems (computer programs that emulate the actions of a human who is proficient at some task) are being used to construct “expert controllers” that seek to automate the actions of a human operator who controls a system. Other knowledge-based systems such as fuzzy systems (rule-based systems that use fuzzy logic for knowledge representation and inference) and planning systems (that emulate human planning activities) are being used in a similar manner to automate the perceptual, cognitive (deductive and inductive), and action-taking characteristics of humans who perform control tasks. Artificial neural networks emulate biological neural networks and have been used to (1) learn how to control systems by observing the way that a human performs a control task, and (2) learn in an online fashion how to best control a system by taking control actions, rating the quality of the responses achieved when these actions are used, then adjusting the recipe used for generating control actions so that the response of the system improves. Genetic algorithms are being used to evolve controllers via off-line computer-aided-design of control systems or in an online fashion by maintaining a population of controllers and using survival of the fittest principles where “fittest” is defined by the quality of the response achieved by the controller.
