Jinkun Liu - Intelligent Control Design and MATLAB Simulation

Tsinghua University Press, Beijing and Springer Nature Singapore Pte Ltd. 2018

Jinkun Liu 1

(1)

The term intelligent control may be loosely used to denote a control technique that can be carried out using the intelligence of a human who is knowledgeable in the particular domain of control. In this definition, constraints pertaining to limitations of sensory and actuation capabilities and information processing speeds of humans are not considered. It follows that if a human in the control loop can properly control a plant, then that system would be a good candidate for intelligent control. Information abstraction and knowledge-based decision making that incorporates abstracted information are considered important in intelligent control. Unlike conventional control, intelligent control techniques possess capabilities of effectively dealing with incomplete information concerning the plant and its environment, and unexpected or unfamiliar conditions. The term adaptive control is used to denote a class of control techniques where the parameters of the controller are changed (adapted) during control, utilizing observations on the plant (i.e., with sensory feedback), to compensate for parameter changes, other disturbances, and unknown factors of the plant. Combining these two terms, one may view intelligent adaptive control as those techniques that rely on intelligent control for proper operation of a plant, particularly in the presence of parameter changes and unknown disturbances.

There are several artificial intelligent techniques that can be used as a basis for the development of intelligent systems, namely expert control, fuzzy logic, neural network, and intelligent search algorithms.

In this class, we will study some fundamental techniques and some application examples of expert control, fuzzy logic, neural networks, and intelligent search algorithms. The main focus here will be their use in intelligent control.

The artificial intelligent techniques should be integrated with modern control theory to develop intelligent control systems.

In this class, we study intelligent control in four parts: expert control, fuzzy logic and control, neural network and control, and genetic algorithm.