A symbol-based intelligent control system with self-exploration process

Abstract

This paper presents a symbol-based intelligent control system (SyICS) with a self-exploration process. The SyICS is comprised of a symbolic controller, a percepter, and a self-adaptor, and is a rule-based control system with on-line parametric adaptation. The symbolic controller consists of a number of symbolic rules, such as IF–THEN rules, for controlling the plant. The percepter is a sensory mechanism to perceive the control efficiency. Once the sensory information is found to be improper, i.e., there is inefficient control, the self-adaptor will be activated; otherwise, the symbolic controller will keep on the controlling assignment. The self-adaptor is an adaptive mechanism to explore the new symbolic rules and update the knowledge base for on-line and real-time adaptation. The self-exploration process is applied for the self-adaptor, and the hybrid genetic algorithm with variable-length chromosome is presented to fulfill the self-exploration process. The advantages of the SyICS are: (1) the symbolic controller is intuitive and easy to implement, and (2) The mechanism of the on-line adaptation is adopted and performed by the efficient hybrid genetic algorithm. A robotic path planning application is used to demonstrate the SyICS approach by comparing it with other intelligent control methods. The simulation results show that the robotic paths of SyICS model are the most efficient for all cases based on the path's efficiency measure.