An intelligent control system with a multi-objective self-exploration process

This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System—mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.