Finding the fuzzy satisfying solutions to constrained optimal control systems and application to robot path planning
Knowledge-based control tries to integrate the knowledge of human operators or process engineers into the controller design. Fuzzy control, one of the most popular intelligent techniques, has been successfully applied to a large number of consumer products and industrial processes. Model predictive control (MPC) has been used in process control systems with constraints; however, the constrained optimization problem involved in control systems has generally been solved in practice in a piece-meal fashion. To solve the problem systemically, the multi-objective fuzzy-optimization (MOFO) is used in the constrained predictive control for online applications as a means of dealing with fuzzy goals and fuzzy constraints in control systems. The conventional MPC is integrated with the techniques from fuzzy multicriteria decision making, translating the goals and the constraints to predictive control in a transparent way. This paper investigates how to use the fuzzy goal programming in predictive control and how to use the fuzzy goals and fuzzy constraints in predictive control. The presented method allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. It is shown that the model predictive controller based on MOFO allows the designers a more flexible aggregation of the control objectives than the usual weighting sum of squared errors in MPC. The visual robot path planning validates the efficiency of the presented algorithm.
optimization fuzzy goals fuzzy constraints fuzzy constraint satisfaction