Application of fuzzy sliding mode control to robotic manipulator using multi-objective genetic algorithm

In this paper a Fuzzy Sliding Mode (FSM) control strategy is proposed and also Genetic Algorithms are employed to find the sliding parameters and membership functions of fuzzy part. Furthermore, due to conflicting between objective functions, means that as one objective function improves, another one deteriorates; there is a set of optimal solutions, well-known as Pareto optimal solutions. Therefore, Multi-objective Genetic Algorithms (MOGA) are used for Pareto approach optimization of fuzzy sliding mode control. The important conflicting objectives that have been considered in this work are, integrate tracking errors (ITE) and control inputs (CI). Moreover, this approach returns the optimum answers in Pareto form that designer can, by making trade-offs, select desired answer. Finally, simulation results of the close-loop system of two-degree-of-freedom rigid robot manipulator with the proposed controller show the effectiveness of the method.