Genetic Algorithm of Sliding Mode Control Design for Manipulator Robot

The dynamical model of manipulator robot is represented by equations systems which are nonlinear and strongly coupled. Furthermore, the inertial parameters of manipulator depend on the payload which is often unknown and variable. The sliding mode controller (SMC) provides an effective and robust means of controlling nonlinear plants. The performance of SMC depends on control parameter selection of gain switching (k) and sliding surface constant (s). It is very difficult to obtain the optimal control parameters. In this paper, a control parameter selection algorithm is proposed by genetic algorithm to select the gain switching (k) and sliding surface constant parameter (s) so that the controlled system can achieve a good overall performance in the sliding mode controller design. Testing is done by giving a reference position for joint 1 and joint 2 of the robot manipulator of 45O (degree) with the controller performance indicator is settling time <2 seconds, and the tracking error tolerance is 1%. Simulation results demonstrate better performance of the PML with a genetic algorithm with a small response time by 1.03 seconds to 1.05 seconds joint 1 and 2 as well as for tracking error of the output state by 0.0015 degree for joint 1 and 0.0004 degree for joint 2.