Parameter Optimization of Reaching Law Based Sliding Mode Control by Computational Intelligence Techniques

Sliding Mode Control (SMC) is an efficacious control algorithm for non-linear control system. For a competent implementation of SMC, selection of the gain parameters of SMC is an important task to minimize the chattering, tracking error, disturbances and for the improvement of dynamic response of the system. In this paper, performance of two computational intelligent algorithms such as GA- Genetic Algorithm & PSO - Particle Swarm Optimization are assessed to compute an optimal gain values for reaching based law sliding mode control. The optimal values of the gain are computed for constant-rate, proportional-rate and power-rate reaching laws. The efficacies of the computed optimal gains are validated on spring, mass and damper system using specified reaching laws. The simulation results shows that the gain parameters computed using intelligent techniques for power-rate reaching law outperforms as compared to the other reaching laws (constant-rate and constant plus proportional rate) for SMC in the presence of matched disturbances.