Comparison of Bioinspired Optimization Techniques for Improving the Performance of Dynamic Sliding Mode Controllers

Bio-inspired optimization algorithms have been shown in recent years to produce optimal solutions to various complex computational issues in science and engineering. This paper presents a comparative examination of bio-inspired algorithms to better understand and measure their efficacy in searching for the best tuning parameters for a Dynamical Sliding Mode Control for integrating systems with an inverse response and dead time. The comparative analysis includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms, and how they can improve the performance of the controller by searching for optimum tuning parameter solutions. The parameters of each algorithm have different impacts on the searching mechanism and were evaluated in two simulated systems. Ant colony optimization shows a significant capability over other algorithms to find optimal solutions for our problem.