Bio-Inspired Controller Tuning/ Parameter Estimation of an Uncertain MIMO System

Bio-inspired algorithms are powerful intelligent optimization tools capable of handling complex real-world problems by providing optimal solutions (values) of system parameters in various complex engineering and scientific problems such as the complex and highly nonlinear Twin Rotor MIMO System (TRMS) plant. Practically due to the near impossibility of trial and error method in obtaining the optimal gains or parameter values of the controller, it becomes inevitable/essential to employ metaheuristics optimization techniques to intelligently determine these values by allowing the optimization iterative simulation converge to the local or global solutions with ease. In designing a control system for the TRMS in this research, Jumping Spider Optimization Algorithm (JSOA), a new and very recent population based bio-inspired metaheuristics approach is used. It was well able to tune the parameters of Proportional Integral and Derivative (PID) controllers by solving a nonlinear optimization problem using multi-objective functions, subject to several intrinsic constraints or restrictions. Unlike other deterministic algorithms that get caught in a local minimum, the JSOA evolved an optimal/global solution after searching the entire solution search space in a vectorized fashion. The global solution corresponds to the best possible values of the controller parameters within the search space. Compared with several other well-established and widely used controllers and optimization techniques, the statistical results obtained through simulations presented showed that JSOA was able to find the gains of the PID controllers outperforming the other optimization methods well.