Abstract
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.
