Optimizing PID Parameters for Non-linear System using Social Spider Optimization Algorithm

The control strategy of non-linear systems plays a crucial role in process industries. Nature-Inspired metaheuristic algorithms are among the most vicious and eloquent algorithms for solving real time optimization problems. A numerous tuning methodologies have been proposed for optimal tuning of PID controller. Although classical tuning techniques produces quadrature decay ratio response, they fail in optimization over non-minimum phase systems. Hence, a suitable technique has been proposed in this paper using Social Spider Optimization (SSO) algorithm. SSO travails on the collaborative strategy of the male and female spiders, thus eliminating the occurrence of local optimum and eluding the exploration-exploitation balance between the spiders. Social Spider Algorithm (SSA) based on foraging strategy is also implemented to analyze the behavior of the colonial spiders. Thus SSO and SSA is engineered for the optimal tuning of PID parameters for a non -linear process and thus the systems behavior has been interpreted with minimization of IAE as its objective function and a comparative study of the evaluated outcomes has been done with the GA and PSO results. In PSO, each particle strives for their own best position and finds a global optimal solution. Unlike other swarm algorithms, SSO stands for its eminence in comprehending its current position and strive through for exploring the Unknown position, thus arriving at a Global optimal solution.