Chaotic particle swarm optimization for optimal design of PID controllers in industrial systems

Proportional integral derivative (PID) controllers are widely used in industrial control processes since they are simple and easy to implement and act as an effective measure to manipulate the dynamic properties of industry systems. Carrying out the optimal design of the PID controllers is an indispensable constituent of the premises of highly precious control of these systems. In order to solve the problem of designing the parameters of the PID controllers more effectively, we bring forward a chaotic particle swarm optimization (CPSO) approach which we call CPIDSO. In this approach, we introduce the combination of chaotic logistic dynamics, hierarchical inertia weight, enhancement learning strategy, mutation mechanism and a proportional integral derivative (PID) controller. The chaotic logistic map is used in the substitution of the two random parameters affecting the convergence behavior. The hierarchical inertia weight coefficients are determined in accordance with the present fitness values of the local best positions so as to adaptively expand the particles' search space. The PID controller and enhancement learning strategy are simultaneously incorporated into standard PSO (SPSO) to efficiently enhance the particles' local and global search exploration and exploitation abilities. For performance validation of CPIDSO, CPIDSO, together with other algorithms like chaotic catfish PSO (CCPSO), genetic algorithm (GA) and PSO, is exploited to design the parameters of a PID controller in a Kalman filter based cybernetic system. The simulation results illustrate that CPIDSO exhibits better performance than other algorithms and yields the best result in the parameter optimization design of the system.