Efficient Tuning of PID Controllers using Swarm-based Optimization Algorithms

Most swarm-based optimization algorithms rely on tens of agents and thousands of iterations to achieve a satisfactory level of optimization accuracy, which means that the number of Fitness Evaluations (FEs) is large. Usually, the FE operation for the Proportional-Integral-Derivative (PID) controller tuning task requires us to integrate the error signal for a step response, which is time-consuming. Thus, using swarm-based algorithms to tune PID controllers requires a large amount of time and cannot be implemented in online tuning or real-time tuning required for machines working in dynamic environments. This paper utilizes our recently proposed improved version of the Particle Swarm optimization (PSO) algorithm enhanced by the PID architecture to tune PID controllers. To validate the efficiency of this new application, we compare its tuning performances with 7 other state-of-the-art swarm-based optimization algorithms on 4 different kinds of systems with the requirement that the time usage is less than 1 second. The experimental results show that the new algorithm achieves the best trade-off between accuracy and time consumption and could be employed as a potential candidate for online or real-time tuning of PID controllers in applications such as driverless cars or robot manipulators, where fast decision making is critical.