An Optimal PID Controller for a Bidirectional Inductive Power Transfer System Using Multiobjective Genetic Algorithm

TL;DR: Simulated and experimental results of a 1-kW prototype bidirectional IPT system are presented to demonstrate the effectiveness of the GA-tuned controller as well as to show that gain selection through multiobjective GA using the weighted objective function yields the best performance of the PID controller.

Abstract: Bidirectional inductive power transfer (IPT) systems are suitable for applications that require wireless and two-way power transfer. However, these systems are high-order resonant networks in nature and, hence, design and implementation of an optimum proportional-integral-derivative (PID) controller using various conventional methods is an onerous exercise. Further, the design of a PID controller, meeting various and demanding specifications, is a multiobjective problem and direct optimization of the PID gains often lead to a nonconvex problem. To overcome the difficulties associated with the traditional PID tuning methods, this paper, therefore, proposes a derivative-free optimization technique, based on genetic algorithm (GA), to determine the optimal parameters of PID controllers used in bidirectional IPT systems. The GA determines the optimal gains at a reasonable computational cost and often does not get trapped in a local optimum. The performance of the GA-tuned controller is investigated using several objective functions and under various operating conditions in comparison to other traditional tuning methods. It was observed that the performance of the GA-based PID controller is dependent on the nature of the objective function and therefore an objective function, which is a weighted combination of rise time, settling time, and peak overshoot, is used in determining the parameters of the PID controller using multiobjective GA. Simulated and experimental results of a 1-kW prototype bidirectional IPT system are presented to demonstrate the effectiveness of the GA-tuned controller as well as to show that gain selection through multiobjective GA using the weighted objective function yields the best performance of the PID controller.