A Hybrid Genetic Algorithm and Bacterial Foraging Approach for Global Optimization and Robust Tuning of PID Controller with Disturbance Rejection

The social foraging behavior of Escherichia coli (E. Coli) bacteria has been used to solve optimization problems. This chapter proposes a hybrid approach involving genetic algorithm (GA) and bacterial foraging (BF) algorithm for function optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria. The proposed algorithm is then used to tune a PID controller of an Automatic Voltage Regulator (AVR). To design disturbance rejection tuning, disturbance rejection conditions based on H∞ are illustrated and the performance of response is computed for the designed PID controller as the integral of time weighted squared error. Simulation results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems.