Hybrid improved particle swarm optimization-cuckoo search optimized fuzzy PID controller for micro gas turbine

In order to improve the control performance of the micro gas turbine in the full range of operating conditions, this paper proposes a new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm (HIPSO_CS) to tune the fuzzy PID controller parameters. Firstly, the traditional particle swarm optimization algorithm is improved by linearly decreasing the number of particles and the value of the inertia weight. Secondly, the cuckoo algorithm's local random walk strategy is introduced into the particle swarm optimization algorithm to enhance particles' diversity. Through comparing with traditional optimization algorithms, the proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed. To improve the dynamic response performance of the micro gas turbine, controllers with different structures are designed, and a comparative study of HIPSO_CS optimized Fuzzy PID/PID/PI is presented. The simulation results show that the micro gas turbine controlled by the fuzzy PID controller has a rapid response to fuel flow, minor speed overshoot, and shorter stabilization time during load increase or decrease. In addition, the designed control method can also achieve significant control effects under load disturbance, model parameter changes, and extreme operating condition. (C) 2021 The Authors. Published by Elsevier Ltd.

Automated summary

A new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm is proposed to tune the fuzzy PID controller parameters for improving the control performance of micro gas turbine in the full range of operating conditions. The proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed compared with traditional optimization algorithms, and it shows significant control effects in load disturbance, model parameter changes, and extreme operating conditions.