An Improved Chaos Particle Swarm Optimization Approach in FOPID Controller for Microbial Fuel Cells

Abstract

Microbial fuel cells (MFCs) play a vital role in water quality monitoring, where stable power generation is essential for ensuring the accuracy of water-quality detection. However, the complex reactions occurring in MFCs make it challenging to maintain a stable output voltage under uncontrolled conditions. Thus, a fractional-order PID (FOPID) controller is proposed. The parameters of this controller are typically determined using the particle swarm optimization (PSO) algorithm. To address the limitations of traditional PSO algorithms, such as low precision and slow convergence, an improved PSO algorithm integrating chaotic mechanisms, reverse learning, golden sine algorithm, and elite Gaussian mutation is proposed. Simulation results demonstrate that faster and more accurate convergence of the improved PSO. The proposed FOPID controller achieves a setting time of 8.2965 s, outperforming others with times of 88.8889 s, 39.0680 s, and so on. The FOPID controller offers advanced technical support for the application of MFCs in water-quality monitoring.