Design of PSO-tuned FOPI & Smith predictor controller for nonlinear polymer electrolyte membrane fuel cell

ABSTRACT

The polymer electrolyte membrane (PEM) fuel cell or proton exchange membrane fuel cell (PEMFC) has sparked a lot of interest in renewable electricity generation due to high efficiency, quick start up time, and low operating temperature. However, they exhibit highly nonlinear behavior with degraded power quality under uncertain input conditions. The hydrogen, oxygen, and air feed determine the PEMFC system’s performance. Controlling the manifold pressure at the cathode side is essential in preventing the problem of oxygen starvation. In this paper, a nonlinear dynamic model of the PEMFC is considered to control the supply manifold pressure. The performance of the nonlinear PEMFC with various classical controllers (PI, PD, PID, and PID with filter derivative), Smithpredictor, and Fractional Order PI (FOPI) controllers are compared. The optimization of classical controllers is performed by PID tuner. On the other hand, Smith predictor and FOPI controller are tuned by artificial intelligence technique, namely, Particle Swarm Optimization (PSO). The FOPI controller is also tuned with Genetic Algorithm (GA). The PEMFC output is compared in terms of performance evaluation parameters, i.e. settling time, overshoot, and steady state error. The computer simulations suggest that in comparison with GA-FOPI [settling time (t_s) = 1.374 s, overshoot (M_p) = 18.452%, steady state error (e_ss) = 0.1%] and PSO-Smith predictor (t_s = 1.262 s, M_p = 5.581%, e_ss = 0%), the PSO-FOPI controller results in much better settling time (t_s = 1.008 s), overshoot (M_p = 2.577%), and steady state error (e_ss = 0%). Thus, PSO-optimized FOPI controller with nonlinear PEMFC outperforms the classical, PSO-Smith predictor, and GA-FOPI control schemes.

KEYWORDS:

• Proton electrolyte membrane (PEM) fuel cell (FC)
• proportional-integral-derivative (PID) controller
• PID-controller with filter derivative (PIDF)
• fractional order (FO) proportional integral (PI) controller
• particle swarm optimization (PSO)
• settling time (t_s)
• overshoot (M_p)
• steady state error (e_ss)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Swati Singh

Swati Singh is research scholar at Amity School of Engineering & Technology, Noida. Her interests includes controller design and renewable energy systems.

Vijay Kumar Tayal

Vijay Kumar Tayal received his M.Tech and Ph.D. from NIT, Kurukshetra. He is currently an Associate Professor at Amity School of Engineering and Technology (ASET), Noida, in the Department of Electrical and Electronics Engineering. His research interests are Robust, Fuzzy Controller Design and intelligent controller design.

Hemender Pal Singh

Swati Singh is research scholar at Amity School of Engineering & Technology, Noida. Her interests includes controller design and renewable energy systems.

Hemender Pal Singh received his Ph.D. degree from Rohilkhand University Bareilly, UP. Presently he is Professor and Head, Department of Electrical & Electronics Engineering at Amity School of Engineering & Technology (ASET), Noida. His research interests are Optical Communication, and Instrumentation Systems.

Vinod Kumar Yadav

Vinod Kumar Yadav received his Ph.D. from Indian Institute of Technology, Roorkee, Uttarakhand. He is currently Professor in Department of Electrical Engineering at Delhi Technological University, New Delhi.