Solid oxide fuel cells (SOFCs) are promising electrochemical devices which translate chemical energy directly into electric energy with high efficiency and low pollution. However, the control of the output voltage of SOFCs is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. Nowadays, proportional-integral-derivative (PID) controllers are commonly utilized in industrial control systems for their high reliability and simplicity. However, it will lead to overshoot and windup issues when used in the wide-range operation of SOFCs. This paper aims to improve the PID controller performance based on fuzzy logic by (1) identifying a linear model based on the least squares method; (2) optimizing the PID parameters based on the generated linear model; and (3) designing a fuzzy adaptive PID controller based on the optimized parameters. The simulation results of the conventional PID controller and the fuzzy adaptive PID controller are compared, demonstrating that the proposed controller can achieve satisfactory control performance for SOFCs in terms of anti-windup, overshoot reduction, and tracking acceleration. The main contribution of this paper can be summarized as: (1) this paper identifies the SOFC model and uses the identified model as a control object to optimize conventional PID controllers; (2) this paper combines a fuzzy logic control scheme and PID control scheme to design our proposed fuzzy adaptive PID controller; and (3) this paper develops an anti-windup structure based on a back-calculation method to reduce saturation time and overshoot.

Nowadays, given the great deal of fossil fuel consumption and associated environmental pollution, solid oxide fuel cells (SOFCs) have shown their great merits in terms of high energy conversion efficiency and low emissions as a stationary power source. To ensure power quality and efficiency, both the output voltage and fuel utilization of an SOFC should be tightly controlled. However, these two control objectives usually conflict with each other, making the controller design of an SOFC quite challenging and sophisticated. To this end, a multi-objective genetic algorithm (MOGA) was employed to tune the proportional–integral–derivative (PID) controller parameters through the following steps: (1) Identifying the SOFC system through a least squares method; (2) designing the control based on a relative gain array (RGA) analysis; and (3) applying the MOGA to a simulation to search for a set of optimal solutions. By comparing the control performance of the Pareto solutions, satisfactory control parameters were determined. The simulation results demonstrated that the proposed method could reduce the impact of disturbances and regulate output voltage and fuel utilization simultaneously (with strong robustness).

The recent decades have witnessed refrigeration systems playing an important role in the life of human beings, with wide applications in various fields, including building comfort, food storage, food transportation and the medical special care units. However, if the temperature is not controlled well, it will lead to many harmful public health effects, such as the human being catching colds, food spoilage and harm to the recovering patients. Besides, refrigeration systems consume a significant portion of the whole society’s electricity usage, which consequently contributes a considerable amount of carbon emissions into the public environment. In order to protect human health and improve the energy efficiency, an optimal control strategy is designed in this paper with the following steps: (1) identifying the refrigeration system model based on a least squares method; (2) tuning an initial group of parameters of the proportional-integral-derivative (PID) controller via the pidTuner Toolbox of Matlab; (3) using an intelligent algorithm, namely fruit fly optimization (FOA), to further optimize the parameters of the PID controller. By comparing the optimal PID controller and the controller provided in the reference, the simulation results demonstrate that the proposed optimal PID controller can produce a more controllable temperature, with less tacking overshoot, less settling time, and more stable performance under a constant set-point.