The air feeding system is one of the most important systems in the proton exchange membrane fuel cell (PEMFC) stack, which has a great impact on the stack performance. The main control objective is to design an optimal controller for the air feeding system to regulate oxygen excess at the required level to prevent oxygen starvation and obtain the maximum net power output from the PEMFC stack at different disturbance conditions. This paper proposes a fractional order fuzzy PID controller as an efficient controller for the PEMFC air feed system. The proposed controller was then employed to achieve maximum power point tracking for the PEMFC stack. The proposed controller was optimized using the neural network algorithm (NNA), which is a new metaheuristic optimization algorithm inspired by the structure and operations of the artificial neural networks (ANNs). This paper is the first application of the fractional order fuzzy PID controller to the PEMFC air feed system. The NNA algorithm was also applied for the first time for the optimization of the controllers tested in this paper. Simulation results showed the effectiveness of the proposed controller by improving the transient response providing a better set point tracking and disturbance rejection with better time domain performance indices. Sensitivity analyses were carried-out to test the robustness of the proposed controller under different uncertainty conditions. Simulation results showed that the proposed controller had good robustness against parameter uncertainty in the system.

The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.

The unmanned surface vessel (USV) trajectory with spatial and temporal information plays an important role in its positioning and navigation. Unlike traditional trajectory reconstruction methods, this paper proposes a novel method based on the automatic identification system (AIS) for USV. Aside from the AIS data applied for restoring the USV’s trajectory, the proposed method considers the constraints of the vessel’s navigation state, maneuvering factors and time stamps. This method consists of three steps: AIS data restoration, Empirical Mode Decomposition (EMD) denoising and Fermat’s spiral fitting. Firstly, AIS data restoration is applied to eliminate abnormal data. Next, the EMD noise reduction algorithm is used to reduce the jitters and interference of Speed over Ground (SOG) and Course over Ground (COG) components, and the denoised latitude and longitude positions are calculated using the kinematics model of the vessel. Finally, a curve fitting process based on Fermat’s spiral is employed to reconstruct a smoother trajectory. Several experiments illustrate that the low-cost AIS equipment is compatible with the navigation system of the USV. The reference trajectory is determined by Global Position System (GPS) and Inertial Measurement Unit (IMU) modules in USV. Compared to conventional methods, the residual errors of the proposed method is smaller. The results show that the novel trajectory reconstruction method is effective and can be applied to USV’s positioning and navigation.