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In this paper, a neuroadaptive distributed output feedback formation tracking control scheme for multiple marine surface vessels with model uncertainties, unknown environmental disturbances and input and output constraints is proposed. A neural network based observer is developed to reconstruct the unmeasured velocity and approach the model uncertainties. To handle the input constraint, an auxiliary dynamic system is introduced. The tracking error transformation and the barrier Lyapunov function are used to tackle with the output constraint. Subsequently, by using the estimated velocity of neighboring vessels, neuroadaptive distributed output feedback controllers are developed. Furthermore, to avoid directly taking the time derivation of virtual control law and generate smooth reference signals, linear tracking differentiators are employed. Finally, it is shown that all the signals in the closed-loop system are bounded via Lyapunov analysis. Simulations are carried out to verify the proposed control scheme. INDEX TERMS Barrier Lyapunov function, distributed control, input constraint, marine surface vessels, output constraint, output feedback control.

An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

In order to provide a comfortable living and working environment indoors in tropical countries, the outdoor air often needs to be cooled and dehumidified before it enters the rooms. Membrane separation is an emerging technology for air dehumidification and it is based on the solution diffusion mechanism. Water molecules are preferentially permeating through the membranes due to its smaller kinetic diameter and higher condensability than the other gases. Compared to other dehumidification technologies such as direct cooling or desiccation, there is no phase transition involved in membrane dehumidification, neither the contact between the fresh air stream and the desiccants. Hence, membrane dehumidification would not only require less energy consumption but also avoid cross-contamination problems. A pilot scale air dehumidification system is built in this study which comprises nine pieces of one-inch PAN/PDMS hollow fiber membrane modules. A 150 h long-term test shows that the membrane modules has good water vapor transport properties by using a low vacuum force of only 0.78 bar absolute pressure at the lumen side. The water vapor concentration of the feed humid air decreases dramatically from a range of 18–22 g/m3 to a range of 13.5–18.3 g/m3. Most importantly, the total energy saving is up to 26.2% compared with the conventional air conditioning process.