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Unmanned express vehicles play an increasingly important role in the logistics industry. In order to save time, reduce transportation costs in the process of logistics and distribution, and improve the utilization of resources, it is necessary to optimize the path of unmanned express vehicles to achieve the shortest logistics path in multiple destinations. Using the hybrid particle swarm optimization algorithm based on particle swarm optimization and genetic algorithm to optimize the path and overcome the long iteration of genetic algorithm could obtain a better optimal solution with fewer iterations, and the optimal solution is more stable. This can reduce the path mileage of logistics and transportation, improve work efficiency and reduce transportation time and cost.

This paper proposes a current sharing method for interleaved LLC resonant converter with secondary partial energy phase shift regulation. An AC voltage source with phase shift regulation is constructed and in series with main transformer at secondary side to balance the output currents. With secondary regulation ability, the current imbalance caused by the resonant tank tolerance can be compensated. The major part of power is delivered to load by main transformer, and small portion power is converted by the auxiliary parts, therefore, the energy loss of the conversion is almost as high as conventional LLC resonant converter. The steady state operating principle of the proposed converter is analyzed, and a prototype with two phases current sharing ability is built-up based on the theoretical analysis.

In the greenhouse control, there is a coupling relationship among the parameters under the multi-parameter control, but a traditional control strategy is based on a single parameter. This paper puts forward a multi-parameter fusion method about the integrated multi-parameter control of the growth environment, which is a two-level data fusion adopting an integrated control of temperature, humidity and illumination. In the one-level fusion, an adaptive weighted method is utilized; in the two-level fusion, a D-S evidence theory algorithm is employed. The general control method and experimental research using the data fusion technology have carried out in this paper. Experimental results show that the multi-parameter fusion method can benefit the systems combination control and the optimization of system equipment. In this case, the plants can be provided with an optimal environment and the environmental parameters can be controlled more stably and smoothly.

Artificial Climate Chamber (ACC) is widely used in agricultural research as a simulation of the natural environment of the laboratory equipment. The real-time control of the ACC system is very difficult, due to its serious coupling, hysteresis and nonlinearity. In the paper, an intelligent real-time control strategy based on a novel Improved Credit Assigned Cerebellar Model Arculation Controller Neural network (ICA-CMAC) inverse model is proposed. The novel ICA-CMAC algorithm can not only accelerate the network's convergence rate, but also reduce interference in learning. Additionally, the inverse model is designed to accurately replicate the inverse system behavior, easy to be applied and has clear physical meaning. Simulation results prove that such a new type of intelligent control strategy is able to effectively achieve fast, stable, real-time tracking control of ACC.

The high negative voltage power supply for the ECRH(Electron Cyclotron Resonance Heating) is a key component of the cyclotron. Aiming at the nonlinear characteristics, and the high requirements on static and dynamic performance of the system, Single Neuron Adaptive PID (SNAPID)control method with self-learning is proposed. The results show that the system have good dynamic and static performance with strong robustness and adaptive capacity. This control method is not only a new control strategy for high-performance negative high-voltage power supply, but also lays the foundation for the intelligent digital control of this system.