Parameter extraction of photovoltaic (PV) models plays a vital role in simulation, evaluation and control of PV systems. It requires to identify the parameters of different PV models quickly and accurately. In this paper, an improved differential evolution by reusing the past individual vectors and adaptive mutation strategy is proposed to extract PV parameters. In the proposed method, the successful difference vectors from previous generations are introduced to produce the offspring in the next generations to improve the performance of differential evolution. In addition, to obtain a nice result, an adaptive mutation strategy is considered to establish a good balance of exploration and exploitation. The proposed method is applied to identify the parameters of different PV models, such as single diode, double diode, and PV models. Comparison results demonstrate that the proposed method obtains the competitive performance on accuracy, reliability and convergence when compared with other state-of-the-art methods.

A variety of reinforcement learning (RL) methods are developed to achieve the motion control for the robotic systems, which has been a hot issue. However, the performance of the conventional RL methods often encounters a bottleneck, because the robots have difficulty in choosing an appropriate action in the control task due to the exploration-exploitation dilemma. To address this problem and improve the learning performance, this work introduces an experience aggregative reinforcement learning method with a Multi-Attribute Decision-Making (MADM) to achieve the real-time obstacle avoidance of wheeled mobile robot (WMR). The proposed method employs an experience aggregation method to cluster experiential samples and it can achieve more effective experience storage. Moreover, to achieve the effective action selection using the prior experience, an action selection policy based on a Multi-Attribute Decision-Making is proposed. Inspired by the hierarchical decision-making, this work decomposes the original obstacle avoidance task into two sub-tasks using a divide-and-conquer approach. Each sub-task is trained individually by a double Q-learning using a simple reward function. Each sub-task learns an action policy, which enables the sub-task to selects an appropriate action to achieve a single goal. The standardized rewards of sub-tasks are calculated when fusing these sub-tasks to eliminate differences in rewards for sub-tasks. Then, the proposed method integrates the prior experience of three trained sub-tasks via an action policy based on a MADM to complete the source task. Simulation results show that the proposed method outperforms competitors.

Leaky Coaxial Cables (LCXs) are widely used in tunnels and underpasses for smoother electric field coverage in wireless communication systems. But there are very few studies on LCXs working in 2.4GHz, which are the frequency bands used in 802.11b/g. The paper shows characteristics of 2.4GHz LCX through simulations and field tests. The results show that the communication through 2.4GHz frequency band LCX can provide stronger radio signals and thus better throughput compared with free space in tunnels. What's more, the handover interruption time between APs can be reduced because the Ping-Pong handover is avoided through LCX.

Communication Based Train Control (CBTC) system is the most advanced train control system in the world nowadays. How to model and simulate the system to find the design defects in the research and development has became one of the key issues of CBTC research. This paper describes a method of developing the hierarchy system model and refined modular model of Vehicle-on-board Automatic Train Protection (V-ATP) subsystem of CBTC system using Colored Petri Net (CPN). The aim is to provide a simulation environment for the research and development of V-ATP subsystem.