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It is a challenge to maintain a safe and efficient tracking for multiple high-speed trains under the moving block operational mode. In this paper, a novel cooperative tracking control based on a consensus algorithm and artificial potential field theory is proposed to realize the train tracking within a distance range. A tunable artificial potential function is first designed to dynamically adjust the distance between adjacent high-speed trains with real-time train states. By regulating the parameters of the artificial potential function, the safety distance can be adjusted according to the required tolerance deviation of the actual distance. Under the proposed strategy, each high-speed train operates with the desired speed and tracks the preceding one with an adjustable distance range. Numerical train operational cases are investigated to illustrate the effectiveness of the proposed methods., Author(s): Zhiwu Huang [1]; Pingping Wang [1]; Feng Zhou (corresponding author) [2]; Weirong Liu [3]; Jun Peng [3] 1. Introduction Based on an advanced wireless communication technology and positioning technology, [...]

The high-speed train operation process is highly nonlinear and has multiple constraints and objectives, which lead to a requirement for the automatic train operation (ATO) system. In this paper, a hybrid model predictive control (MPC) framework is proposed for the controller design of the ATO system. Firstly, a piecewise linear system with state and input constraints is constructed through piecewise linearization of the high-speed train's nonlinear dynamics. Secondly, the piecewise linear system is transformed into a mixed logical dynamical (MLD) system by introducing the auxiliary binary variables. For the transformed MLD system, a hybrid MPC controller is designed to realize the precise control under hard constraints. To reduce the online computation complexity, the explicit control law is computed offline by employing the mixed-integer linear programming (MILP) technique. Simulation results validate the effectiveness of the proposed method., 1. Introduction With rapid development of high-speed railway, the operation safety, punctuality, and energy consumption of train have received more and more attentions. Nowadays, in China, the high-speed railway generally [...]

Introducing orthogonal frequency division multiplexing (OFDM) into cognitive radio (CR) can potentially increase the spectrum efficiency, but it also leads to further challenges for the resource allocation of CR networks. In OFDM-based cognitive relay networks, two of the most significant research issues are subcarrier selection and power allocation. In this paper, a non-cooperative game model is proposed to maximize the system throughput by jointly optimizing subcarrier selection and power allocation. First, taking the direct and relay links into consideration, an equivalent channel gain is presented to simplify the cooperative relay model into a non-relay model. Then, a variational inequality method is utilized to prove the existence and uniqueness of the Nash equilibrium solution of the proposed non-cooperative game. Moreover, to compute the solution of the game, a suboptimal algorithm based on the Lagrange function and distributed iterative water-filling algorithm is proposed. The proposed algorithm can jointly optimize the process of subcarrier selection and power allocation. Finally, simulation results are shown to demonstrate the effectiveness of the proposed joint subcarrier selection and power allocation scheme. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

As an important part of intelligent transportation systems, path planning algorithms have been extensively studied in the literature. Most of existing studies are focused on the global optimization of paths to find the optimal path between Origin-Destination (OD) pairs. However, in urban road networks, the optimal path may not be always available when some unknown emergent events occur on the path. Thus a more practical method is to calculate several suboptimal paths instead of finding only one optimal path. In this paper, a cooperative Q-learning path planning algorithm is proposed to seek a suboptimal multipath set for OD pairs in urban road networks. The road model is abstracted to the form that Q-learning can be applied firstly. Then the gray prediction algorithm is combined into Q-learning to find the suboptimal paths with reliable constraints. Simulation results are provided to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Cognitive radio has emerged as a promising solution to address the problems posed by coming spectrum scarcity for the inherently resource-constrained sensor networks. Reliability and energy consumption are key objectives for spectrum sensing in cognitive sensor networks. In this paper, a fast differential evolution algorithm is proposed to optimize the energy consumption and spectrum sensing performance jointly. By constructing a comprehensive performance metric, the joint optimization is transferred to a multiobjective optimal problem, in which the sleeping schedule and censoring mechanism are taken into consideration. The main objective of the proposed algorithm is to minimize the network energy consumption subjected to constraints on the detection performance by optimally deriving the censoring and sleeping probabilities. To accelerate the convergence speed and maintain the diversity, the algorithm utilizes the advantages of opposite-based learning for generating the initial population and a tournament scheme in mutation step. In the crossover step, a control parameters dynamic adjustment scheme is applied to make a trade-off between exploration and exploitation. Finally, a selection mechanism is introduced for generating a well-distributed Pareto optimal front. The simulation results show that the proposed algorithm can reduce the average energy consumption of cognitive sensor node, while improving the global probability of spectrum sensing. [ABSTRACT FROM AUTHOR]

Cognitive spectrum management can improve the utilization efficiency of spectrum while increasing the energy consumption of sensor network nodes. Hence, how to balance the energy consumption and spectrum efficiency has become a critical challenge in the resource-constrained cognitive radio sensor networks. In this paper, by analyzing the channel characteristics and the energy efficiency of networks, a joint channel selection and power control spectrum decision algorithm based on distributed Q learning is proposed. To evaluate the performance of the proposed framework, an optimal Q value subject to communication efficiency index is formulated. Then, the learning strategy selection scheme is designed to solve the optimization problem by establishing a learning model. In this learning model, each node can get the strategy of other nodes to select the optimal strategy by introducing distributed strategy estimation. The simulation results show that the proposed algorithm has better performance than the existing methods. [ABSTRACT FROM AUTHOR]