Abstract
This paper proposes a non-singular fast terminal sliding mode control strategy based on the self-organizing radial basis function neural network (RBFNN) approximation for the train key network system to realize the safe and reliable operation of the train. In order to improve the RBFNN approximation performance and speed, an improved multi-strategy particle swarm optimization (IMPSO) algorithm, which utilizes multi-strategy evolution ways with a nonlinear deceasing inertia weight to improve the global optimizing performance of particle swarm, is proposed to optimize the structure and parameters for better mapping the highly nonlinear characteristics of train traction braking. In addition, the IMPSO is also introduced into a non-singular fast terminal sliding mode (NFTSM) controller to obtain the most appropriate tuning parameters of the controller and suppresses the chattering phenomenon from sliding mode controller. The stability characteristic of the system under the proposed NFTSM controller is studied based on the Lyapunov theory. Further combined with effective delay prediction and delay compensation methods, the NFTSM high-precision control of the train key nonlinear network system is implemented. The simulation results show that the proposed method has more efficient and robust tracking performance and real-time performance compared with other control methods, which can provide effective means for realizing the symmetrical bus control by automatic train operation (ATO) at both ends of the train, with the safe operation of the train under every complex motion condition.
Highlights
Modern high-speed trains are using the train communication network (TCN) to realize train control and diagnosis
To guarantee the accuracy of the train running speed control, we will introduce the improved multi-strategy particle swarm optimization (IMPSO) algorithm and radial basis function neural network (RBFNN) into the non-singular fast terminal sliding mode (NFTSM) controller and utilize the global optimization characteristics of particle swarm to improve the approximation ability of RBFNN to the nonlinear disturbance in the process of train traction braking, and significantly reduce chattering caused by sliding mode control and the train running speed tracking error, which realizes the safe driving and parking precision
The real-time performance of the RBFNN is the best when the number of neurons is 13, and its average training time is far less than the task period (50 ms), as long as the sampling time is chosen larger than 50 ms, on-line real-time control could be realized
Summary
Modern high-speed trains are using the train communication network (TCN) to realize train control and diagnosis. The improved PSO algorithm was applied to train the RBF neural network for determining optimal network parameters, which effectively improved network generalization capability Among these control approaches, due to the intrinsic insensitivity of the sliding mode (SM). A NFTSM control method based on the improved multi-strategy particle swarm optimization (IMPSO) algorithm and the RBF neural network (RBFNN) is proposed for the key nonlinear network control system. The IMPSO algorithm is introduced into RBFNN to optimize the structure and parameters for better mapping the highly nonlinear characteristics of train traction braking, which ensures the safe and reliable operation of the train network control system, combined with effective delay prediction and delay compensation methods.
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