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This paper develops an improved model predictive controller based on the online obtaining of softness factor and fusion velocity for automatic train operation to enhance the tracking control performance. Specifically, the softness factor of the improved model predictive control algorithm is not a constant, conversely, an improved online adaptive adjusting method for softness factor based on fuzzy satisfaction of system output value and velocity distance trajectory characteristic is adopted, and an improved whale optimization algorithm has been proposed to solve the adjustable parameters; meanwhile, the system output value for automatic train operation is not sampled by a normal speed sensor, on the contrary, an improved online velocity sampled method for the system output value based on a fusion velocity model and an intelligent digital torque sensor is applied. In addition, the two improved strategies proposed take the real-time storage and calculation capacities of the core chip of the controller into account. Therefore, the proposed improved strategies (I) have good performance in tracking precision, (II) are simple and easily conducted, and (III) can ensure the accomplishing of computational tasks in real-time. Finally, to verify the effectiveness of the improved model predictive controller, the Matlab/simulink simulation and hardware-in-the-loop simulation (HILS) are adopted for automatic train operation tracking control, and the tracking control simulation results indicate that the improved model predictive controller has better tracking control effectiveness compared with the existing traditional improved model predictive controller.

Model Predictive Controller Based on Online Obtaining of Softness Factor and Fusion Velocity for Automatic Train Operation