This paper investigates the problems of distributed cooperative cruise control and train running comfort for high-speed trains with input constraints under moving block mode. Firstly, inspired by the mode of hitting a hard wall for moving block, a distributed cooperative cruise control algorithm based on the position and speed state information of adjacent trains is proposed. Under this control algorithm, all trains can track the given speed and realize speed cruise. In this algorithm, the relationship between speed and displacement is used to improve the position term, so as to achieve the minimum safe distance between trains by adjusting the speed. Secondly, aiming at the problem of the excessive acceleration of each train in the moving block mode, the hyperbolic tangent function is introduced to limit the acceleration of each train in an appropriate range by limiting the control input. Finally, the stability of the closed-loop system in error state is proved by using Lyapunov stability theorem, and the feasibility of the algorithm is verified by simulation.

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With the aim of improving the energy utilization during the cooperative operation of multiple trains, this paper proposes an optimal distributed cooperative cruise control strategy to ensure safe and efficient tracking. A performance index function with distributed characteristics is constructed by considering the state errors among trains and energy consumption. An LQR-based optimal design technique is applied to cooperative cruise control to optimize the cooperative control gain to find the optimal solution. Additionally, the scalar coupling gains are introduced to decouple the design of the optimal cooperative control gain from the communication topology of trains. Thus, the proposed strategy is robust for arbitrary directed communication topologies and can eventually be used to achieve the distributed tracking optimization of multiple trains. The asymptotic stability of the system is proved strictly by exploiting the Hurwitz and Lyapunov stability theorem. A numerical simulation example is given to verify the feasibility and effectiveness of the proposed strategy.