Computationally Inexpensive Decentralized Adaptive Asymptotic Tracking Control for a Single Under-Actuated High-Speed Train

This paper investigates decentralized tracking control problem for a single high-speed train (HST) with unknown aerodynamic resistance, motor faults and disturbances. First, a multiple point-mass model of the HST system, which reflects in-train coupling force between adjacent cars, is derived. It is assumed that the first car of the HST is a motor car, i.e. the car is capable of traction and braking. Next, under the conditions that the bias faults and the disturbances are norm-bounded, a computationally inexpensive decentralized adaptive control method with backstepping technique is developed. Utilizing the information garnered from the adaptive mechanism, the detrimental effects stemming from the aerodynamic resistance and the motor faults can be comprehensively eradicated in the motor cars. Furthermore, a linear matrix inequality technique is introduced to address the in-train coupling forces for ensuring the tracking stability of the trailer cars. It is shown that the resulting closed-loop system is not only stable, but also that the position tracking error and velocity tracking error of the motor cars can asymptotically converge to zero. Compared with the existing results, a computationally inexpensive decentralized adaptive asymptotic tracking control approach is proposed, which requires only one parameter to be updated online adaptively per motor car. Finally, simulation on a HST with 2 motor cars and 6 trailer cars is provided for verifying the theoretical results.