Robust PI protective tracking control of decentralized‐power trains with model uncertainties against over‐speed and signal passed at danger

Abstract Controlling the movements of trains to desired target speed and distance without breaking through safety regions is of primary importance in practical applications for safety reasons. In the classical train control and protection framework, automatic train operation regulates the speed and distance with respect to tracking desired ones under the supervision of automatic train protection, an independently operating subsystem, to prevent the phenomenon of over‐speed and signal passed at danger. This motivates to develop an integrated control scheme combining functions of control and protection, achieving protective tracking control against over‐speed and signal passed at danger doubtlessly. Meanwhile, computationally inexpensive control structure is desired for practical applications due to limited computing resource provided by on‐board computer on trains. In this paper, a robust control with PI structure and protective tracking is proposed for decentralized‐power trains regardless of model uncertainties. Specifically, the circumvent problem of over‐speed and signal passed at danger is formulated as prescribed performance control. It is proved rigorously that the proposed approach results in stable closed‐loop system, and finally, comparative simulation results are given to demonstrate the effectiveness and advantages

Error-Driven Nonlinear Feedback Design for Fuzzy Adaptive Dynamic Surface Control of Nonlinear Systems With Prescribed Tracking Performance

This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones