Abstract
We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics. The dynamics feature of the high speed train is modeled as a cascade of cars connected by flexible couplers, and the formulation is mathematically transformed into a Takagi-Sugeno (T-S) fuzzy model. The goal of this study is to design a state feedback control law at each decision step to enhance safety, comfort, and energy efficiency of high speed train subject to safety constraints on the control input. Based on Lyapunov stability theory, the problem of optimizing an upper bound on the cruise control cost function subject to input constraints is reduced to a convex optimization problem involving linear matrix inequalities (LMIs). Furthermore, we analyze the influences of second-order actuator dynamics on the fuzzy constrained predictive controller, which shows risk of potentially deteriorating the overall system. Employing backstepping method, an actuator compensator is proposed to accommodate for the influence of the actuator dynamics. The experimental results show that with the proposed approach high speed train can track the desired speed, the relative coupler displacement between the neighbouring cars is stable at the equilibrium state, and the influence of actuator dynamics is reduced, which demonstrate the validity and effectiveness of the proposed approaches.
Highlights
High speed railway systems have attracted much attention, since they can provide greater transport capacity, significantly faster speeds, and outstanding features of punctuality when compared to aircraft and autovehicle
We investigate the problem of fuzzy constrained predictive optimal control of high speed train considering the effect of actuator dynamics
Howlett [4] applied the Pontryagin maximum principle to prove that the optimal sequence of train driving strategy should consist of four phases including maximum acceleration, cruising, coasting, and maximum braking [4,5,6]
Summary
High speed railway systems have attracted much attention, since they can provide greater transport capacity, significantly faster speeds, and outstanding features of punctuality when compared to aircraft and autovehicle. In order to overcome these disadvantages, Gruber and Bayoumi [12] investigated the longitudinal dynamics of multilocomotive powered train and approximated it as a nonlinear mass-spring dashpot model which was interconnected via couplers at the first time They demonstrated that this method could efficiently minimize the coupler forces which result in safer operation and increased traveling speeds. As one of the most powerful directions of the modern control, model predictive control (MPC) can be regarded as a circular operation in which a minimization problem is solved to calculate optimal control for certain time horizon with hard physical constraints [27, 28] This feature makes MPC an ideal candidate for optimal cruise control of high speed train too. (2) x(k + j | k) is the predicted state at time k + j based on the current state x(k)
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