Efficient Model Predictive Speed Control of Electric Vehicle with Actuator Saturation Constraint

Abstract

In this paper, we study an efficient model predictive speed control method for electric vehicles (EVs) with actuator saturation constraints. The model of the electric vehicle is described as a Takagi Sugeno (T-S) fuzzy system. The model predictive controller is derived in accordance with the strategy of "offline design, online synthesis". Firstly, offline design a fixed state feedback control law that can robustly stabilize the system, and introduce an additional control degree of freedom to solve an invariant set of the augmented system such that the projection of the invariant set in the original state space the largest one; Secondly, online optimizing the additional control quantity to obtain the optimal control law that can enlarge the initial feasible region and reduce the burden of calculation. Moreover, considering that the controller is prone to oversaturation in the actual control process, the actuator saturation constraint is introduced to regulate the control action of the controller. The process of obtaining the feedback gain allows for off-line, low control input computation, limited control input amplitude, and low energy consumption, which allows the proposed controller to be implemented practically. Finally, the experimental results of the proposed method are compared with those of the conventional fuzzy model predictive controller (FMPC) and the input-output constrained robust disturbance rejection stable fuzzy controller (IOCRDRSFC) in the MATLAB simulation environment to validate its effectiveness and superiority.