Robust Data-driven TS MPC-based Reference Governor for an Autonomous Racing Vehicle Considering Battery State of Charge

A reference governor approach based on model predictive control (MPC-RG) for an autonomous racing vehicle is developed in this work. This control strategy avoids constraint violations and includes online health management capabilities by solving a multi-objective optimization problem. In this case, a trade-off between the maximization of the state of charge of the battery and the longitudinal velocity, even the minimization of the control actions variation is carried out. In turn, the invariant zonotopic sets analysis ensures the convergence of states to a stable region. On the other hand, the proposed control scheme also combines a robust states feedback linear quadratic regulator (LQR) with a Kalman filter (KF) estimator to compensate for model uncertainty and exogenous disturbances, as well as, to estimate the unmeasured lateral velocity. Moreover, to represent the non-linear behaviour of the vehicle, a data-driven neuro-fuzzy Takagi-Sugeno (TS) model is employed. The developed approach is tested and evaluated in realistic environments by means of a simulated 1/10 Scale RC car.