Robust TS-ANFIS MPC of an Autonomous Racing Electrical Vehicle Considering the Battery State of Charge
IEEE/ASME Transactions on Mechatronics(2023)
Abstract
In this work, the trajectory tracking problem of an autonomous racing electrical vehicle is addressed. Accordingly, a two-layer control scheme is developed, such that stability, recursive feasibility, and constraint satisfaction are guaranteed. The outer layer includes a zonotopic tube-based predictive control to ensure trajectory tracking while minimizing energy consumption considering the state of charge of the vehicle's battery. Meanwhile, the inner layer combines a linear quadratic zonotopic controller with a zonotopic Kalman filter to reduce the effect of exogenous disturbances and modeling errors. Moreover, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference system (ANFIS) is employed. To illustrate the performance of the proposed control scheme, a simulated 1/10 Scale RC car is used.
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Key words
Adaptive neuro-fuzzy inference system (ANFIS),data-driven Takagi–Sugeno (TS) fuzzy model,racing electrical vehicle,trajectory tracking,tube-based model predictive control (MPC),zonotopes
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