Explicit MPC for column-type EPS systems

• Published in: IET electric power applications Vol. 14; no. 1; pp. 91 - 100
• Main Authors: Lee, Junho, Chang, Hyuk-Jun, Ahn, Hyun-Sik
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.01.2020
• Subjects: assist torque; automotive components; column‐type EPS system; current control law; effective control method; electric current control; electric motor; electric motors; electric power steering systems; EPS systems; explicit MPC controller; linear quadratic control; linear quadratic regulator; LQR controller; microcontrollers; model predictive control; multiparametric quadratic programming technique; optimisation; predictive control; quadratic programming; Research Article; steering systems; model predictive control; multiparametric quadratic programming technique; effective control method; explicit MPC controller; LQR controller; steering systems; quadratic programming; electric current control; electric motor; optimisation; EPS systems; electric power steering systems; linear quadratic regulator; column-type EPS system; electric motors; predictive control; current control law; assist torque; automotive components; linear quadratic control; microcontrollers
• ISSN: 1751-8660; 1751-8679; 1751-8679
• DOI: 10.1049/iet-epa.2018.5865

For decades, electric power steering (EPS) systems have replaced hydraulic-assist steering systems. The main purpose of EPS systems is to generate an assist torque using an electric motor for the improvement of the driving comfort. The proposed controller is designed against a model predictive control (MPC) and linear quadratic regulator (LQR) for a comparison in terms of generating a desired value of the assist torque. The motion equations of a column-type EPS system are derived in this study. Meanwhile, MPC has been regarded as an effective control method for constrained problems because this method can anticipate future events of the system. However, the repeated online optimisation imposes huge computational complexity. The proposed scheme explicitly obtains the current control law by adopting a multi-parametric quadratic programming technique. The proposed controller is evaluated in MATLAB/Simulink and a hardware-in-the-loop simulation using the Infineon automotive TriCore AURIX TC277 microcontroller. The simulation and experimental results confirmed that the proposed controller successfully tracks the reference value while reducing the computational burden. It is also shown by conducting experiments with various vehicle speeds that the tracking ability of the explicit model predictive controller (MPC controller) is superior to that of the LQR controller.