Explicit MPC for column‐type EPS systems. Issue 1 (28th November 2019)

• Record Type: Journal Article
• Authors: Lee, Junho; Chang, Hyuk‐Jun; Ahn, Hyun‐Sik
• Is Part Of: IET electric power applications. Volume 14:Issue 1(2020)
• Page Start: 91
• Page End: 100
• Publication Date: 2019-11-28
• Subjects: optimisation -- automotive components -- predictive control -- steering systems -- linear quadratic control -- electric motors -- quadratic programming -- electric current control -- microcontrollers; column‐type EPS system -- effective control method -- current control law -- multiparametric quadratic programming technique -- explicit MPC controller -- LQR controller -- electric power steering systems -- EPS systems -- assist torque -- electric motor -- model predictive control -- linear quadratic regulator; Electric power -- Periodicals; Electric power systems -- Periodicals; 621.305
• DOI: 10.1049/iet-epa.2018.5865
• Languages: English
• ISSNs: 1751-8660

Abstract

Abstract : 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.