Finite-Memory Front-Wheel Angle Estimation Strategy for Steer-by-Wire System

This article proposes a finite-memory-based front-wheel angle estimation (FMFWAE) strategy for robust and accurate performance in a steer-by-wire (SBW) system. The SBW system integrates the steering actuator and vehicle models, considering both the steering angle and vehicle motion. Our proposed FMFWAE, designed based on the SBW system, operates with the finite-memory property, using only a limited number of recent measurements and control inputs. This finite-memory property provides robustness to the proposed FMFWAE against linearization errors, model uncertainties, and fault signals. Furthermore, by developing a Frobenius norm minimization problem, we obtained the gain matrices of FMFWAE to minimize the impact of linearization errors and model uncertainties on the SBW system. The robust and accurate performance of FMFWAE was validated through real-time experiments.