ADAPTIVE NEURAL NETWORK-ASSISTED LINEAR QUADRATIC REGULATOR CONTROL WITH FULL-ORDER EXTENDED STATE OBSERVER FOR ACTIVE SUSPENSION SYSTEMS
TL;DR
This paper proposes integrating a Linear Quadratic Regulator (LQR) control scheme with an adaptive Neural Network (NN) updating law to improve ride comfort performance and confirms the effectiveness of the proposed control strategy in suspension system regulation.
Abstract
This paper proposes integrating a Linear Quadratic Regulator (LQR) control scheme with an adaptive Neural Network (NN) updating law to improve ride comfort performance. The dynamic states of the suspension system are effectively estimated using a Full-Order Extended State Observer (FOESO) rather than through direct sensor measurements, thereby reducing implementation costs and minimizing the influence of sensor noise. Numerical simulations are conducted under two different road profile scenarios based on ISO standards, while accounting for lumped uncertainties in the system dynamic modeling. The results show that the Root Mean Square (RMS) body displacement is reduced to 4.854 mm in the first case with ISO C-class road disturbance, and the RMS body acceleration decreases to 0.220 m/s² in the second case with ISO D-class road disturbance; both are significantly lower than those achieved by conventional controllers. Furthermore, the dynamic states are estimated with high accuracy, confirming the effectiveness of the proposed control strategy in suspension system regulation.