Data-Driven Observer Design for Nonlinear Vehicle Dynamics using Deep Radial Basis Function Networks with Delay Compensation
Résumé
This study presents a novel approach for esti-mating lateral velocity, an important parameter for vehicle stability characterization. Aiming to resolve the problems of poor estimation accuracy caused by the insufficient modeling of traditional model-based methods and issues with sampled and delayed measurements, a sampled delay data neural network method for lateral velocity estimation is designed. Our approach incorporates a compensating injector to fill information gaps between samples, an extended compensation dynamic to reduce delays' impact, and a radial basis function neural network to mimic vehicle motions. Continuous weight updates ensure adaptability, and stability is demonstrated using the Lyapunov methodology. Experimental results confirm the effectiveness of our approach, providing promising insights to enhance lateral velocity estimation and improve control and stability in autonomous vehicle systems.