A Blending Based Multiple Model Reference Adaptive Approach to Lateral Vehicle Motion Control

This paper studies reference tracking control of uncertain lateral vehicle dynamics, using a blending based multiple-model reference adaptive control (MMRAC) approach to overcome the parametric uncertainties and time-variations, including those in the tire force capacities and cornering stiffness. The lateral vehicle dynamics model under consideration is multiple-input, multiple-output, linear, and parameter varying. The design will assume a time-invariant system, such that all uncertain parameter variations lie inside of a known, compact, and convex set. The proposed MMRAC law guarantees perfect tracking of the desired state values generated by a linear reference model representing ideal driving conditions, and the system parameter estimates asymptotically converge to the unknown true values. We present simulations to show the stability and effectiveness of the proposed MMRAC scheme, even in the presence of slow time variations, as well as a performance comparison with existing lateral vehicle motion controllers.