Optimal Intelligent Chassis Layout Design Framework Based on Particle Swarm Optimization and Robust Finite-Frequency $H_{\infty }$ Control

Unmanned special vehicles (USVs) that adopt the automatic control method to achieve acceleration, deceleration and steering put forward higher requirements for lateral stability. The USVs chassis equipped with heavy work equipment and support equipment will change the center of gravity (c.g.) and significantly affect lateral stability. In this work, we aim to come up with an optimal chassis layout design framework to arrange the various equipment for the intelligent chassis from the control point of view. An algorithm combining the Particle Swarm Optimization (PSO) and enumeration method is developed to derive the candidate USVs chassis layout schemes. Candidate schemes are then filtered based on steering characteristics and Lyapunov stability. Finally, the selection method based on robust finite-frequency
$H_{\infty }$
control is proposed to obtain the final scheme with good lateral stability. Simulation results validate that the yaw rate response of the final USVs chassis layout scheme is close to the two-degree-of-freedom (2-DOF) chassis dynamic model compared with other schemes under the front wheel steering angular step signal input and sinusoidal signal input.