Tracking control of unmanned ground vehicles using state estimation and robotic formalism

Abstract

This paper proposes a novel method for the high speed tracking control of unmanned ground vehicles (UGVs) subject to load changes. The vehicle is modeled as a 16 degree-of-freedom multi-body system whose dynamics is derived using robotic formalism. The hierarchical control structure comprises two levels: a high-level (HLC) and a low-level (LLC) control system. The states are estimated with a two-stage Kalman Filter (KF) using GPS and inertial (IMU) sensory information. The LLC system ensures that the vehicle maintains the desired velocity and steering profile by employing PID-type active suspension, velocity and steering control that provide the generalized torques for the vehicle. The HLC utilizes a finite horizon linear quadratic (LQR) optimal controller to eliminate the position and orientation errors that cannot solely be compensated by the LLC system in case of load changes. The HLC control algorithm is based on the kinematic model of the vehicle and the reference trajectories are computed from the prescribed profiles. The optimal solution is given as a time-varying state-feedback control law. The efficiency of the tracking control system for high speed maneuvers is verified under noisy measurements and load changes by simulation.