On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics

Increasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence model uncertainties significantly reduces a vehicle when the is traversing an unknown terrain or inertial parameters vary due to mission schedule external disturbances. A comprehensive mathematical skid steering tracked presented in this paper and used design control law. Analysis controller under vehicle-terrain interaction revealed undesirable behavior, such as divergence offset from desired trajectory. compound identification scheme utilizing exponential forgetting recursive least square, generalized Newton–Raphson (NR), Unscented Kalman Filter methods proposed estimate parameters, mass inertia, well interaction, slip, resistance coefficients, cohesion, shear deformation modulus on-line. The facilitates adaptive capability system, improves tracking contributes path trajectory planning framework, which essential future autonomous missions.