Output feedback adaptive controller of a autonomous skid-steering mobile vehicle based on sequential super-twisting differentiators

Abstract

The main purpose of this work is to develop an output state-dependent controller that solves the path-tracking deviation error for a skid-steering autonomous vehicle. The controller takes advantage of a nonlinear diffeomorphism that transforms skid-steering autonomous vehicle into a multi-input multi-output chain of integrators. This research assumes that available skid-steering autonomous vehicle variables are its position and its orientation. This in fact motivates the development of a modified super-twisting algorithm operating as a sequential step-by-step differentiator that estimates traslational velocity and acceleration of the studied autonomous vehicle in a finite time, which were used as part of the controller implementation. Based on the estimated states by the step-by-step multi-variable differentiator, an adaptive control design enforces the asymptotic convergence of the tracking trajectories for the skid-steering autonomous vehicle to the origin. The explicit form of the controller gains is derived using a class of control Lyapunov function including the deviation corresponding to the tracking error and a term that defines a matrix norm associated with control gains. Numerical results confirm the workability of the proposed controller considering the reduced norm of tracking error obtained with the proposed controller. Experimental evaluations compared the adaptive control introduced in this study and a state-feedback form justifying the control proposal. The adaptive form enforced smaller tracking errors using the estimated states forced by the step-by-step differentiator and the information obtained from a multi-camera video high-frequency acquisition system.

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“…The proposal was implemented on an SSARP following the proposal from Fuentes-Alvarez et al. (2022) and instrumented onto a 4WD Dagu Electronics chassis to be used as the SAR structural base.…”

“…These signals are generated from the control algorithm, which is hosted on Raspberry Pi 3 B+ running Ubuntu and ROS. Both electronic boards are interconnected to calculate the adaptive gains through the methods established by Fuentes-Alvarez et al. (2022) , which will then control the velocity of the SSARP wheels.…”

“…It is worth mentioning that the stability proof presented by Fuentes-Alvarez et al. (2022) ensures the trajectory tracking error convergence to a small region.…”