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This paper investigates an output feedback sliding mode control scheme for a two-wheeled self-balancing robot under terrain inclination and disturbances. First of all, an adaptive high-gain observer is designed for the robot to estimate, simultaneously, the unmeasured states and the unknown terrain inclination angle which appears nonlinearly in the dynamics of the wheeled robot, using the only measured linear and angular positions. Then, the estimated states and the reconstructed unknown inclination angle are used by an appropriate continuously implemented sliding mode controller whose the design is based on the boundary layer approximation approach to reduce the chattering phenomenon. The objective of the proposed robust controller is to ensure the tracking control of the two-wheeled robot despite the unknown terrain inclination and the presence of friction disturbances. The stability of the adaptive observer-based output feedback system is established through a Lyapunov analysis, and it is inspired from sliding modes theory. Numerical simulations results highlight the effectiveness of the proposed tracking control scheme applied on two-wheeled self-balancing robot subject to terrain inclination even in the presence of unavailable disturbances.

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In this paper, an output feedback control approach based on an adaptive observer is developed for the two-wheeled self-balancing robot subject to unknown parameters (with nonlinear parameterization). Firstly, a high gain control method with state feedback is proposed. Then, an adaptive observer is designed to estimate the unknown state and the unknown body mass of the robot which influences the height of the center of mass. Next, the adaptive observer is combined with the designed high gain controller: a Lyapunov-based stability analysis of the closed loop system is developed to establish the convergence of the tracking error as well as estimation and adaptation errors. Simulation results assert the performance of the developed tracking control scheme for the two-wheeled self-balancing robot subject to mass variation.

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