This paper investigates an adaptive output-feedback stabilization problem for a class of uncertain nonlinear systems with an unknown sensor sensitivity. A high-gain observer based on an inexact system output is designed to estimate unmeasurable state variables and an adaptive output-feedback dynamic surface control strategy is established to accomplish the robust stabilization in the presence of the unknown sensor sensitivity. Compared with the existing control results dealing with the unknown sensor sensitivity, the primary contribution of this paper is that the proposed adaptive control strategy does not require the information of the sensor sensitivity and its bound where the effect of the unknown sensor sensitivity is compensated by designing an adaptation structure. From the Lyapunov stability analysis, the ultimate uniform boundedness of all closed-loop signals and the convergence of the stabilization error to an adjustable neighborhood of the origin are ensured. A simulation for a numerical example and an experiment for a flexible-joint robot are provided to validate the effectiveness of the proposed theoretical approach.
A decentralized adaptive resilient output-feedback stabilization strategy is presented for a class of uncertain interconnected nonlinear systems with unknown time-varying measurement sensitivities. In the concerned problem, the main difficulty is to achieve the decentralization of interconnected output nonlinearities unmatched to the control input by using only local output information corrupted by measurement sensitivity, namely the exact output information cannot be used to design the decentralized output-feedback control scheme. Thus, a decentralized output-feedback stabilizer design using only the corrupted output of each subsystem is developed where the adaptive control technique is employed to compensate for the effects of unknown measurement sensitivities. The stability of the resulting decentralized control scheme is analyzed based on the Lyapunov stability theorem.
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