Abstract
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.
Highlights
For the implementation of control systems, sensors play a crucial role in measuring the state variables of physical systems, and the measurement precision of the sensor affects the control performance [1]
Output-feedback control approaches were proposed via the feedback domination approach [6,7,8,9,10], and a sampled data output-feedback stabilizer was developed in [11]
In [12], the output-feedback controller using the dual-domination approach was designed for nonlinear systems with unknown time-varying measurement sensitivity
Summary
For the implementation of control systems, sensors play a crucial role in measuring the state variables of physical systems, and the measurement precision of the sensor affects the control performance [1]. Due manufacturing reasons or the practical limitations of sensors, the measured state variables can be inaccurate in the practical environment [2,3]. A main issue is to design stable controllers regardless of the inaccurate measurement of sensors. In [12], the output-feedback controller using the dual-domination approach was designed for nonlinear systems with unknown time-varying measurement sensitivity. These control approaches require the knowledge about the bounds of the partial derivatives of unknown output functions [6,7,8,9,10,11] and the bounds of unknown time-varying measurement sensitivity [12]
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