Solutions already exist for the problem of canceling sinusoidal disturbances by measurement of the state or by measurement of an output for linear and nonlinear systems. In this paper, we design an adaptive backstepping controller to cancel unmatched sinusoidal disturbances forcing a linear time-invariant system which is augmented by a linear input subsystem by using only measurement of state-derivatives of the original subsystem and state of the input subsystem. Our design is based on four steps, 1) parametrization of the sinusoidal disturbance as the output of a known feedback system with an unknown output vector that depends on unknown disturbance parameters, 2) design of an adaptive disturbance observer for both disturbance and its derivative, 3) design of an adaptive controller for virtual control input, and 4) design final controller by defining error system and using backstepping procedure. We prove that the equilibrium of the closed-loop adaptive system is stable and state of the considered error system goes to zero as t → ∞ with perfect disturbance estimation. The effectiveness of the controller is illustrated with a simulation example of a third order system.
- Dynamic Systems and Control Division
Adaptive Backstepping Cancelation of Unmatched Unknown Sinusoidal Disturbances for LTI Systems by State Derivative Feedback
Baştürk, Hİ, & Krstic, M. "Adaptive Backstepping Cancelation of Unmatched Unknown Sinusoidal Disturbances for LTI Systems by State Derivative Feedback." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 1-9. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8529
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