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Concurrent learning based finite time parameter estimation in adaptive control of uncertain switched systems

Yazdani, M ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1109/ICRoM.2016.7886856
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2017
  4. Abstract:
  5. In this paper, We propose concurrent learning adaptive controller, which uses recorded and current data concurrently for adaptation, to model reference adaptive control (MRAC) of uncertain switched systems. In standard MRAC architecture for switched systems, the adaptive update laws are derived based on the gradient descent scheme, but here we developed two novel parameter estimation schemes by using modification terms in adaptation laws in which recorded data is used simultaneously with current data and a triggering time is considered in which a sufficient condition on the linear independence of the recorded data is obtained to guarantee the exponential convergence of tracking error and parameter estimation error to zero for the uncertain switched system under all admissible switching strategy. The convergence of the parameters to the ideal values makes an on-line learned model of the system available. This sufficient condition is easily verifiable in comparison to the restrictive persistence of excitation (PE) condition of the standard MRAC structures in practical applications. Finally a simulation example is given to illustrate the efficacy of the proposed method. © 2016 IEEE
  6. Keywords:
  7. Finite time parameter estimation ; Model reference adaptive control(MRAC) ; Uncertain nonlinear switched systems ; Adaptive control systems ; Concurrency control ; Parameter estimation ; Robotics ; Switching systems ; Uncertainty analysis ; Concurrent learning adaptation ; Exponential convergence ; Finite time ; Nonlinear switched systems ; Parameter estimation errors ; Persistence of excitation ; Switching strategies ; Uncertain switched systems ; Model reference adaptive control
  8. Source: 4th RSI International Conference on Robotics and Mechatronics, ICRoM 2016, 26 October 2016 through 28 October 2016 ; 2017 , Pages 258-265 ; 9781509032228 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/7886856