doi: 10.17586/2226-1494-2022-22-3-472-479


Adaptive control of nonlinear plant with unmatched parametric uncertainties and input saturation

A. V. Pashenko, D. N. Gerasimov, A. V. Paramonov, V. O. Nikiforov


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Pashenko A.V., Gerasimov D.N., Paramonov A.V., Nikiforov V.O. Adaptive control of nonlinear plant with unmatched parametric uncertainties and input saturation. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2022, vol. 22, no. 3, pp. 472–479 (in Russian). doi: 10.17586/2226-1494-2022-22-3-472-479


Abstract
The problem of adaptive control of a parametrically uncertain nonlinear plant presented in a state-feedback form with unmatched uncertainties and input constraint is considered. The solution is based on adaptive backstepping procedure, in which the virtual controls includes the high-order time derivatives of adjustable parameters calculated with the use of an adaptation algorithm with improved parametric convergence. Analytical design of control with compensation for the influence of input constraints is performed using of a special filter. The approach presented in this paper allows one to design an adaptive controller that ensures the boundedness for all signals in the closed-loop system and provides tracking of the reference signal. Simulation results presented in the MATLAB/Simulink environment illustrate the performance of the presented approach and the acceleration of parametric convergence with an increase of the adaptation coefficient. The plant considered in this work describes a wide class of systems, such as various manipulation robots, technological processes, electromagnetic levitation systems, chemical processes, etc. The proposed control algorithm considers natural for practical applications input constraints and significantly reduces the influence of the regulator parameters tuning speed on the control system transient processes.

Keywords: adaptive control, backstepping, nonlinear object, input saturation, input constraint, improved parametric tuning

Acknowledgements. The reported study was funded by RFBR according to the research project № 20-31-90103.

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