Parameter identification in nonlinear systems using PD controllers as penalty functions

Manikantan, R. and Vyasarayani, C P and Manjuprasad, M. (2020) Parameter identification in nonlinear systems using PD controllers as penalty functions. In: 6th Conference on Advances in Control and Optimization of Dynamical Systems, ACODS 2020, 16-19 February 2020, Chennai.

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The identification of parameters in nonlinear systems using a partial set of experimental measurements is considered in this paper. The estimation of these parameters introduces an optimization problem. For parameter estimation, the use of gradient-based optimizers often converges to a local minimum rather than the global optimum. To overcome the local convergence of the parameters, a PD controller algorithm is implemented for estimation. The addition of a morphing parameter with a proportional-derivative controller (PD) to the system equation transforms the objective function into convex, and the optimization is performed using a gradient-based optimizer. To illustrate the nonlinear parameter estimation using the present approach, a numerical example of Van der Pol-Duffing oscillator is presented. A comparative analysis is then carried out with global optimization methods, such as genetic algorithm (GA) and particle swarm optimization (PSO) techniques. The numerical results confirm that the PD controller algorithm is superior in terms of computational effort and convergence efficiency. © 2020, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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IITH Creators:
IITH CreatorsORCiD
Vyasarayani, C P
Item Type: Conference or Workshop Item (Paper)
Additional Information: ISSN: 2405-8963 Issue: 1
Uncontrolled Keywords: Nonlinear systems, Parameter estimation, PD controller, Van der Pol-Duffing oscillator
Subjects: Physics > Mechanical and aerospace
Physics > Modern physics
Divisions: Department of Mechanical & Aerospace Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 16 Nov 2022 06:41
Last Modified: 16 Nov 2022 06:41
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