Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion. (June 2021)

• Record Type: Journal Article
• Authors: Khodamipour, Gholamreza; Khorashadizadeh, Saeed; Farshad, Mohsen
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 43:Number 10(2021)
• Page Start: 2307
• Page End: 2320
• Publication Date: 2021-06
• Subjects: Observer-based controller -- reinforcement learning -- model-free controller -- Fourier series expansion -- robot manipulator; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331221995336
• Languages: English
• ISSNs: 0142-3312

Abstract

Designing observer-controller structures for nonlinear system with unknown dynamics such as robotic systems is among popular research fields in control engineering. The novelty of this paper is in presenting an observer-based model-free controller for robot manipulators using reinforcement learning (RL). The proposed controller calculates the desired motor voltages that fulfil a satisfactory tracking performance. Moreover, the uncertainties and nonlinearities in the observer model and RL controller are estimated and compensated for by using the Fourier series expansion. Simulation results and comparison with the previous related works (extended state observer and radial basis function neural networks) indicate the satisfactory performance of the proposed method.