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- W3134295744 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." @default.
- W3134295744 created "2021-03-15" @default.
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- W3134295744 date "2021-03-03" @default.
- W3134295744 modified "2023-10-12" @default.
- W3134295744 title "Observer-based adaptive control of robot manipulators using reinforcement learning and the Fourier series expansion" @default.
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- W3134295744 doi "https://doi.org/10.1177/0142331221995336" @default.
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