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RBFNN Based Decentralized Adaptive Tracking Control Using PSO for an Uncertain Electrically Driven Robot System with Input Saturation  

Shin, Jin-Ho (Dong-eui University, Department of Electronic Engineering)
Han, Dae-Hyun (Dong-eui University, Department of Electronic Engineering)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.19, no.2, 2018 , pp. 77-88 More about this Journal
Abstract
This paper proposes a RBFNN(Radial Basis Function Neural Network) based decentralized adaptive tracking control scheme using PSO(Particle Swarm Optimization) for an uncertain electrically driven robot system with input saturation. Practically, the magnitudes of input voltage and current signals are limited due to the saturation of actuators in robot systems. The proposed controller overcomes this input saturation and does not require any robot link and actuator model parameters. The fitness function used in the presented PSO scheme is expressed as a multi-objective function including the magnitudes of voltages and currents as well as the tracking errors. Using a PSO scheme, the control gains and the number of the RBFs are tuned automatically and thus the performance of the control system is improved. The stability of the total control system is guaranteed by the Lyapunov stability analysis. The validity and robustness of the proposed control scheme are verified through simulation results.
Keywords
Robot system; Decentralized adaptive control; Particle swarm optimization; Input saturation; Radial basis function neural network;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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