Identification and Control of an Electro Hydraulic Robot Particle Swarm Optimization-neural Network(PSO-NN) Approach

This paper proposes a novel approach based on the training of the Neural Network method with Particle Swarm Optimization (PSO-NN) for identification of a hydraulic servo robot. The robot is considered to have two degrees of freedom; one is rotational and the other is translational. A feed forward NN is designed for the problem and the weights of the network are trained using Particle Swarm Optimization (PSO) algorithm. In order to demonstrate the performance of PSO-NN, the designed network is also trained and tested with the Back Propagation (BP-NN) algorithm. Test results validated that the performance of PSONN is better than BP-NN algorithm both in convergence speed and in convergence accuracy. The results also illustrate that PSO-NN algorithm is an applicable and effective method for identification and control of a robotic system.

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