Abstract: In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neu- ral networks (RBFNN) are used: The first is a RBFNN identifier trained offline to emulate the dynamic performance of the DC motor-load system. The second is a RBFNN controller, which is trained to make the motor speed follow a selected reference signal. Two RBFNN control schemes are proposed using direct inverse and internal model con- trol schemes. The performance of the RBFNN identifier and controller is investigated in terms of step response, sharp changes in speed trajectory, and sudden load change, as well as changes in motor parameters. The perform- ance of RBFNN in system identification and control has been compared with the performance of the well-known back-propagation neural network (BPNN). The simulation results show that both of the BPNN and RBFNN con- trollers exhibit excellent dynamic response, adapt well to changes in speed trajectory and load connected to the motor, and adapt to the variations of motor parameters. Furthermore, the simulation results show that the step response of RBFNN internal model and direct inverse controllers are identical. Keywords: DC motors, Identification, Neurocontrol, Neural network

Abstract: In this paper, a neural network approach for the identification and control of a separately excited direct (DC) motor (SEDCM) driving a centrifugal pump load is applied. In this application, two radial basis function neu- ral networks (RBFNN) are used: The first is a RBFNN identifier tra...