Numerical and neural network modeling of motors of a robot

In this paper, parametric and numerical model of the motors of a robot are extracted. A method is proposed here to control the torque and velocity of the motor simultaneously using the extracted dynamics of the motor and consequently control the robot motion more accurately. Parametric model of the motors are derived by conducting standard tests like locked rotor test and step and sine wave input test. In order to derive the neural network and numerical models, a set of sinusoidal, triangular, and random steps signal, are applied as the input to the motor and its speed is recorded as the output. Neural network model of the motors is extracted by using these dataset and considering the MLP neural network structure with Levenberg _Marquardt training method. Results of the numerical model and parametric models are compared and validated by experimental tests.