Identification of nonlinear systems with hysteresis characteristics

This paper proposes a method of identification for nonlinear system with saturation and hysteresis characteristics. Gears and wires have been widely used for mechatronics equipment, mechanical systems and robots as transmission and converter of the power. In such transmission and converter of power, when especially the direction of the force changes, the hysteretic behavior called the hysteresis characteristic is generated. This characteristic is a nonlinear two-valued function, and it is expressed by using the recurrent neural network. Actuators also show the nonlinear characteristic such as the saturation. They are modeled as the dynamic discrete-time system subject to gain saturation. The parameters of these models are identified based on the input-output data of the overall system which is a combined dynamical system with saturation and hysteresis characteristics. In the evaluation function for parameter estimation, the square error of the system and model is used, and the minimization of the function is carried out by nonlinear optimization techniques.