Abstract
Identification of robotic systems with hysteresis is the main focus of this article. Nonlinear AutoRegressive eXogenous input models are proposed to describe the systems with hysteresis, with no limitation on the nonlinear characteristics. The article introduces an efficient approach to select model terms. This selection process is achieved using an orthogonal forward regression based on the leave-one-out cross-validation. A sampling rate reduction procedure is proposed to be incorporated into the term selection process. Two simulation examples corresponding to two typical hysteresis phenomena and one experimental example are finally presented to illustrate the applicability and effectiveness of the proposed approach.
Highlights
Hysteresis, a memory-dependent, multivalued relation between input and output, is often observed in many robotic systems
The polynomial Nonlinear AutoRegressive with eXogenous input (NARX) model has been considered for modeling robotic systems with hysteresis
The model term selection problem has been investigated for using polynomial NARX models
Summary
Hysteresis, a memory-dependent, multivalued relation between input and output, is often observed in many robotic systems. The system may exhibit a pathdependent pattern, where multiple outputs are associated with increasing or decreasing but the same input and form a loop under cyclic excitation. It exists in many applications, such as actuators and sensors involving smart materials (e.g. piezoelectrics[1,2] and magnetostrictive materials3,4) which possess the property of hysteresis in the reaction, and some special robotic systems with hysteretic dynamics like aerial vehicles.[5] The control of these robots is difficult due to the presence of the high nonlinearity. Hysteresis phenomenon.[8,9,10,11] Two of the most popular models are explained in detail
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