Modeling Identification of the Nonlinear Robot Arm System Using MISO NARX Fuzzy Model and Genetic Algorithm

Abstract

The PAM robot arm is belonged to highly nonlinear systems where perfect knowledge of their parameters is unattainable by conventional modeling techniques because of the timevarying inertia, hysteresis and other joint friction model uncertainties. To guarantee a good tracking performance, robust-adaptive control approaches combining conventional methods with new learning techniques are required. Thanks to their universal approximation capabilities, neural networks provide the implementation tool for modeling the complex input-output relations of the multiple n DOF PAM robot arm dynamics being able to solve problems like variable-coupling complexity and state-dependency. During the last decade several neural network models and learning schemes have been applied to on-line learning of manipulator dynamics (Karakasoglu et al., 1993), (Katic et al., 1995). (Ahn and Anh, 2006a) have optimized successfully a pseudo-linear ARX model of the PAM robot arm using genetic algorithm. These authors in (Ahn and Anh, 2007) have identified the PAM manipulator based on recurrent neural networks. The drawback of all these results is considered the n-DOF robot arm as n independent decoupling joints. Consequently, all intrinsic coupling features of the n-DOF robot arm have not represented in its recurrent NN model respectively. To overcome this disadvantage, in this study, a new approach of intelligent dynamic model, namely MISO NARX Fuzzy model, firstly utilized in simultaneous modeling and identification both joints of the prototype 2-axes pneumatic artificial muscle (PAM) robot arm system. This novel model concept is also applied to (Ahn and Anh, 2009) by authors. The rest of chapter is organized as follows. Section 2 describes concisely the genetic algorithm for identifying the nonlinear NARX Fuzzy model. Section 3 is dedicated to the modeling and identification of the 2-axes PAM robot arm based on the MISO NAR Fuzzy model. Section 4 presents the experimental set-up configuration for MISO NARX Fuzzy model-based identification. The results from the MISO NARX Fuzzy model-based identification of the 2-axes PAM robot arm are presented in Section 5. Finally, in Section 6 a conclusion remark is made for this paper.