Abstract
In this paper, the NARX neural network system is used to identify the complex dynamics model of omnidirectional mobile robot while rotating with moving, and analyze its stability. When the mobile robot model rotates and moves at the same time, the dynamic model of the mobile robot is complex and there is motion coupling. The change of the model in different states is a kind of symmetry. In order to solve the problem that there is a big difference between the mechanism modeling motion simulation and the actual data, the dynamic model identification of mobile robot in special state based on NARX neural network is proposed, and the stability analysis method is given. To verify that the dynamic model of NARX identification is consistent with that of the mobile robot, the Activation Path-Dependent Lyapunov Function (APLF) algorithm is used to distinguish the NARX neural network model expressed by LDI. However, the APLF method needs to calculate a large number of LMIs in practice and takes a lot of time, and, to solve this problem, an optimized APLF method is proposed. The experimental results verify the effectiveness of the theoretical method.
Highlights
The omnidirectional mobile robot has more flexible mobile performance than the traditional wheeled mobile robot
Based on the mechanical structure characteristics of the omnidirectional mobile robot, the force analysis has carried out to establish the dynamic model of the mobile robot
The NARX neural network has been used to identify the dynamic model of the mobile robot
Summary
The omnidirectional mobile robot has more flexible mobile performance than the traditional wheeled mobile robot. It is important to reduce the use of the LDI method to represent the A matrices generated by the neural network system [16] and to reduce the number of LMIs that need to be calculated [29]. Establishes the dynamic model of the omnidirectional mobile robot and introduces the NARX neural network to identify the condition that the dynamic model cannot accurately describe the actual movement state of the robot when the mobile robot moving with rotation. This paper is organized as follows: Section 2 establishes the dynamic model of omnidirectional mobile robot.
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