Chaitanya V Sree Krishna
Department of Mechanical Engineering, Indian School of Mines, Dhanbad, India.
Int J Neural Syst. 2005 Oct;15(5):403-14. doi: 10.1142/S0129065705000372.
In this paper a nonholonomic mobile robot with completely unknown dynamics is discussed. A mathematical model has been considered and an efficient neural network is developed, which ensures guaranteed tracking performance leading to stability of the system. The neural network assumes a single layer structure, by taking advantage of the robot regressor dynamics that expresses the highly nonlinear robot dynamics in a linear form in terms of the known and unknown robot dynamic parameters. No assumptions relating to the boundedness is placed on the unmodeled disturbances. It is capable of generating real-time smooth and continuous velocity control signals that drive the mobile robot to follow the desired trajectories. The proposed approach resolves speed jump problem existing in some previous tracking controllers. Further, this neural network does not require offline training procedures. Lyapunov theory has been used to prove system stability. The practicality and effectiveness of the proposed tracking controller are demonstrated by simulation and comparison results.
本文讨论了一个动力学完全未知的非完整移动机器人。考虑了一个数学模型并开发了一种高效的神经网络，该网络确保了有保证的跟踪性能，从而实现系统的稳定性。该神经网络采用单层结构，利用机器人回归器动力学，它以已知和未知机器人动力学参数的线性形式表示高度非线性的机器人动力学。对未建模干扰不做任何有界性假设。它能够生成实时平滑且连续的速度控制信号，驱动移动机器人跟踪期望轨迹。所提出的方法解决了一些先前跟踪控制器中存在的速度跳跃问题。此外，该神经网络不需要离线训练过程。利用李雅普诺夫理论证明了系统的稳定性。通过仿真和比较结果验证了所提出跟踪控制器的实用性和有效性。
