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A Novel Tracking Control of Mobile Robots Based on Bioinspired Neural Dynamics and Unscented Kalman Filter

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Title: A Novel Tracking Control of Mobile Robots Based on Bioinspired Neural Dynamics and Unscented Kalman Filter
Author: Xu, Zhe
Department: School of Engineering
Program: Engineering
Advisor: Yang, Simon
Abstract: Tracking control has been a vital research area for robotics. It is important to design robotic systems with smooth velocity commands and robustness to system and measurement noises. This thesis presents a novel controller based on a bioinspired neural dynamic model and the unscented Kalman filter. Typical backstepping techniques usually suffer from speed jumps, which may create discontinuities in the speed of robotic systems. This disadvantage of backstepping techniques creates difficulties in applications to real robots. The bioinspired backstepping controller is able to generate smooth velocity commands without any speed jumps. Additionally, in real world applications, the robotic system may operate in noisy environments or be equipped with imperfect sensors that result in inaccurate measurements due to the system and measurement noises. The unscented Kalman filter is capable of reducing the effects of these noises and providing accurate estimates. Therefore, the innovative contribution of this thesis is that the unscented Kalman filter is integrated with the bioinspired backstepping control, minimizing the effects of the system and measurement noises to the mobile robot. Simulation experiments demonstrate the efficiency and effectiveness of the proposed bioinspired controller with unscented Kalman filter in removing speed jumps existed in conventional backstepping controllers, and providing accurate state estimates of robotic systems.
Date: 2019
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