Robust neural control of robot-camera visual tracking
Christchurch（2009）
摘要
In this paper, we propose a new method to control a robot-camera visual tracking system to track a moving target so that the image feature of the target can match some desired one. In particular, we develop a new control algorithm to calculate the necessary joint torques. To deal with the dynamics and Jacobian uncertainty of the problem, an on-line learning neural network (NN) is used to approximate uncertain components and tune the control scheme to ensure the mismatch of the image feature vanishing to 0. We also prove the asymptotical stability of the proposed tracking method by using Lyapunov stability method.
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关键词
jacobian matrices,lyapunov methods,asymptotic stability,neural nets,neurocontrollers,robot vision,robust control,target tracking,uncertain systems,visual servoing,jacobian uncertainty,lyapunov stability method,image feature,joint torques calculation,moving target tracking,online learning neural network,robot camera visual tracking,robust neural control,image features,neural network,visual tracking
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