Abstract
On a NVIDIA Jetson Nano device, this study illustrates a unique and original usage of automated control and artificial intelligence algorithms for angular velocity estimates of a first-order manipulator device. A platform can be used as a position estimation platform using computer vision and three state estimation algorithms: sliding mode differentiator, high gain observer, and static filter. A hybrid system for process performance improvement is proposed using computer vision and three state estimation algorithms: sliding mode differentiator, high gain observer, and static filter. The results of numerical simulations are provided, as well as real-time judgments. The ITAE and IAE indices reveal that the sliding mode differentiator is much superior in angular velocity estimation for position signals utilizing artificial intelligence sensors.
Published Version
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