Advanced Learning Algorithms.pdf
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An individual named Naiyan Noor has completed the online non-credit course "Advanced Learning Algorithms" offered through Coursera and authorized by DeepLearning.AI and Stanford University. The course was instructed by Andrew Ng and Eddy Shyu of DeepLearning.AI, with curriculum support from Aarti Bagul and Geoff Ladwig. Coursera has verified Noor's identity and participation in the course.
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Adaptive synchronous sliding control for a robot manipulator based on neural ...
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
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