Gaussian Process Active Learning Modal Python Acquisition
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%2C%20an%20advanced%20approach%20integrating%20Gaussian%20process%20(GP)%20with%20model%20predictive%20control%20(MPC)%20for%20enhanced%20control%20in%20complex%20systems.%20It%20begins%20with%20GP%20regression%20fundamentals%2C%20illustrating%20how%20it%20enriches%20MPC%20with%20enhanced%20predictive%20accuracy%20and%20robust%20handling%20of%20uncertainties.%20A%20central%20contribution%20of%20this%20tutorial%20is%20the%20first%20detailed%2C%20systematic%20mathematical%20formulation%20of%20GP-MPC%20in%20literature%2C%20focusing%20on%20deriving%20the%20approximation%20of%20means%20and%20variances%20propagation%20for%20GP%20multi-step%20predictions.%20Practical%20applications%20in%20robotics%20control%2C%20such%20as%20path-following%20for%20mobile%20robots%20in%20challenging%20terrains%20and%20mixed-vehicle%20platooning%2C%20are%20discussed%20to%20demonstrate%20the%20real-world%20effectiveness%20and%20adaptability%20of%20GP-MPC.%20This%20tutorial%20aims%20to%20make%20GP-MPC%20accessible%20to%20researchers%20and%20practitioners%2C%20enriching%20the%20learning-based%20control%20field%20with%20in-depth%20theoretical%20and%20practical%20insights%20and%20fostering%20further%20innovations%20in%20complex%20system%20control.)






























