Скачать с ютуб Clear, Complete, and Correct Explanation of State Observers with Python Implementation - Control в хорошем качестве

Clear, Complete, and Correct Explanation of State Observers with Python Implementation - Control

#controltheory #mechatronics #systemidentification #machinelearning #datascience #recurrentneuralnetworks #signalprocessing #dynamics #mechanics #mechanicalengineering #controltheory #mechatronics #robotics #astrodynamics #astrophysics #physics #chaos #mathematics #mathematicians#electricalengineering #mechanicalengineering #engineering #leastsquares #nonlinearsystems #modelpredictivecontrol #optimalcontrol #controlengineering #controltheory #optimalcontrol #modelpredictivecontrol #robotics #reinforcementlearning #automation #industrialautomation #processcontrol #systemidentification #machinelearning #python #optimization #datascience #timeseries #automation #robotics #mechatronics #gnc #nonlinear #mathematics #signalprocessing #processengineering #processautomation #observability #controllability #estimation #linearsystems #advancedcontrol It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way: - Buy me a Coffee: https://www.buymeacoffee.com/Aleksand... - PayPal: https://www.paypal.me/AleksandarHaber - Patreon: https://www.patreon.com/user?u=320801... - You Can also press the Thanks YouTube Dollar button In this control engineering and control theory tutorial, we provide a correct and detailed explanation of state observers that are used for state estimation of state-space models. We explain how to implement and simulate observers in Python. The main motivation for creating this video tutorial comes from the fact that in a number of control engineering books and online tutorials, observers are presented from the mathematical perspective without providing enough practical insights and intuitive explanations of observers and state estimators. In contrast, in this control engineering tutorial, we provide an intuitive explanation of observers as well as a concise mathematical explanation. The observer developed in this tutorial belongs to the class of Luenberger Observers. We use the pole placement method to design the observer gain matrix. We design, implement, and simulate the observer in Python by using Control Systems Library and the function place().