Learning-Based Adaptive Control. An Extremum Seeking by Mouhacine Benosman

By Mouhacine Benosman

Adaptive keep watch over has been one of many major difficulties studied up to the mark concept. the topic is definitely understood, but it has a really lively study frontier. This publication makes a speciality of a particular subclass of adaptive regulate, particularly, learning-based adaptive keep watch over. As structures evolve in the course of time or are uncovered to unstructured environments, it truly is anticipated that a few of their features may possibly swap. This e-book deals a brand new standpoint approximately the best way to care for those adaptations. through merging jointly Model-Free and Model-Based studying algorithms, the writer demonstrates, utilizing a few mechatronic examples, how the educational strategy might be shortened and optimum keep watch over functionality should be reached and maintained.

  • Includes plenty of Mechatronics Examples of the techniques.
  • Compares and blends Model-free and Model-based studying algorithms.
  • Covers basic strategies, state of the art examine, invaluable instruments for modeling, and control.

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Extra resources for Learning-Based Adaptive Control. An Extremum Seeking Approach - Theory and Applications

Example text

We will follow here the literature of adaptive control and define direct adaptive control, as an approach where a feedback adaptive control adjusts the controller parameters online to compensate for some model uncertainties. We cite next some of the main references in direct adaptive control by decomposing them into two main groups, namely linear versus nonlinear control. 2) where the matrices A, B, C, and D are of appropriate dimensions, and could be partially known only. The coefficients of these matrices could also be time varying.

The idea behind reinforcement learning is that by trying random control actions, the controller can eventually build a predictive model of the system on which it is operating. Reinforcement learning is a class of machine learning algorithms which learns how to map states to actions in such a way to maximize a desired reward. In these algorithms the controller has to discover the best actions by trial and error. This idea was motivated by the field of psychology, where it has been realized that animals have the tendency to reselect (or not) actions based on their good (or bad) outcomes, see Thorndike (1911).

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