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Optimal Adaptive Control Systems by David Sworder, Volume 25 - 1st Edition
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A 29 , Not Accessible Your account may give you access. Abstract This paper compares two control methods to predict and correct aero-optical wavefronts derived from recent flight-test data.
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Intelligent Optimal Adaptive Control for Mechatronic Systems
Username Password. Most reinforcement learning methods require full measurements of the system internal state. In this paper we develop reinforcement learning methods which require only output feedback and yet converge to an optimal controller. Deterministic linear time-invariant systems are considered.
Both policy iteration PI and value iteration VI algorithms are derived. It is shown that, similar to Q-learning, the new output-feedback optimal learning methods have the important advantage that knowledge of the system dynamics is not needed for their implementation. The learned output feedback controller is in the form of a polynomial ARMA controller that has equivalent performance with the optimal state variable feedback gain.