Linear Quadratic Tracking Control of Partially-Unknown Continuous-Time Systems Using Reinforcement Learning

In this technical note, an online learning algorithm is developed to solve the linear quadratic tracking (LQT) problem for partially-unknown continuous-time systems. It is shown that the value function is quadratic in terms of the state of the system and the command generator. Based on this quadrati...

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Veröffentlicht in:	IEEE transactions on automatic control 2014-11, Vol.59 (11), p.3051-3056
Hauptverfasser:	Modares, Hamidreza, Lewis, Frank L.
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Commands Dynamical systems Dynamics Equations Generators Heuristic algorithms Learning (artificial intelligence) Linear quadratic Mathematical model Optimal control Quadratic forms Reinforcement Trajectory
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