Learning Algorithm for LQG Model With Constrained Control
نویسندگان
چکیده
The paper considers a discrete-time linear quadratic Gaussian model with constrained control. It is formulated with Markov systems. With the derivative equation, a performance gradient with respect to control parameters is estimated from a sample path. Then a learning algorithm is proposed to obtain a suboptimal feedback policy in affine linear form. The learning algorithm can be implemented on-line. Its improving feature makes the algorithm attain better performance than existing approaches, and the idea can be applied to more general cases.
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تاریخ انتشار 2008