In this paper, a Q-learning algorithm is proposed to solve the linear quadratic regulator problem of black box systems. The only has access input and output measurements. A Luenberger observer parametrization constructed using control new obtained from factorization utility function. An integral reinforcement learning approach used develop approximator structure. gradient descent update rule es...

In this work, we study a natural extension of the Linear Quadratic Regulator (LQR) on time scales. Here, we unify and extend the Linear Quadratic Tracker (LQT). We seek to find an affine optimal control that minimises a cost functional associated with a completely observable linear system. We then find an affine optimal control for the fixed final state case in terms of the current state. Final...

in this article, we characterize the involutiveness of the linear combination of the forma1a1 +a2a2 when a1, a2 are nonzero complex numbers, a1 is a quadratic or tripotent matrix,and a2 is arbitrary, under certain properties imposed on a1 and a2.

We consider the static output feedback control for Linear Quadratic Regulator problems with structured constraints under assumption that system parameters are unknown. To solve problem in model free setting, we propose policy gradient algorithm based on projection method and show its global convergence to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3....

An overview of recent results concerning the non-standard, finite horizon Linear Quadratic Regulator problem for a class of boundary control systems is provided.