3 Quadratic Optimal Control 5 3.1 Linear Quadratic Optimal State Regulator Design . . . . . . . . . . . . . . . . . 6 3.2 Linear Quadratic Optimal Output Regulator Design . . . . . . . . . . . . . . . . 9 3.3 Stability of LQ controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.4 Linear Quadratic Gaussian (LQG) Control . . . . . . . . . . . . . . . . . . . . . 11 3.4.1 S...

in this paper, the consequences of well-known characteristics of near-fault ground motions, forward directivity and fling step, on the seismic response control is investigated. an integrated fuzzy rule-based control strategy for building structures incorporated with semi active friction damping system with amplifying braces (fdsab) is developed. the membership functions and fuzzy rules of fuzzy...

Abstract: Both the linear moving horizon estimator and controller may be solved by solving a linear quadratic optimal control problem. A primal active set, a dual active set, and an interior point algorithm for solution of the linear quadratic optimal control problem are presented. The major computational effort in all these algorithms reduces to solution of certain unconstrained linear quadrat...

We consider the exploration-exploitation tradeoff in linear quadratic (LQ) control problems, where the state dynamics is linear and the cost function is quadratic in states and controls. We analyze the regret of Thompson sampling (TS) (a.k.a. posterior-sampling for reinforcement learning) in the frequentist setting, i.e., when the parameters characterizing the LQ dynamics are fixed. Despite the...

Abstract. In this paper, we formulate a general time-inconsistent stochastic linear–quadratic (LQ) control problem. The time-inconsistency arises from the presence of a quadratic term of the expected state as well as a state-dependent term in the objective functional. We define an equilibrium, instead of optimal, solution within the class of open-loop controls, and derive a sufficient condition...

In this paper we present stability and convergence results for Dynamic Programming-based reinforcement learning applied to Linear Quadratic Regulation (LQR). The spe-ciic algorithm we analyze is based on Q-learning and it is proven to converge to the optimal controller provided that the underlying system is controllable and a particular signal vector is persistently excited. The performance of ...

The adaptive linear quadratic Gaussian control problem, where the linear transformation of the state A and the linear transformation of the control B are unknown, is solved assuming only that (A; B) is controllable and (A; Q 1 ) is observable, where Q 1 determines the quadratic form for the state in the integrand of the cost functional. A weighted least squares algorithm is modified by using a ...

We apply the recently developed duality methods based on information relaxations to the classic linear quadratic (LQ) control problem. We derive two dual optimal penalties for the LQ problem when the control space is unconstrained. These two penalties, which are derived using value function and gradient methods, respectively, may be used to evaluate sub-optimal policies for constrained LQ probl...