Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 24 August 2020Accepted: 28 January 2021Published online: 08 April 2021Keywordslinear quadratic problem, overtaking optimal control, controllabilityAMS Subject Headings49J15, 49N10, 93B05Publication DataISSN (print): 0363-0129ISSN (online): 1095-7138Publisher: Society for ...

Linearly solvable Markov Decision Process (LMDP) is a class of optimal control problem in which the Bellman's equation can be converted into a linear equation by an exponential transformation of the state value function (Todorov, 2009b). In an LMDP, the optimal value function and the corresponding control policy are obtained by solving an eigenvalue problem in a discrete state space or an eigen...

This study investigates the efficacy of optimal semi-active dampers for achieving the best results in seismic response mitigation of adjacent buildings connected to each other by magnetorheological (MR) dampers under earthquakes. One of the challenges in the application of this study is to develop an effective optimal control strategy that can fully utilize the capabilities of the MR dampers. H...

In this paper we consider new regularization methods for linear inverse problems of dynamic type. These are based on programming techniques quadratic optimal control problems. Two different approaches followed: a continuous and discrete one. We prove properties also obtain rates convergence the derived from both approaches. A numerical example concerning EIT problem is used to illustrate theore...

NIE, TIANTIAN. Quadratic Programming with Discrete Variables. (Under the direction of Dr. Shu-Cherng Fang.) In this dissertation, we study the quadratic programming problem with discrete variables (DQP). DQP is important in theory and practice, but the combination of the quadratic feature of the objective function and the discrete nature of the feasible domain makes it hard to solve. In this th...

In this paper, the finite-horizon near optimal adaptive regulation of linear discrete-time systems with unknown system dynamics is presented in a forward-in-time manner by using adaptive dynamic programming and Q-learning. An adaptive estimator (AE) is introduced to relax the requirement of system dynamics, and it is tuned by using Q-learning. The time-varying solution to the Bellman equation i...