Machine learning in medical applications: A review of state-of-the-art methods

M Shehab, L Abualigah, Q Shambour… - Computers in Biology …, 2022‏ - Elsevier
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …

Adaptive dynamic programming for control: A survey and recent advances

D Liu, S Xue, B Zhao, B Luo… - IEEE Transactions on …, 2020‏ - ieeexplore.ieee.org
This article reviews the recent development of adaptive dynamic programming (ADP) with
applications in control. First, its applications in optimal regulation are introduced, and some …

Adaptive multi-step evaluation design with stability guarantee for discrete-time optimal learning control

D Wang, J Wang, M Zhao, P **n… - IEEE/CAA Journal of …, 2023‏ - ieeexplore.ieee.org
This paper is concerned with a novel integrated multi-step heuristic dynamic programming
(MsHDP) algorithm for solving optimal control problems. It is shown that, initialized by the …

Discounted iterative adaptive critic designs with novel stability analysis for tracking control

M Ha, D Wang, D Liu - IEEE/CAA Journal of Automatica Sinica, 2022‏ - ieeexplore.ieee.org
The core task of tracking control is to make the controlled plant track a desired trajectory. The
traditional performance index used in previous studies cannot eliminate completely the …

Reinforcement learning-based decentralized fault tolerant control for constrained interconnected nonlinear systems

Y Zhao, H Wang, N Xu, G Zong, X Zhao - Chaos, Solitons & Fractals, 2023‏ - Elsevier
This paper addresses the decentralized fault tolerant control problem for interconnected
nonlinear systems under a reinforcement learning strategy. The system under consideration …

Online reinforcement learning multiplayer non-zero sum games of continuous-time Markov jump linear systems

X **n, Y Tu, V Stojanovic, H Wang, K Shi, S He… - Applied Mathematics and …, 2022‏ - Elsevier
In this paper, a novel online mode-free integral reinforcement learning algorithm is proposed
to solve the multiplayer non-zero sum games. We first collect and learn the subsystems …

Optimal and autonomous control using reinforcement learning: A survey

B Kiumarsi, KG Vamvoudakis… - IEEE transactions on …, 2017‏ - ieeexplore.ieee.org
This paper reviews the current state of the art on reinforcement learning (RL)-based
feedback control solutions to optimal regulation and tracking of single and multiagent …

Model-Free λ-Policy Iteration for Discrete-Time Linear Quadratic Regulation

Y Yang, B Kiumarsi, H Modares… - IEEE Transactions on …, 2021‏ - ieeexplore.ieee.org
This article presents a model-free-policy iteration (-PI) for the discrete-time linear quadratic
regulation (LQR) problem. To solve the algebraic Riccati equation arising from solving the …

Safe reinforcement learning using robust MPC

M Zanon, S Gros - IEEE Transactions on Automatic Control, 2020‏ - ieeexplore.ieee.org
Reinforcement learning (RL) has recently impressed the world with stunning results in
various applications. While the potential of RL is now well established, many critical aspects …

Reinforcement learning for control: Performance, stability, and deep approximators

L Buşoniu, T De Bruin, D Tolić, J Kober… - Annual Reviews in …, 2018‏ - Elsevier
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …