Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications
D Wang, N Gao, D Liu, J Li… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …
adaptive/approximate dynamic programming (ADP) within the control community. This paper …
Review of electric vehicles integration impacts in distribution networks: Placement, charging/discharging strategies, objectives and optimisation models
Vehicles around the world are being converted to electric power in order to combat climate
change and lower pollution levels. Sustaining this process calls for more electric vehicle …
change and lower pollution levels. Sustaining this process calls for more electric vehicle …
The intelligent critic framework for advanced optimal control
The idea of optimization can be regarded as an important basis of many disciplines and
hence is extremely useful for a large number of research fields, particularly for artificial …
hence is extremely useful for a large number of research fields, particularly for artificial …
Sliding-mode surface-based decentralized event-triggered control of partially unknown interconnected nonlinear systems via reinforcement learning
T Wang, H Wang, N Xu, L Zhang, KH Alharbi - Information Sciences, 2023 - Elsevier
This paper studies a sliding-mode surface (SMS)-based decentralized event-triggered
control problem for a class of partially unknown interconnected nonlinear systems within an …
control problem for a class of partially unknown interconnected nonlinear systems within an …
Discounted iterative adaptive critic designs with novel stability analysis for tracking control
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 …
traditional performance index used in previous studies cannot eliminate completely the …
An integrated multitasking intelligent bearing fault diagnosis scheme based on representation learning under imbalanced sample condition
Accurate bearing fault diagnosis is of great significance of the safety and reliability of rotary
mechanical system. In practice, the sample proportion between faulty data and healthy data …
mechanical system. In practice, the sample proportion between faulty data and healthy data …
Value iteration and adaptive optimal output regulation with assured convergence rate
In this paper, we investigate the learning-based adaptive optimal output regulation problem
with convergence rate requirement for disturbed linear continuous-time systems. An …
with convergence rate requirement for disturbed linear continuous-time systems. An …
Hamiltonian-driven adaptive dynamic programming with efficient experience replay
This article presents a novel efficient experience-replay-based adaptive dynamic
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …
programming (ADP) for the optimal control problem of a class of nonlinear dynamical …
Adaptive critic learning for approximate optimal event-triggered tracking control of nonlinear systems with prescribed performances
T Wang, L Zhang, N Xu, KH Alharbi - International Journal of …, 2023 - Taylor & Francis
This paper studies the approximate optimal event-triggered tracking control problem for a
class of second-order nonlinear systems with prescribed performances. By employing a …
class of second-order nonlinear systems with prescribed performances. By employing a …
Resilient reinforcement learning and robust output regulation under denial-of-service attacks
In this paper, we have proposed a novel resilient reinforcement learning approach for
solving robust optimal output regulation problems of a class of partially linear systems under …
solving robust optimal output regulation problems of a class of partially linear systems under …