Adaptive dynamic programming for control: A survey and recent advances
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 …
applications in control. First, its applications in optimal regulation are introduced, and some …
Cooperative deep reinforcement learning for large-scale traffic grid signal control
Exploiting reinforcement learning (RL) for traffic congestion reduction is a frontier topic in
intelligent transportation research. The difficulty in this problem stems from the inability of the …
intelligent transportation research. The difficulty in this problem stems from the inability of the …
An approximate neuro-optimal solution of discounted guaranteed cost control design
The adaptive optimal feedback stabilization is investigated in this article for discounted
guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis …
guaranteed cost control of uncertain nonlinear dynamical systems. Via theoretical analysis …
An adaptive neural sliding mode control with ESO for uncertain nonlinear systems
J Wang, P Zhu, B He, G Deng, C Zhang… - International journal of …, 2021 - Springer
An adaptive neural sliding mode control with ESO for uncertain nonlinear systems is
proposed to improve the stability of the control system. Any control system inevitably exists …
proposed to improve the stability of the control system. Any control system inevitably exists …
Cooperative game-based approximate optimal control of modular robot manipulators for human–robot collaboration
Major challenges of controlling human–robot collaboration (HRC)-oriented modular robot
manipulators (MRMs) include the estimation of human motion intention while cooperating …
manipulators (MRMs) include the estimation of human motion intention while cooperating …
Dynamic event-triggering neural learning control for partially unknown nonlinear systems
This article presents an event-sampled integral reinforcement learning algorithm for partially
unknown nonlinear systems using a novel dynamic event-triggering strategy. This is a novel …
unknown nonlinear systems using a novel dynamic event-triggering strategy. This is a novel …
Adaptive learning and sampled-control for nonlinear game systems using dynamic event-triggering strategy
Static event-triggering-based control problems have been investigated when implementing
adaptive dynamic programming algorithms. The related triggering rules are only current …
adaptive dynamic programming algorithms. The related triggering rules are only current …
Continuous-time distributed policy iteration for multicontroller nonlinear systems
In this article, a novel distributed policy iteration algorithm is established for infinite horizon
optimal control problems of continuous-time nonlinear systems. In each iteration of the …
optimal control problems of continuous-time nonlinear systems. In each iteration of the …
Fault-tolerant multiplayer tracking control for autonomous vehicle via model-free adaptive dynamic programming
H Pan, C Zhang, W Sun - IEEE Transactions on Reliability, 2022 - ieeexplore.ieee.org
This article investigates the completely unknown autonomous vehicle tracking issues with
actuator faults through model-free adaptive dynamic programming (MFADP) approaches …
actuator faults through model-free adaptive dynamic programming (MFADP) approaches …
Safe reinforcement learning and adaptive optimal control with applications to obstacle avoidance problem
This paper presents a novel composite obstacle avoidance control method to generate safe
motion trajectories for autonomous systems in an adaptive manner. First, system safety is …
motion trajectories for autonomous systems in an adaptive manner. First, system safety is …