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 …
Dual event-triggered constrained control through adaptive critic for discrete-time zero-sum games
D Wang, L Hu, M Zhao, J Qiao - IEEE Transactions on Systems …, 2022 - ieeexplore.ieee.org
In this article, through adaptive critic, a dual event-triggered (DET) constrained control
scheme is established for discrete-time nonlinear zero-sum games. The neural networks are …
scheme is established for discrete-time nonlinear zero-sum games. The neural networks are …
Deterministic policy gradient algorithms
In this paper we consider deterministic policy gradient algorithms for reinforcement learning
with continuous actions. The deterministic policy gradient has a particularly appealing form …
with continuous actions. The deterministic policy gradient has a particularly appealing form …
Event-triggered control of discrete-time zero-sum games via deterministic policy gradient adaptive dynamic programming
In order to address zero-sum game problems for discrete-time (DT) nonlinear systems, this
article develops a novel event-triggered control (ETC) approach based on the deterministic …
article develops a novel event-triggered control (ETC) approach based on the deterministic …
Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics
J Zhong, T Wang, L Cheng - Complex & Intelligent Systems, 2021 - Springer
In actual welding scenarios, an effective path planner is needed to find a collision-free path
in the configuration space for the welding manipulator with obstacles around. However, as a …
in the configuration space for the welding manipulator with obstacles around. However, as a …
Barrier Lyapunov function-based safe reinforcement learning for autonomous vehicles with optimized backstepping
Guaranteed safety and performance under various circumstances remain technically critical
and practically challenging for the wide deployment of autonomous vehicles. Safety-critical …
and practically challenging for the wide deployment of autonomous vehicles. Safety-critical …
[HTML][HTML] Universal workflow of artificial intelligence for energy saving
Artificial intelligence (AI) controls are commonly used to save energy. However, excessive
diversity in technological development has resulted in the inability to provide consistent …
diversity in technological development has resulted in the inability to provide consistent …
Adaptive dynamic programming for optimal control of discrete‐time nonlinear system with state constraints based on control barrier function
Adaptive dynamic programming (ADP) methods have demonstrated their efficiency.
However, many of the applications for which ADP offers great potential, are also safety …
However, many of the applications for which ADP offers great potential, are also safety …
Goal representation adaptive critic design for discrete-time uncertain systems subjected to input constraints: The event-triggered case
In this article, the event-triggered near-optimal control issue is studied for the input-
constrained uncertain system with the input-to-state stability (ISS) attribute. In the proposed …
constrained uncertain system with the input-to-state stability (ISS) attribute. In the proposed …
[HTML][HTML] Actor–critic reinforcement learning and application in developing computer-vision-based interface tracking
This paper synchronizes control theory with computer vision by formalizing object tracking
as a sequential decision-making process. A reinforcement learning (RL) agent successfully …
as a sequential decision-making process. A reinforcement learning (RL) agent successfully …