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 …

Fault-tolerant control of a hydraulic servo actuator via adaptive dynamic programming

V Stojanović - 2023‏ - scidar.kg.ac.rs
The fault-tolerant control problem of a hydraulic servo actuator in the presence of actuator
faults is studied utilizing adaptive dynamic programming. This task is challenging because of …

Data-driven control of hydraulic servo actuator: An event-triggered adaptive dynamic programming approach

V Djordjevic, H Tao, X Song, S He, W Gao… - 2023‏ - scidar.kg.ac.rs
Hydraulic servo actuators (HSAs) are often used in the industry in tasks that request great
power, high accuracy and dynamic motion. It is well known that an HSA is a highly complex …

Data-driven control of hydraulic servo actuator based on adaptive dynamic programming

V Djordjevic, V Stojanovic, H Tao, X Song… - … Dynamical Systems-S, 2022‏ - aimsciences.org
The hydraulic servo actuators (HSA) are often used in the industry in tasks that request great
powers, high accuracy and dynamic motion. It is well known that HSA is a highly complex …

Output-feedback adaptive optimal control of interconnected systems based on robust adaptive dynamic programming

W Gao, Y Jiang, ZP Jiang, T Chai - Automatica, 2016‏ - Elsevier
This paper studies the adaptive and optimal output-feedback problem for continuous-time
uncertain systems with nonlinear dynamic uncertainties. Data-driven output-feedback …

Optimal output-feedback control of unknown continuous-time linear systems using off-policy reinforcement learning

H Modares, FL Lewis, ZP Jiang - IEEE transactions on …, 2016‏ - ieeexplore.ieee.org
A model-free off-policy reinforcement learning algorithm is developed to learn the optimal
output-feedback (OPFB) solution for linear continuous-time systems. The proposed …

Reinforcement learning-based linear quadratic regulation of continuous-time systems using dynamic output feedback

SAA Rizvi, Z Lin - IEEE transactions on cybernetics, 2019‏ - ieeexplore.ieee.org
In this paper, we propose a model-free solution to the linear quadratic regulation (LQR)
problem of continuous-time systems based on reinforcement learning using dynamic output …

Robust output regulation and reinforcement learning-based output tracking design for unknown linear discrete-time systems

C Chen, L **e, Y Jiang, K **e… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
In this article, we investigate the optimal output tracking problem for linear discrete-time
systems with unknown dynamics using reinforcement learning (RL) and robust output …

Inverse reinforcement learning for trajectory imitation using static output feedback control

W Xue, B Lian, J Fan, T Chai… - IEEE Transactions on …, 2023‏ - ieeexplore.ieee.org
This article studies the trajectory imitation control problem of linear systems suffering
external disturbances and develops a data-driven static output feedback (OPFB) control …

Model-free optimal output regulation for linear discrete-time lossy networked control systems

J Fan, Q Wu, Y Jiang, T Chai… - IEEE Transactions on …, 2019‏ - ieeexplore.ieee.org
In this article, a new model-free approach is proposed to solve the output regulation problem
for networked control systems, where the system state can be lost in the feedback process …