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 …
Reinforcement learning and cooperative H∞ output regulation of linear continuous-time multi-agent systems
This paper proposes a novel control approach to solve the cooperative H∞ output
regulation problem for linear continuous-time multi-agent systems (MASs). Different from …
regulation problem for linear continuous-time multi-agent systems (MASs). Different from …
Reinforcement learning for optimal tracking of large-scale systems with multitime scales
This paper aims to solve an optimal tracking control (OTC) problem of large-scale systems
with multitime scales and coupled subsystems using singular perturbation (SP) theory and …
with multitime scales and coupled subsystems using singular perturbation (SP) theory and …
Policy gradient adaptive critic design with dynamic prioritized experience replay for wastewater treatment process control
R Yang, D Wang, J Qiao - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
With the industrialization of modern society, the pollution of water resources becomes more
and more serious. Although purifying urban sewage through the wastewater treatment …
and more serious. Although purifying urban sewage through the wastewater treatment …
Inverse reinforcement Q-learning through expert imitation for discrete-time systems
In inverse reinforcement learning (RL), there are two agents. An expert target agent has a
performance cost function and exhibits control and state behaviors to a learner. The learner …
performance cost function and exhibits control and state behaviors to a learner. The learner …
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 …
Inverse reinforcement learning for adversarial apprentice games
This article proposes new inverse reinforcement learning (RL) algorithms to solve our
defined Adversarial Apprentice Games for nonlinear learner and expert systems. The games …
defined Adversarial Apprentice Games for nonlinear learner and expert systems. The games …
Robust Inverse Q-Learning for Continuous-Time Linear Systems in Adversarial Environments
This article proposes robust inverse-learning algorithms for a learner to mimic an expert's
states and control inputs in the imitation learning problem. These two agents have different …
states and control inputs in the imitation learning problem. These two agents have different …
Reinforcement learning based optimal control of linear singularly perturbed systems
This brief studies the optimal control problem of linear singularly perturbed systems (SPSs)
via reinforcement learning (RL). We first present an offline model-based algorithm on the …
via reinforcement learning (RL). We first present an offline model-based algorithm on the …
Reinforcement learning for robust stabilization of nonlinear systems with asymmetric saturating actuators
X Yang, Y Zhou, Z Gao - Neural networks, 2023 - Elsevier
We study the robust stabilization problem of a class of nonlinear systems with asymmetric
saturating actuators and mismatched disturbances. Initially, we convert such a robust …
saturating actuators and mismatched disturbances. Initially, we convert such a robust …