609 citations · 1.1k across the 13 of their papers we have counts for
7 papers · 1 filter
Novel Nussbaum-Type Function based Safe Adaptive Distributed Consensus Control with Arbitrary Unknown Control Direction
Dan Qiao, Zhaoxia Peng, Guoguang Wen +1
Existing Nussbaum function based methods on the consensus of multi-agent systems require (partial) identical unknown control directions of all agents and cause dangerous dramatic c…
Robust Adaptive Safety-Critical Control for Unknown Systems with Finite-Time Element-Wise Parameter Estimation
Shengbo Wang, Bo Lyu, Shiping Wen +3
Safety is always one of the most critical principles for a system to be controlled. This paper investigates a safety-critical control scheme for unknown structured systems by using…
Optimal Tracking Control for Unknown Linear Systems with Finite-Time Parameter Estimation
Shengbo Wang, Shiping Wen, Kaibo Shi +2
The optimal control input for linear systems can be solved from algebraic Riccati equation (ARE), from which it remains questionable to get the form of the exact solution. In engin…
Q-learning for Optimal Control of Continuous-time Systems
Biao Luo, Derong Liu, Tingwen Huang
In this paper, two Q-learning (QL) methods are proposed and their convergence theories are established for addressing the model-free optimal control problem of general nonlinear co…
Data-based approximate policy iteration for nonlinear continuous-time optimal control design
Biao Luo, Huai-Ning Wu, Tingwen Huang +1
This paper addresses the model-free nonlinear optimal problem with generalized cost functional, and a data-based reinforcement learning technique is developed. It is known that the…
Off-policy reinforcement learning for control design
Biao Luo, Huai-Ning Wu, Tingwen Huang
The control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear control problem can be tra…
Chaotic Characteristic of Discrete-time Linear Inclusion Dynamical Systems
Xiongping Dai, Tingwen Huang, Yu Huang +1
In this paper, we study the fiber-chaos of switched linear dynamical systems.