Search

Your search keyword '"Jiang, Zhong-Ping"' showing total 1,186 results
Start Over Author "Jiang, Zhong-Ping"
1,186 results on '"Jiang, Zhong-Ping"'

174. A Reinforcement Learning Look at Risk-Sensitive Linear Quadratic Gaussian Control

Author

Cui, Leilei and Jiang, Zhong-Ping

Subjects
FOS: Electrical engineering, electronic engineering, information engineering, Systems and Control (eess.SY), Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper proposes a novel robust reinforcement learning framework for discrete-time systems with model mismatch that may arise from the sim2real gap. A key strategy is to invoke advanced techniques from control theory. Using the formulation of the classical risk-sensitive linear quadratic Gaussian control, a dual-loop policy iteration algorithm is proposed to generate a robust optimal controller. The dual-loop policy iteration algorithm is shown to be globally exponentially and uniformly convergent, and robust against disturbance during the learning process. This robustness property is called small-disturbance input-to-state stability and guarantees that the proposed policy iteration algorithm converges to a small neighborhood of the optimal controller as long as the disturbance at each learning step is small. In addition, when the system dynamics is unknown, a novel model-free off-policy policy iteration algorithm is proposed for the same class of dynamical system with additive Gaussian noise. Finally, numerical examples are provided for the demonstration of the proposed algorithm., Comment: 21 Pages, 4 Figures

Published
2022
Full Text
View/download PDF

177. A Unified Framework for Data-Driven Optimal Control of Connected Vehicles in Mixed Traffic

Author

Liu, Tong, Cui, Leilei, Pang, Bo, and Jiang, Zhong-Ping

Abstract

This article presents a unified approach to the problem of learning-based optimal control of connected human-driven and autonomous vehicles in mixed-traffic environments including both the freeway and ring road settings. The stabilizability of a string of connected vehicles including multiple autonomous vehicles (AVs) and heterogeneous human-driven vehicles (HDVs) is studied by a model reduction technique and the Popov-Belevitch-Hautus (PBH) test. For this problem setup, a linear quadratic regulator (LQR) problem is formulated and a solution based on adaptive dynamic programming (ADP) techniques is proposed without a priori knowledge on model parameters. To start the learning process, an initial stabilizing control law is obtained using the small-gain theorem for the ring road case. It is shown that the obtained stabilizing control law can achieve general $\mathcal {L}_{p}$ string stability under appropriate conditions. Besides, to minimize the impact of external disturbance, a linear quadratic zero-sum game is introduced and solved by an iterative learning-based algorithm. Finally, the simulation results verify the theoretical analysis and the proposed methods achieve desirable performance for control of a mixed-vehicular network.

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results