8 citations · 25 across the 16 of their papers we have counts for
17 papers
Direct Adaptive Control of Grid-Connected Power Converters via Output-Feedback Data-Enabled Policy Optimization
Feiran Zhao, Ruohan Leng, Linbin Huang +3
Power electronic converters are becoming the main components of modern power systems due to the increasing integration of renewable energy sources. However, power converters may be…
Linear Convergence of Data-Enabled Policy Optimization for Linear Quadratic Tracking
Shubo Kang, Feiran Zhao, Keyou You
Data-enabled policy optimization (DeePO) is a newly proposed method to attack the open problem of direct adaptive LQR. In this work, we extend the DeePO framework to the linear qua…
Asynchronous Parallel Policy Gradient Methods for the Linear Quadratic Regulator
Xingyu Sha, Feiran Zhao, Keyou You
Learning policies in an asynchronous parallel way is essential to the numerous successes of RL for solving large-scale problems. However, their convergence performance is still not…
Policy Gradient Methods for the Cost-Constrained LQR: Strong Duality and Global Convergence
Feiran Zhao, Keyou You
In safety-critical applications, reinforcement learning (RL) needs to consider safety constraints. However, theoretical understandings of constrained RL for continuous control are…
Harnessing Data for Accelerating Model Predictive Control by Constraint Removal
Zhinan Hou, Feiran Zhao, Keyou You
Model predictive control (MPC) solves a receding-horizon optimization problem in real-time, which can be computationally demanding when there are thousands of constraints. To accel…
Data-Enabled Policy Optimization for Direct Adaptive Learning of the LQR
Feiran Zhao, Florian Dörfler, Alessandro Chiuso +1
Direct data-driven design methods for the linear quadratic regulator (LQR) mainly use offline or episodic data batches, and their online adaptation has been acknowledged as an open…
Data-enabled Policy Optimization for the Linear Quadratic Regulator
Feiran Zhao, Florian Dörfler, Keyou You
Policy optimization (PO), an essential approach of reinforcement learning for a broad range of system classes, requires significantly more system data than indirect (identification…
Globally Convergent Policy Gradient Methods for Linear Quadratic Control of Partially Observed Systems
Feiran Zhao, Xingyun Fu, Keyou You
While the optimization landscape of policy gradient methods has been recently investigated for partially observed linear systems in terms of both static output feedback and dynamic…
Convergence and Sample Complexity of Policy Gradient Methods for Stabilizing Linear Systems
Feiran Zhao, Xingyun Fu, Keyou You
System stabilization via policy gradient (PG) methods has drawn increasing attention in both control and machine learning communities. In this paper, we study their convergence and…
System-Level Evaluation of Beam Hopping in NR-Based LEO Satellite Communication System
Jingwei Zhang, Dali Qin, Chuili Kong +4
Satellite communication by leveraging the use of low earth orbit (LEO) satellites is expected to play an essential role in future communication systems through providing ubiquitous…
Data-driven Control of Unknown Linear Systems via Quantized Feedback
Feiran Zhao, Xingchen Li, Keyou You
Control using quantized feedback is a fundamental approach to system synthesis with limited communication capacity. In this paper, we address the stabilization problem for unknown…
Learning Stabilizing Controllers of Linear Systems via Discount Policy Gradient
Feiran Zhao, Xingyun Fu, Keyou You
Stability is one of the most fundamental requirements for systems synthesis. In this paper, we address the stabilization problem for unknown linear systems via policy gradient (PG)…
Global Convergence of Policy Gradient Primal-dual Methods for Risk-constrained LQRs
Feiran Zhao, Keyou You, Tamer Başar
While the techniques in optimal control theory are often model-based, the policy optimization (PO) approach directly optimizes the performance metric of interest. Even though it ha…
Infinite-horizon Risk-constrained Linear Quadratic Regulator with Average Cost
Feiran Zhao, Keyou You, Tamer Basar
The behaviour of a stochastic dynamical system may be largely influenced by those low-probability, yet extreme events. To address such occurrences, this paper proposes an infinite-…
Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator
Feiran Zhao, Keyou You
Risk-aware control, though with promise to tackle unexpected events, requires a known exact dynamical model. In this work, we propose a model-free framework to learn a risk-aware c…
Minimax Q-learning Control for Linear Systems Using the Wasserstein Metric
Feiran Zhao, Keyou You
Stochastic optimal control usually requires an explicit dynamical model with probability distributions, which are difficult to obtain in practice. In this work, we consider the lin…
Suspension Regulation of Medium-low-speed Maglev Trains via Deep Reinforcement Learning
Feiran Zhao, Keyou You, Shiji Song +2
The suspension regulation is critical to the operation of medium-low-speed maglev trains (mlsMTs). Due to uncertain environment, strong disturbances and high nonlinearity of the sy…