Data-Driven Robust Control Using Reinforcement Learning
This paper proposes a robust control design method using reinforcement-learning for controlling partially-unknown dynamical systems under uncertain conditions. The method extends the optimal reinforcement-learning algorithm with a new learning technique that is based on the robust control theory. By learning from the data, the algorithm proposed actions that guarantees the stability of the closed loop system within the uncertainties estimated from the data. Control policies are calculated by solving a set of linear matrix inequalities. The controller was evaluated using simulations on a blood glucose model for patients with type-1 diabetes. Simulation results show that the proposed methodology is capable of safely regulates the blood glucose within a healthy level under the influence of measurement and process noises. The controller has also significantly reduced the post-meal fluctuation of the blood glucose. A comparison between the proposed algorithm and the existing optimal reinforcement learning algorithm shows the improved robustness of the closed loop system using our method.
Code (0)
등록된 구현이 없습니다.
Tasks
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
Predictive reinforcement learning based adaptive PID controller
Purpose: This study aims to address the challenges of controlling unstable and nonlinear systems by proposing an adaptive PID controller based on predictive reinforcement learning (PRL-PID), where the PRL-PID combines th…
reinforcement-learningReinforcement LearningAction-Driven Processes for Continuous-Time Control
At the heart of reinforcement learning are actions -- decisions made in response to observations of the environment. Actions are equally fundamental in the modeling of stochastic processes, as they trigger discontinuous …
Reinforcement LearningOnline Reinforcement Learning Control by Direct Heuristic Dynamic Programming: from Time-Driven to Event-Driven
In this paper time-driven learning refers to the machine learning method that updates parameters in a prediction model continuously as new data arrives. Among existing approximate dynamic programming (ADP) and reinforcem…
Reinforcement Learning (RL)Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, an…
Deep Reinforcement LearningGaussian ProcessesModel Predictive Controlreinforcement-learning+1PDE Control Gym: A Benchmark for Data-Driven Boundary Control of Partial Differential Equations
Over the last decade, data-driven methods have surged in popularity, emerging as valuable tools for control theory. As such, neural network approximations of control feedback laws, system dynamics, and even Lyapunov func…
reinforcement-learningReinforcement Learning