A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems

Faria, R.R.; Capron, B.D.O.; Secchi, A.R.; De Souza, M.B.

doi:10.1016/j.engappai.2023.107256

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…28 It utilizes a selfparametrized policy, or actor, to learn from interactions between the agent and the environment, guided by rewards. The objective is to learn a policy that maximizes the sum of rewards generated through a stochastic sequential decisionmaking process known as the Markov decision process, 32,33 as shown in Figure 4.…”

Section: Problem Statementmentioning

confidence: 99%

Gas-Lift Optimization Using Physics-Informed Deep Reinforcement Learning

de Rezende Faria,

Capron,

Secchi

et al. 2024

Ind. Eng. Chem. Res.

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Real-time optimization (RTO) methodologies have become essential for optimal process operation in the oil and gas industries. Typically, RTO is based on a steady-state model (steady-state real-time optimization�SSRTO) and operates as a closed-loop optimizer. However, this technique can result in suboptimal policies due to steady-state waiting and plant-model mismatch issues. To alleviate these problems, we combine this closed-loop optimizer with a data-driven residual optimizer based on deep reinforcement learning (DRL). The idea is to use the SSRTO solution's stability and convergence properties while reducing plant-model mismatch through a residual DRL controller that can tune the optimal inputs calculated by the SSRTO based on transient measurements (i.e., real data). Our proposed methodology delivered robust online control policies compared to dynamic real-time optimization (DRTO) for closed-loop control, even when the plant-model mismatch was simulated by introducing parameter uncertainty and noise in the plant and using an SSRTO model with structural uncertainty. As a result, its economic gain was improved compared to the SSRTO solution, with an average computational cost 11 times lower than DRTO, making it suitable for online applications.

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Section: Problem Statementmentioning

confidence: 99%

Gas-Lift Optimization Using Physics-Informed Deep Reinforcement Learning

de Rezende Faria,

Capron,

Secchi

et al. 2024

Ind. Eng. Chem. Res.

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Physics‐informed reinforcement learning for optimal control of nonlinear systems

Wang,

2024

AIChE Journal

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This article proposes a model‐free framework to solve the optimal control problem with an infinite‐horizon performance function for nonlinear systems with input constraints. Specifically, two Physics‐Informed Neural Networks (PINNs) that incorporate the knowledge of the Lyapunov stability theorem and the convergence conditions of the policy iteration algorithm are utilized to approximate the value function and control policy, respectively. Then, a Reinforcement Learning (RL) algorithm that does not require any first‐principles or data‐driven models of nonlinear systems is developed to iteratively learn a nearly optimal control policy. Furthermore, we provide a rigorous theoretical analysis showing the conditions that ensure the stability of closed‐loop systems with the control policy learned by RL and guarantee the convergence of the iteration algorithm. Finally, the proposed Physics‐Informed Reinforcement Learning (PIRL) method is applied to a chemical process example to demonstrate its effectiveness.

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Research on safety verification methods of static data of train control systems based on deep association rules

Wang,

2024

J Supercomput

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A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems

Cited by 7 publications

References 40 publications

Gas-Lift Optimization Using Physics-Informed Deep Reinforcement Learning

Gas-Lift Optimization Using Physics-Informed Deep Reinforcement Learning

Physics‐informed reinforcement learning for optimal control of nonlinear systems

Research on safety verification methods of static data of train control systems based on deep association rules

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