From multi‐physics models to neural network for predictive control synthesis

Blaud, Pierre Clément; Chevrel, Philippe; Claveau, Fabien; Haurant, Pierrick; Mouraud, Anthony

doi:10.1002/oca.2845

Cited by 6 publications

(21 citation statements)

References 63 publications

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“…One challenge is to find a suitable g nn ; it could be a linear physical model with state space representation [34], a non-linear one [35] or a grey-box model identified from data [36].One challenge is to find a suitable g nn ; it could be a linear physical model with state space representation [34] or a non-linear one [35]. PB The method explored in this work uses a black-box model following a methodology extracted from [37]. The main steps are summarized in Fig.…”

Section: Second Level: Dynamic Optimization Via Multi Energy-mpcmentioning

confidence: 99%

Multi-flow optimization of a greenhouse system: A hierarchical control approach

Blaud,

Haurant,

Chevrel

et al. 2023

Applied Energy

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Section: Second Level: Dynamic Optimization Via Multi Energy-mpcmentioning

confidence: 99%

Multi-flow optimization of a greenhouse system: A hierarchical control approach

Blaud,

Haurant,

Chevrel

et al. 2023

Applied Energy

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“…Unlike physics-based approaches, black box models do not require expert knowledge and model adaptation based on sensors configuration. Instead, they rely on collected data to identify relationships between inputs and outputs (see [1]), making them well suited to applications where the system's physics is not always well understood or is expensive to understand.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Linear-Nonlinear ARX Model for Reliable Multi-Step Prediction : Application to SwPool Benchmark

Gauthier-Clerc,

Le Capitaine,

Claveau

et al. 2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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We present a hybrid ARX model that is useful for system identification of nonlinear models. Our motivation is to combine the advantages of linear and nonlinear models in the context of extrapolation outside of the training dataset. The proposed method uses a residual hybridization approach to ensure a large linear contribution. Based on this hybrid ARX model, the proposed learning method is evaluated using the available operating data of a specific aquatic center. The results obtained on this benchmark are compared to those of traditional linear and nonlinear identification, showing that the hybrid approach achieves both the accuracy of the pure nonlinear model and the consistency of the linear ARX model. Our approach provides a promising solution for nonlinear identification, particularly for dynamical systems partially explainable by a linear model. As in the strictly linear case, the proposed model can be learned from a small volume of data, but can be enriched to improve prediction accuracy. Its potential use for data-based predictive control is particularly useful.

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“…An optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine is considered, and an intelligent real‐time optimal control method based on deep neural networks is developed for the online tracking of the flow front position to improve the efficient production process of the plastics 20 . An efficient and systematic method is proposed for model‐based predictive control synthesis 21 . The decentralized control issues of nonlinear large‐scale systems are investigated via critic‐only adaptive dynamic programming learning methods 22 .…”

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confidence: 99%

“…18 The problem of the post-stall pitching maneuver of an aircraft with lower deflection frequency of control actuator is studied by considering the unsteady aerodynamic disturbances. 19 The fourth group of papers [20][21][22][23] focuses on neural networks and deep neural networks learning methods for optimal control. An optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine is considered, and an intelligent real-time optimal control method based on deep neural networks is developed for the online tracking of the flow front position to improve the efficient production process of the plastics.…”

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confidence: 99%

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Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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From multi‐physics models to neural network for predictive control synthesis

Cited by 6 publications

References 63 publications

Multi-flow optimization of a greenhouse system: A hierarchical control approach

Multi-flow optimization of a greenhouse system: A hierarchical control approach

A Hybrid Linear-Nonlinear ARX Model for Reliable Multi-Step Prediction : Application to SwPool Benchmark

Data‐based learning control for optimization of nonlinear systems

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