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Adaptive Contraction-based control of Uncertain Nonlinear Processes using Neural Networks 13th

Adaptive Contraction-based Control of Uncertain Nonlinear Pr...

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13th IFAC Symposium on dynamics and control of Process Systems, including Biosystems (DYCOPS)

作者： Wei, Lai McCloy, Ryan Bao, Jie UNSW Sydney Sch Chem Engn Sydney NSW 2052 Australia

Driven by the flexible manufacturing trend in the process control industry and the uncertain nature of chemical process models, this article aims to achieve offset-free tracking for a family of uncertain nonlinear systems (e.g., using process models with parametric uncertainties) with adaptable performance. The proposed adaptive control approach incorporates into the control loop an adaptive neural network embedded contraction-based controller (to ensure convergence to time-varying references) and an online parameter identification module coupled with reference generation (to ensure modelled parameters converge those of the physical system). The integrated learning and control approach involves training a state and parameter dependent neural network to learn a contraction metric parameterized by the uncertain parameter and a differential feedback gain. This neural network is then embedded in an adaptive contraction-based control law which is updated by parameter estimates online. As uncertain parameter estimates converge to the corresponding physical values, offset-free tracking, simultaneously with improved convergence rates, can be achieved, resulting in a flexible, efficient and less conservative approach to the reference tracking control of uncertain nonlinear processes. An illustrative example is included to demonstrate the overall approach. Copyright (C) 2022 The Authors.

关键词： Artificial intelligence and machine learning dynamic modelling and simulation for control and operation Modeling and identification Process control

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Physics Constrained Learning in Neural Network based Modeling 13th

Physics Constrained Learning in Neural Network based Modelin...

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13th IFAC Symposium on dynamics and control of Process Systems, including Biosystems (DYCOPS)

作者： Patel, Rahul S. Bhartiya, Sharad Gudi, Ravindra D. Indian Inst Technol Dept Chem Engn Mumbai 400076 Maharashtra India

Neural Network (NN) models based on training solely using data are limited in their use due to issues related to extrapolability and interpretability. On the other hand, while mechanistic models based on governing physical laws can overcome these limitations, the unavailability of accurate mechanistic models render them unsuitable for critical applications. In this paper, we propose an approach to develop an NN model that is trained to exploit available data while also being regularized by physics based information;in other words the loss function of NN is augmented by constraints associated with the system physics. This approach, also known as PINNs (Raissi et al. 2019) has been applied to representative problems in process systems engineering (PSE) to evaluate its efficacy to represent the knowledge about the physics of the system while also exploiting the information in the data. It has been shown that in the presence of noisy data and partially known physics model, this approach can give better predictions compared to the conventional training methodology. It has also been shown that the constraints given by the physics based model are also satisfied to a greater extent as compared to models trained only on data. Copyright (C) 2022 The Authors.

关键词： Artificial intelligence and machine learning Modeling and identification dynamic modelling and simulation for control and operation

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