@article {354, title = {Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells}, year = {Submitted}, abstract = {

Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function, making them applicable to solving Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs). The recently introduced PINC framework extends PINNs to control applications, allowing for open-ended long-range prediction and control of dynamic systems. In this work, we enhance PINC for modeling highly nonlinear systems such as gas-lifted oil wells. By introducing skip connections in the PINC network and refining certain terms in the ODE, we achieve more accurate gradients during training, resulting in an effective modeling process for the oil well system. Our proposed improved PINC demonstrates superior performance, reducing the validation prediction error by an average of 67\% in the oil well application and significantly enhancing gradient flow through the network layers, increasing its magnitude by four orders of magnitude compared to the original PINC. Furthermore, experiments showcase the efficacy of Model Predictive Control (MPC) in regulating the bottom-hole pressure of the oil well using the improved PINC model, even in the presence of noisy measurements.

}, author = {Jonas Ekeland Kittelsen and Eric Aislan Antonelo and Eduardo Camponogara and Lars Struen Imsland} } @article {332, title = {Physics-Informed Neural Nets for Control of Dynamical Systems}, journal = {Neurocomputing}, volume = {579}, year = {2024}, chapter = {127419}, abstract = {

Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data. For systems represented by Ordinary Differential Equations (ODEs), the conventional PINN has a continuous time input variable and outputs the solution of the corresponding ODE. In their original form, PINNs do not allow control inputs neither can they simulate for long-range intervals without serious degradation in their predictions. In this context, this work presents a new framework called Physics-Informed Neural Nets for Control (PINC), which proposes a novel PINN-based architecture that is amenable to \emph{control} problems and able to simulate for longer-range time horizons that are not fixed beforehand. The framework has new inputs to account for the initial state of the system and the control action. In PINC, the response over the complete time horizon is split such that each smaller interval constitutes a solution of the ODE conditioned on the fixed values of initial state and control action for that interval. The whole response is formed by feeding back the predictions of the terminal state as the initial state for the next interval. This proposal enables the optimal control of dynamic systems, integrating a priori knowledge from experts and data collected from plants into control applications. We showcase our proposal in the control of two nonlinear dynamic systems: the Van der Pol oscillator and the four-tank system.

}, doi = {10.1016/j.neucom.2024.127419}, url = {https://doi.org/10.1016/j.neucom.2017.09.005}, author = {Eric A Antonelo and Eduardo Camponogara and Laio Oriel Seman and Eduardo Rehbein de Souza and Jean Panaioti Jordanou and Jomi Fred H{\"u}bner} } @proceedings {334, title = {Proximal Policy Optimization with Continuous Bounded Action Space via the Beta Distribution}, journal = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, year = {2021}, pages = {1-8}, abstract = {

Reinforcement learning methods for continuous control tasks have evolved in recent years generating a family of policy gradient methods that rely primarily on a Gaussian distribution for modeling a stochastic policy. However, the Gaussian distribution has an infinite support, whereas real world applications usually have a bounded action space. This dissonance causes an estimation bias that can be eliminated if the Beta distribution is used for the policy instead, as it presents a finite support. In this work, we investigate how this Beta policy performs when it is trained by the Proximal Policy Optimization (PPO) algorithm on two continuous control tasks from OpenAI gym. For both tasks, the Beta policy is superior to the Gaussian policy in terms of agent\&$\#$39;s final expected reward, also showing more stability and faster convergence of the training process. For the CarRacing environment with high-dimensional image input, the agent\&$\#$39;s success rate was improved by 63\% over the Gaussian policy.

}, doi = {10.1109/SSCI50451.2021.9660123}, url = {https://ieeexplore.ieee.org/abstract/document/9660123}, author = {Irving G. B. Petrazzini and Eric A Antonelo} } @conference {296, title = {Predicting Air Compressor Failures with Echo State Networks}, booktitle = {Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016}, year = {2016}, publisher = {PHM Society}, organization = {PHM Society}, abstract = {

Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to de- tect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment. A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One ap- proach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal\’s characteristics and work well on simulated data. However, the histogram model, de- spite being simpler, handles the deviations in real data better than the echo state network.

}, author = {Fan, Yuantao and Nowaczyk, S{\l}awomir and R{\"o}gnvaldsson, Thorsteinn and Antonelo, Eric A.} }