@article {345, title = {Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments}, journal = {arXiv preprint arXiv:2302.04823}, year = {Submitted}, abstract = {

Deriving robust control policies for realistic urban navigation scenarios is not a trivial task. In an end-to-end approach, these policies must map high-dimensional images from the vehicle\&$\#$39;s cameras to low-level actions such as steering and throttle. While pure Reinforcement Learning (RL) approaches are based exclusively on rewards,Generative Adversarial Imitation Learning (GAIL) agents learn from expert demonstrations while interacting with the environment, which favors GAIL on tasks for which a reward signal is difficult to derive. In this work, the hGAIL architecture was proposed to solve the autonomous navigation of a vehicle in an end-to-end approach, mapping sensory perceptions directly to low-level actions, while simultaneously learning mid-level input representations of the agent\&$\#$39;s environment. The proposed hGAIL consists of an hierarchical Adversarial Imitation Learning architecture composed of two main modules: the GAN (Generative Adversarial Nets) which generates the Bird\&$\#$39;s-Eye View (BEV) representation mainly from the images of three frontal cameras of the vehicle, and the GAIL which learns to control the vehicle based mainly on the BEV predictions from the GAN as input.Our experiments have shown that GAIL exclusively from cameras (without BEV) fails to even learn the task, while hGAIL, after training, was able to autonomously navigate successfully in all intersections of the city.

}, doi = {10.48550/arXiv.2302.04823}, author = {Gustavo Claudio Karl Couto and Eric Aislan Antonelo} } @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 {352, title = {Investigation of Proper Orthogonal Decomposition for Echo State Networks}, journal = {Neurocomputing}, year = {2023}, pages = {126395}, abstract = {

Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped with a very efficient training procedure, Reservoir Computing strategies, such as the ESN, require high-order networks, i.e., many neurons, resulting in a large number of states that are magnitudes higher than the number of model inputs and outputs. A large number of states not only makes the time-step computation more costly but also may pose robustness issues, especially when applying ESNs to problems such as Model Predictive Control (MPC) and other optimal control problems. One way to circumvent this complexity issue is through Model Order Reduction strategies such as the Proper Orthogonal Decomposition (POD) and its variants (POD-DEIM), whereby we find an equivalent lower order representation to an already trained high dimension ESN. To this end, this work aims to investigate and analyze the performance of POD methods in Echo State Networks, evaluating their effectiveness through the Memory Capacity (MC) of the POD-reduced network compared to the original (full-order) ESN. We also perform experiments on two numerical case studies: a NARMA10 difference equation and an oil platform containing two wells and one riser. The results show that there is little loss of performance comparing the original ESN to a POD-reduced counterpart and that the performance of a POD-reduced ESN tends to be superior to a normal ESN of the same size. Also, the POD-reduced network achieves speedups of around 80\% compared to the original ESN.

}, keywords = {Echo State Networks., Model Order Reduction, reservoir computing}, issn = {0925-2312}, doi = {https://doi.org/10.1016/j.neucom.2023.126395}, url = {https://www.sciencedirect.com/science/article/pii/S0925231223005180}, author = {Jean Panaioti Jordanou and Eric Aislan Antonelo and Eduardo Camponogara and Eduardo Gildin} } @article {338, title = {Nonlinear Model Predictive Control of Electrical Submersible Pumps based on Echo State Networks }, journal = {Advanced Engineering Informatics}, volume = {52}, year = {2022}, month = {04/2022}, abstract = {

Employed for artificial lifting in oil well production, Electrical Submersible Pumps (ESP) can be operated with Model Predictive Control (MPC) to drive an optimal production, while ensuring a safe operation and respecting system constraints.
\ Due to the nonlinear dynamics of ESPs, Echo State Networks (ESNs), a recurrent neural network with fast training, are employed for efficient system identification of unknown dynamic systems.
Besides the synthesis of highly accurate prediction models, this work contributes by designing two Nonlinear MPC (NMPC) strategies for the control of an ESP-lifted oil well:\  a standard Single-Shooting NMPC that embeds the ESN model completely, and the Practical Nonlinear Model Predictive Controller (PNMPC) that\  approximates the NMPC through fast trajectory-linearization of the ESN model.
Another contribution is the implementation of an error correction filter to reject disturbances and counter modeling errors in both NMPC strategies.
Finally, in computational experiments, both ESN-based NMPC strategies performed well in controlling simulated ESP-lifted oil wells when the model of the plant is unknown. However, PNMPC was more efficient and induced a similar performance to standard NMPC.

}, issn = {1474-0346}, doi = {10.1016/j.aei.2022.101553}, author = {Jean Panaioti Jordanou and Iver Osnes and Sondre B. Hernes and Eduardo Camponogara and Eric Aislan Antonelo and Lars Imsland} }