@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 {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} } @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} } @article {337, title = {Echo State Networks for Practical Nonlinear Model Predictive Control of Unknown Dynamic Systems}, journal = {IEEE Transactions on Neural Networks and Learning Systems}, year = {2021}, pages = {1-15}, abstract = {

Nonlinear model predictive control (NMPC) of industrial processes is changeling in part because the model of the plant may not be completely known but also for being computationally demanding. This work proposes an extremely efficient reservoir computing (RC)-based control framework that speeds up the NMPC of processes. In this framework, while an echo state network (ESN) serves as the dynamic RC-based system model of a process, the practical nonlinear model predictive controller (PNMPC) simplifies NMPC by splitting the forced and the free responses of the trained ESN, yielding the so-called ESN-PNMPC architecture. While the free response is generated by the forward simulation of the ESN model, the forced response is obtained by a fast and recursive calculation of the input-output sensitivities from the ESN. The efficiency not only results from the fast training inherited by RC but also from a computationally cheap control action given by the aforementioned novel recursive formulation and the computation in the reduced dimension space of input and output signals. The resulting architecture, equipped with a correction filter, is robust to unforeseen disturbances. The potential of the ESN-PNMPC is shown by application to the control of the four-tank system and an oil production platform, outperforming the predictive approach with a long-short term memory (LSTM) model, two standard linear control algorithms, and approximate predictive control.

}, keywords = {Echo State Networks, Model Predictive Control, reservoir computing}, doi = {10.1109/TNNLS.2021.3136357}, url = {https://ieeexplore.ieee.org/abstract/document/9664461}, author = {Jordanou, Jean Panaioti and Antonelo, Eric Aislan and Camponogara, Eduardo} } @proceedings {335, title = {Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments}, journal = {2021 IEEE Symposium Series on Computational Intelligence (SSCI)}, year = {2021}, pages = {1-7}, abstract = {

Autonomous driving is a complex task, which has been tackled since the first self-driving car ALVINN in 1989, with a supervised learning approach, or behavioral cloning (BC). In BC, a neural network is trained with state-action pairs that constitute the training set made by an expert, i.e., a human driver. However, this type of imitation learning does not take into account the temporal dependencies that might exist between actions taken in different moments of a navigation trajectory. These type of tasks are better handled by reinforcement learning (RL) algorithms, which need to define a reward function. On the other hand, more recent approaches to imitation learning, such as Generative Adversarial Imitation Learning (GAIL), can train policies without explicitly requiring to define a reward function, allowing an agent to learn by trial and error directly on a training set of expert trajectories. In this work, we propose two variations of GAIL for autonomous navigation of a vehicle in the realistic CARLA simulation environment for urban scenarios. Both of them use the same network architecture, which process high-dimensional image input from three frontal cameras, and other nine continuous inputs representing the velocity, the next point from the sparse trajectory and a high-level driving command. We show that both of them are capable of imitating the expert trajectory from start to end after training ends, but the GAIL loss function that is augmented with BC outperforms the former in terms of convergence time and training stability.

}, doi = {10.1109/SSCI50451.2021.9660156}, url = {https://arxiv.org/abs/2110.08586}, author = {Gustavo Claudio Karl Couto and Eric A Antonelo} } @article {309, title = {Online Learning Control with Echo State Networks of an Oil Production Platform}, journal = {Engineering Applications of Artificial Intelligence}, volume = {85}, year = {2019}, pages = {214-228}, abstract = {

The design of a control algorithm is exceptionally hard when models are unavailable, the physics are varying in time, or structural uncertainties are involved. One such case is an oil production platform in which reservoir conditions and the composition of the multiphase flow are not known precisely. Today, with streams of data being generated from sensors, black-box adaptive control emerged as an alternative to the control of such systems. In this work, an online adaptive controller based on Echo State Networks (ESNs) is employed in diverse scenarios of controlling an oil production platform. The ESN learns an inverse model of the plant from which a control law is derived to attain set-point tracking of a simulated model. The analysis considers high steady-state gains, potentially unstable conditions, and a multi-variate control structure. All in all, this work contributes to the literature by demonstrating that online-learning control can be effective in highly complex dynamic systems (oil production platforms) devoid of suitable models, and with multiple inputs and outputs.

}, doi = {10.1016/j.engappai.2019.06.011}, url = {https://www.sciencedirect.com/science/article/pii/S0952197619301502?dgcid=author}, author = {Jean P. Jordanou and Eric A. Antonelo and Eduardo Camponogara} } @article {304, title = {Nonlinear Model Predictive Control of an Oil Well with Echo State Networks}, journal = {IFAC-PapersOnLine (3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production OOGP 2018)}, volume = {51}, year = {2018}, pages = {13 - 18}, abstract = {

In oil production platforms, processes are nonlinear and prone to modeling errors, as the flowregime and components are not entirely known and can bring about structural uncertainties,making designing predictive control algorithms for this type of system a challenge. In thiswork, an efficient data-driven framework for Model Predictive Control (MPC) using Echo StateNetworks (ESN) as prediction model is proposed. Differently from previous work, the ESN model for MPC is only linearized partially: while the free response of the system is kept fullynonlinear, only the forced response is linearized. This MPC framework is known in the literatureas the Practical Nonlinear Model Predictive Controller (PNMPC). In this work, by using theanalytically computed gradient from the ESN model, no finite difference method to compute derivatives is needed as in PNMPC. The proposed method, called PNMPC-ESN, is applied tocontrol a simplified model of a gas lifted oil well, managing to successfully control the plant,obeying the established constraints while maintaining setpoint tracking.

}, keywords = {Echo State Networks, Gas, Model Predictive Control, Oil, System Identification}, issn = {2405-8963}, doi = {https://doi.org/10.1016/j.ifacol.2018.06.348}, url = {http://www.sciencedirect.com/science/article/pii/S2405896318306785}, author = {Jean Panaioti Jordanou and Eduardo Camponogara and Eric A. Antonelo and Marco Aurelio S. de Aguiar} } @article {Antonelo2016, title = {Echo State Networks for Data-driven Downhole Pressure Estimation in Gas-lift Oil Wells}, journal = {Neural Networks}, volume = {85}, year = {2017}, note = {(in press)}, pages = {106-117}, abstract = {

Process measurements are of vital importance for monitoring and control of industrial plants. When we consider offshore oil production platforms, wells that require gas-lift technology to yield oil production from low pressure oil reservoirs can become unstable under some conditions. This undesir- able phenomenon is usually called slugging flow, and can be identified by an oscillatory behavior of the downhole pressure measurement. Given the importance of this measurement and the unreliability of the related sensor, this work aims at designing data-driven soft-sensors for downhole pressure estimation in two contexts: one for speeding up first-principle model sim- ulation of a vertical riser model; and another for estimating the downhole pressure using real-world data from an oil well from Petrobras based only on topside platform measurements. Both tasks are tackled by employing Echo State Networks (ESN) as an efficient technique for training Recurrent Neu- ral Networks. We show that a single ESN is capable of robustly modeling both the slugging flow behavior and a steady state based only on a square wave input signal representing the production choke opening in the vertical riser. Besides, we compare the performance of a standard network to the performance of a multiple timescale hierarchical architecture in the second task and show that the latter architecture performs better in modeling both large irregular transients and more commonly occurring small oscillations.

}, doi = {10.1016/j.neunet.2016.09.009}, url = {http://authors.elsevier.com/sd/article/S0893608016301393}, author = {Eric A Antonelo and Eduardo Camponogara and Bjarne Foss} } @proceedings {302, title = {Recurrent Neural Network based control of an Oil Well}, journal = {XIII Brazilian Symposium on Intelligent Automation (SBAI)}, year = {2017}, pages = {924-931}, abstract = {

Echo State Networks (ESN) are dynamical learning models composed of two parts: a recurrent network (reservoir) with fixed weights and a linear adaptive readout output layer. The output layer\’s weights are learned for the ESN to reproduce temporal patterns usually by solving a least-squares problem. Such recurrent networks have shown promising results in previous applications to dynamic system identification and closed-loop control. This work applies an echo state network to control the bottom hole pressure of an oil well, whereby the opening of the production choke is manipulated. The controller utilizes a network to learn the plant inverse model, whose model input is the plant output and the vice-versa, and another network to compute the control action that induces a desired plant behavior. Despite the nonlinearities of the well model, the ESN effectively learned the inverse model and achieved near global setpoint tracking and disturbance rejection, with little setpoint deviation in the latter case. These results show that echo state networks are a viable tool for the control of complex dynamic systems by means of online inverse-model learning.

}, url = {https://www.ufrgs.br/sbai17/papers/paper_269.pdf}, author = {Jean Panaioti Jordanou and Eric A Antonelo and Eduardo Camponogara and Marco Aurelio S. de Aguiar} } @proceedings {Antonelo2015a, title = {An Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well}, journal = {16th International Conference on Engineering Applications of Neural Networks}, volume = {Communications in Computer and Information Science, vol 517}, year = {2015}, pages = {379-389}, publisher = {Springer}, abstract = {

Soft sensor technology has been increasingly used in indus- try. Its importance is magnified when the process variable to be estimated is key to control and monitoring processes and the respective sensor ei- ther has a high probability of failure or is unreliable due to harsh environ- ment conditions. This is the case for permanent downhole gauge (PDG) sensors in the oil and gas industry, which measure pressure and tempera- ture in deepwater oil wells. In this paper, historical data obtained from an actual offshore oil well is used to build a black box model that estimates the PDG downhole pressure from platform variables, using Echo State Networks (ESNs), which are a class of recurrent networks with power- ful modeling capabilities. These networks, differently from other neural networks models used by most soft sensors in literature, can model the nonlinear dynamical properties present in the noisy real-world data by using a two-layer structure with efficient training: a recurrent nonlinear layer with fixed randomly generated weights and a linear adaptive read- out output layer. Experimental results show that ESNs are a promising technique to model soft sensors in an industrial setting.

}, isbn = {978-3-319-23981-1}, doi = {https://doi.org/10.1007/978-3-319-23983-5_35}, url = {https://link.springer.com/chapter/10.1007/978-3-319-23983-5_35}, author = {Eric A Antonelo and Eduardo Camponogara} } @article {Antonelo2015, title = {System Identification of a Vertical Riser Model with Echo State Networks}, journal = {IFAC-PapersOnLine (2nd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production)}, volume = {48}, year = {2015}, pages = {304-310}, abstract = {

System identification of highly nonlinear dynamical systems, important for reducing time complexity in long simulations, is not trivial using more traditional methods such as recurrent neural networks (RNNs) trained with back-propagation through time. The recently introduced Reservoir Computing (RC)\∗ approach to training RNNs is a viable and powerful alternative which renders fast training and high performance. In this work, a single Echo State Network (ESN), a flavor of RC, is employed for system identification of a vertical riser model which has stationary and oscillatory signal behaviors depending of the production choke opening input variable. It is shown experimentally that these different behaviors are learned by constraining the high-dimensional reservoir states to attractor subspaces in which the specific behavior is represented. Further experiments show the stability of the identified system.

}, issn = {2405-8963}, doi = {10.1016/j.ifacol.2015.08.048}, url = {http://www.sciencedirect.com/science/article/pii/S2405896315009155}, author = {Eric A Antonelo and Eduardo Camponogara and Agustinho Plucenio} } @proceedings {292, title = {Evolutionary fuzzy system for architecture control in a constructive neural network}, journal = {6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {541-546}, publisher = {IEEE}, abstract = {

This work describes an evolutionary system to control the growth of a constructive neural network for autonomous navigation. A classifier system generates Takagi- Sugeno fuzzy rules and controls the architecture of a constructive neural network. The performance of the mobile robot guides the evolutionary learning mechanism. Experiments show the efficiency of the classifier fuzzy system for analyzing if it is worth inserting a new neuron into the architecture.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554333}, url = {https://ieeexplore.ieee.org/abstract/document/1554333/}, author = {Rodrigo Calvo and Eric A Antonelo and Mauricio Figueiredo} } @proceedings {antonelo2005, title = {Intelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination}, journal = {Proceedings of the 6th IEEE International Symposium on Computational Intelligence in Robotics and Automation}, year = {2005}, pages = {553-557}, publisher = {IEEE}, address = {Helsinki, Finland}, abstract = {

An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.

}, issn = {0-7803-9355-4}, doi = {10.1109/CIRA.2005.1554335}, url = {https://ieeexplore.ieee.org/document/1554335/}, author = {Eric A Antonelo and Mauricio Figueiredo and Albert-Jan Baerlvedt and Rodrigo Calvo} }