@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} } @mastersthesis {antonelo2006thesis, title = {A Neural Reinforcement Learning Approach for Behavior Acquisition in Intelligent Autonomous Systems}, volume = {Master of Science with a major in Computer Systems Engineering}, year = {2006}, school = {Halmstad University}, type = {masters}, abstract = {

In this work new artificial learning and innate control mechanisms are proposed for application in autonomous behavioral systems for mobile robots. An autonomous system (for mobile robots) existent in the literature is enhanced with respect to its capacity of exploring the environment and avoiding risky configurations (that lead to collisions with obstacles even after learning). The particular autonomous system is based on modular hierarchical neural networks. Initially,the autonomous system does not have any knowledge suitable for exploring the environment (and capture targets {\oe} foraging). After a period of learning,the system generates efficientobstacle avoid ance and target seeking behaviors. Two particular deficiencies of the forme rautonomous system (tendency to generate unsuitable cyclic trajectories and ineffectiveness in risky configurations) are discussed and the new learning and controltechniques (applied to the autonomous system) are verified through simulations. It is shown the effectiveness of the proposals: theautonomous system is able to detect unsuitable behaviors (cyclic trajectories) and decrease their probability of appearance in the future and the number of collisions in risky situations is significantly decreased. Experiments also consider maze environments (with targets distant from each other) and dynamic environments (with moving objects).

}, keywords = {reinforcement learning}, url = {http://hh.diva-portal.org/smash/record.jsf?pid=diva2\%3A237466\&dswid=7091}, author = {Eric A Antonelo} } @conference {294, title = {Neural structures for modeling of biological declarative memory applied to robot autonomous navigation systems}, booktitle = {4th Forum of Informatics and Technology of Maring{\'a} (FITEM) (in Portuguese)}, year = {2002}, abstract = {

, pp. (2002)

}, author = {Eric A Antonelo and Mauricio Figueiredo} }