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See detailNonlinear Model Predictive Control of an Oil Well with Echo State Networks
Jordanou, Jean Panaioti; Camponogara, Eduardo; Antonelo, Eric Aislan UL et al

in IFAC-PapersOnLine (2018), 51

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 ... [more ▼]

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. [less ▲]

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See detailRecurrent Neural Network based control of an Oil Well
Jordanou, Jean Panaioti; Antonelo, Eric Aislan UL; Camponogara, Eduardo et al

in Brazilian Symposium on Intelligent Automation, Porto Alegre 1-4 October 2017 (2017, October)

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 ... [more ▼]

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. [less ▲]

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See detailRecurrent Dynamical Projection for Time series-based Fraud detection
Antonelo, Eric Aislan UL; State, Radu UL

in ICANN 2017, Part II, LNCS 10614 (2017)

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See detailEcho State Networks for Data-driven Downhole Pressure Estimation in Gas-lift Oil Wells
Antonelo, Eric Aislan UL; Camponogara, Eduardo; Foss, Bjarne

in Neural Networks (2017), 85

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See detailReservoir Computing for Detection of Steady State in Performance Tests of Compressors
Antonelo, Eric Aislan UL; Flesch, Carlos; Filipe, Schmitz

in Neurocomputing (2017)

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See detailPredicting Air Compressor Failures with Echo State Networks
Fan, Yuantao; Nowaczyk, S. Lawomir; Rögnvaldsson, Thorsteinn et al

in Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016 (2016)

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See detailSystem Identification of a Vertical Riser Model with Echo State Networks
Antonelo, Eric Aislan UL; Camponogara, Eduardo; Plucenio, Agustinho

in IFAC-PapersOnLine (2015), 48(6), 304-310

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 ... [more ▼]

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)∗∗The term reservoir used here is not related to reservoirs in oil and gas industry. 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. [less ▲]

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See detailAn Echo State Network-based Soft Sensor of Downhole Pressure for a Gas-lift Oil Well
Antonelo, Eric Aislan UL; Camponogara, Eduardo

in Iliadis, Lazaros; Jayne, Chrisina (Eds.) Engineering Applications of Neural Networks (2015)

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 ... [more ▼]

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. [less ▲]

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See detailOn Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures
Antonelo, Eric Aislan UL; Schrauwen, B.

in IEEE Transactions on Neural Networks and Learning Systems (2015), 26(4), 763-780

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See detailLearning Slow Features with Reservoir Computing for Biologically-inspired Robot Localization
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in Neural Networks (2012), 25(1), 178-190

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See detailReservoir Computing Architectures for Modeling Robot Navigation Systems
Antonelo, Eric Aislan UL

Doctoral thesis (2011)

This thesis proposes a new efficient and biologically inspired way of modeling navigation tasks for autonomous mobile robots having restrictions on cost, energy consumption, and computational complexity ... [more ▼]

This thesis proposes a new efficient and biologically inspired way of modeling navigation tasks for autonomous mobile robots having restrictions on cost, energy consumption, and computational complexity (such as household and assistant robots). It is based on the recently proposed Reservoir Computing approach for training Recurrent Neural Networks. Robot Navigation Systems Autonomous mobile robots must be able to safely and purposefully navigate in complex dynamic environments, preferentially considering a restricted amount of computational power as well as limited energy consumption. In order to turn these robots into commercially viable domestic products with intelligent, abstract computational capabilities, it is also necessary to use inexpensive sensory apparatus such as a few infra-red distance sensors of limited accuracy. Current state-of-the-art methods for robot localization and navigation require fully equipped robotic platforms usually possessing expensive laser scanners for environment mapping, a considerable amount of computational power, and extensive explicit modeling of the environment and of the task. This thesis The research presented in this thesis is a step towards creating intelligent autonomous mobile robots with abstract reasoning capabilities using a limited number of very simple raw noisy sensory signals, such as distance sensors. The basic assumption is that the low-dimensional sensory signal can be projected into a high-dimensional dynamic space where learning and computation is performed by linear methods (such as linear regression), overcoming sensor aliasing problems commonly found in robot navigation tasks. This form of computation is known in the literature as Reservoir Computing (RC), and the Echo State Network is a particular RC model used in this work and characterized by having the high-dimensional space implemented by a discrete analog recurrent neural network with fading memory properties. This thesis proposes a number of Reservoir Computing architectures which can be used in a variety of autonomous navigation tasks, by modeling implicit abstract representations of an environment as well as navigation behaviors which can be sequentially executed in the physical environment or simulated as a plan in deliberative goal-directed tasks. Navigation attractors A navigation attractor is a reactive robot behavior defined by a temporal pattern of sensory-motor coupling through the environment space. Under this scheme, a robot tends to follow a trajectory with attractor-like characteristics in space. These navigation attractors are characterized by being robust to noise and unpredictable events and by having inherent collision avoidance skills. In this work, it is shown that an RC network can model not only one behavior, but multiple navigation behaviors by shifting the operating point of the dynamical reservoir system into different \emph{sub-space attractors} using additional external inputs representing the selected behavior. The sub-space attractors emerge from the coupling existing between the RC network, which controls the autonomous robot, and the environment. All this is achieved under an imitation learning framework which trains the RC network using examples of navigation behaviors generated by a supervisor controller or a human. Implicit spatial representations From the stream of sensory input given by distance sensors, it is possible to construct implicit spatial representations of an environment by using Reservoir Computing networks. These networks are trained in a supervised way to predict locations at different levels of abstraction, from continuous-valued robot's pose in the global coordinate's frame, to more abstract locations such as small delimited areas and rooms of a robot environment. The high-dimensional reservoir projects the sensory input into a dynamic system space, whose characteristic fading memory disambiguates the sensory space, solving the sensor aliasing problems where multiple different locations generate similar sensory readings from the robot's perspective. Hierarchical networks for goal-directed navigation It is possible to model navigation attractors and implicit spatial representations with the same type of RC network. By constructing an hierarchical RC architecture which combines the aforementioned modeling skills in two different reservoir modules operating at different timescales, it is possible to achieve complex context-dependent sensory-motor coupling in unknown environments. The general idea is that the network trained to predict the location and orientation of the robot in this architecture can be used to select appropriate navigation attractors according to the current context, by shifting the operating point of the navigation reservoir to a sub-space attractor. As the robot navigates from one room to the next, a corresponding context switch selects a new reactive navigation behavior. This continuous sequence of context switches and reactive behaviors, when combined with an external input indicating the destination room, leads ultimately to a goal-directed navigation system, purely trained in a supervised way with examples of sensory-motor coupling. Generative modeling of environment-robot dynamics RC networks trained to predict the position of the robot from the sensory signals learns forward models of the robot. By using a generative RC network which predicts not only locations but also sensory nodes, it is possible to use the network in the opposite direction for predicting local environmental sensory perceptions from the robot position as input, thus learning an inverse model. The implicit map learned by forward models can be made explicit, by running the RC network in reverse: predict the local sensory signals given the location of the robot as input (inverse model). which are fed back to the reservoir, it is possible to internally predict future scenarios and behaviors without actually experiencing them in the current environment (a process analogous to dreaming), constituting a planning-like capability which opens new possibilities for deliberative navigation systems. Unsupervised learning of spatial representations In order to achieve a higher degree of autonomy in the learning process of RC-based navigation systems which use implicit learned models of the environment for goal-directed navigation, a new architecture is proposed. Instead of using linear regression, an unsupervised learning method which extracts slowly-varying output signals from the reservoir states, called Slow Feature Analysis, is used to generate self-organized spatial representations at the output layer, without the requirement of labeling training data with the desired locations. It is shown experimentally that the proposed RC-SFA architecture is empowered with an unique combination of short-term memory and non-linear transformations which overcomes the hidden state problem present in robot navigation tasks. In addition, experiments with simulated and real robots indicate that spatial activations generated by the trained network show similarities to the activations of CA1 hippocampal cells of rats (a specific group of neurons in the hippocampus). [less ▲]

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See detailLearning navigation attractors for mobile robots with reinforcement learning and reservoir computing
Antonelo, Eric Aislan UL; Depeweg, Stefan; Schrauwen, Benjamin

in Proceedings of the X Brazilian Congress on Computational Intelligence (CBIC) (2011)

Autonomous robot navigation in partially observable environments is a complex task because the state of the environment can not be completely determined only by the current sensory readings of a robot ... [more ▼]

Autonomous robot navigation in partially observable environments is a complex task because the state of the environment can not be completely determined only by the current sensory readings of a robot. This work uses the recently introduced paradigm for training recurrent neural networks (RNNs), called reservoir computing (RC), to model multiple navigation attractors in partially observable environments. In RC, the RNN with randomly generated fixed weights, called reservoir, projects the input into a high-dimensional dynamic space. Only the readout output layer is trained using standard linear regression techniques, and in this work, is used to approximate the state-action value function. By using a policy iteration framework, where an alternating sequence of policy improvement (samples generation from environment interaction) and policy evaluation (network training) steps are performed, the system is able to shape navigation attractors so that, after convergence, the robot follows the correct trajectory towards the goal. The experiments are accomplished using an e-puck robot extended with 8 distance sensors in a rectangular environment with an obstacle between the robot and the target region. The task is to reach the goal through the correct side of the environment, which is indicated by a temporary stimulus previously observed at the beginning of the episode. We show that the reservoir-based system (with short-term memory) can model these navigation attractors, whereas a feedforward network without memory fails to do so. [less ▲]

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See detailSupervised Learning of Internal Models for Autonomous Goal-oriented Robot Navigation using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in 2010 IEEE International Conference on Robotics and Automation (2010)

In this work we propose a hierarchical architec- ture which constructs internal models of a robot environment for goal-oriented navigation by an imitation learning process. The proposed architecture is ... [more ▼]

In this work we propose a hierarchical architec- ture which constructs internal models of a robot environment for goal-oriented navigation by an imitation learning process. The proposed architecture is based on the Reservoir Computing paradigm for training Recurrent Neural Networks (RNN). It is composed of two randomly generated RNNs (called reservoirs), one for modeling the localization capability and one for learning the navigation skill. The localization module is trained to detect the current and previously visited robot rooms based only on 8 noisy infra-red distance sensors. These predictions together with distance sensors and the desired goal location are used by the navigation network to actually steer the robot through the environment in a goal-oriented manner. The training of this architecture is performed in a supervised way (with examples of trajectories created by a supervisor) using linear regression on the reservoir states. So, the reservoir acts as a temporal kernel projecting the inputs to a rich feature space, whose states are linearly combined to generate the desired outputs. Experimental results on a simulated robot show that the trained system can localize itself within both simple and large unknown environments and navigate successfully to desired goals. [less ▲]

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See detailModular reservoir computing networks for imitation learning of multiple robot behaviors
Waegeman, Tim; Antonelo, Eric Aislan UL; wyffels, Francis et al

in Proc. of the 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA) (2009)

Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service ... [more ▼]

Autonomous mobile robots must accomplish tasks in unknown and noisy environments. In this context, learning robot behaviors in an imitation based approach would be desirable in the perspective of service robotics as well as of learning robots. In this work, we use Reservoir Computing (RC) for learning robot behaviors by demonstration. In RC, a randomly generated recurrent neural network, the reservoir, projects the input to a dynamic temporal space. The reservoir states are mapped into a readout output layer which is the solely part being trained using standard linear regression. In this paper, we use a two layered modular structure, where the first layer comprises two RC networks, each one for learning primitive behaviors, namely, obstacle avoidance and target seeking. The second layer is composed of one RC network for behavior combination and coordination. The hierarchical RC network learns by examples given by simple controllers which implement the primitive behaviors. We use a simulation model of the e-puck robot which has distance sensors and a camera that serves as input for our system. The experiments show that, after training, the robot learns to coordinate the Goal Seeking (GS) and the Object Avoidance (OA) behaviors in unknown environments, being able to capture targets and navigate efficiently. [less ▲]

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See detailUnsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Alippi, Cesare; Polycarpou, Marios; Panayiotou, Christos (Eds.) et al Artificial Neural Networks -- ICANN 2009 (2009)

Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode ... [more ▼]

Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode locations of the animal's environment). This work seeks to model these place cells by employing three (biologically plausible) techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The proposed architecture is composed of three layers, where the bottom layer is a dynamic reservoir of recurrent nodes with fixed weights. The upper layers (SFA and ICA) provides a self-organized formation of place cells, learned in an unsupervised way. Experiments show that a simulated mobile robot with 17 noisy short-range distance sensors is able to self-localize in its environment with the proposed architecture, forming a spatial representation which is dependent on the robot direction. [less ▲]

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See detailTowards Autonomous Self-localization of Small Mobile Robots using Reservoir Computing and Slow Feature Analysis
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2009)

Biological systems such as rats have special brain structures which process spatial information from the environment. They have efficient and robust localization abilities provided by special neurons in ... [more ▼]

Biological systems such as rats have special brain structures which process spatial information from the environment. They have efficient and robust localization abilities provided by special neurons in the hippocampus, namely place cells. This work proposes a biologically plausible architecture which is based on three recently developed techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The bottom layer of our RC-SFA architecture is a reservoir of recurrent nodes which process the information from the robot's distance sensors. It provides a temporal kernel of rich dynamics which is used by the upper two layers (SFA and ICA) to autonomously learn place cells. Experiments with an e-puck robot with 8 infra-red sensors (which measure distances in [4-30] cm) show that the learning system based on RC-SFA provides a self-organized formation of place cells that can either distinguish between two rooms or to detect the corridor connecting them. [less ▲]

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See detailOn Different Learning Approaches with Echo State Networks for Localization of Small Mobile Robots
Antonelo, Eric Aislan UL; Schrauwen, Benjamin

in Proceedings of the IX Brazilian Conference on Neural Networks (2009)

Animals such as rats have innate and robust localization capabilities which allow them to navigate to goals in a maze. The rodent’s hippocampus, with the so called place cells, is responsible for such ... [more ▼]

Animals such as rats have innate and robust localization capabilities which allow them to navigate to goals in a maze. The rodent’s hippocampus, with the so called place cells, is responsible for such spatial processing. This work seeks to model these place cells using either supervised or unsupervised learning techniques. More specifically, we use a randomly generated recurrent neural network (the reservoir) as a non-linear temporal kernel to expand the input to a rich dynamic space. The reservoir states are linearly combined (using linear regression) or, in the unsupervised case, are used for extracting slowly-varying features from the input to form place cells (the architectures are organized in hierarchical layers). Experiments show that a small mobile robot with cheap and low-range distance sensors can learn to self-localize in its environment with the proposed systems. [less ▲]

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See detailImitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the 10th Brazilian Symposium on Neural Networks (SBRN) (2008)

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome. Reservoir ... [more ▼]

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome. Reservoir computing (RC) uses a randomly created recurrent neural network (the reservoir) which functions as a temporal kernel of rich dynamics that projects the input to a high dimensional space. This projection is mapped into the desired output (only this mapping must be learned with standard linear regression methods).In this work, RC is used for imitation learning of navigation behaviors generated by an intelligent navigation system in the literature. Obstacle avoidance, exploration and target seeking behaviors are reproduced with an increase in stability and robustness over the original controller. Experiments also show that the system generalizes the behaviors for new environments. [less ▲]

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See detailMobile Robot Control in the Road Sign Problem using Reservoir Computing Networks
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Proceedings of the IEEE Int. Conf. on Robotics and Automation (ICRA) (2008)

In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must ... [more ▼]

In this work we tackle the road sign problem with reservoir computing (RC) networks. The T-maze task (a particular form of the road sign problem) consists of a robot in a T-shaped environment that must reach the correct goal (left or right arm of the T-maze) depending on a previously received input sign. It is a control task in which the delay period between the sign received and the required response (e.g., turn right or left) is a crucial factor. Delayed response tasks like this one form a temporal problem that can be handled very well by RC networks. Reservoir computing is a biologically plausible technique which overcomes the problems of previous algorithms such as backpropagation through time - which exhibits slow (or non-) convergence on training. RC is a new concept that includes a fast and efficient training algorithm. We show that this simple approach can solve the T-maze task efficiently. [less ▲]

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See detailEvent detection and localization for small mobile robots using reservoir computing
Antonelo, Eric Aislan UL; Schrauwen, Benjamin; Stroobandt, Dirk

in Neural Networks (2008), 21(6), 862--871

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