Nonlinear Model Predictive Control of an Oil Well with Echo State Networks

Jordanou, Jean Panaioti; Camponogara, Eduardo; Antonelo, Eric Aislan; Aguiar, Marco A. S.

doi:10.1016/j.ifacol.2018.06.348

Cited by 20 publications

(9 citation statements)

References 13 publications

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“…Xiang et al [12] applied the Taylor expansion to linearize the ESN in the context of an operating point and adopted an ESN to compensate for the truncation error in a partially observed dynamical system. Jordanou et al [13] implemented ESNs into the practical nonlinear MPC framework developed by Plucenio et al [14] by applying the analytically computed gradient from the ESN model to the predictive model. Zhang et al [15] applied an ESN for the decentralized control problem of continuous-time nonlinear interconnected systems.…”

Section: Related Workmentioning

confidence: 99%

Echo State Network Based Model Predictive Control for Active Vibration Control of Hybrid Electric Vehicle Powertrains

Ogawa

Takahashi

2021

Applied Sciences

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Reservoir computing refers to a computational framework based on recurrent neural networks that can process time-series data. In an echo state network (ESN), which is a type of reservoir computing framework, the reservoir consists of a recursive network of artificial neurons with nonlinear activation functions. A model predictive control (MPC) technique can determine the control signals by solving the optimization problem of a system using the finite-time domain of each control period. However, real-time optimization cannot be achieved unless the optimal control problem can be solved within the next control period. To overcome this limitation, we propose a new control method based on MPC that explicitly incorporates the predicted disturbance of a time-varying trajectory using ESN to achieve the active vibration control of hybrid electric vehicle (HEV) powertrains. Once the ESN has been trained, the associated MPC explicitly satisfies the constraints over a moving horizon without further training. Instead of completing the real-time optimization within the control period, ESN predicts the future disturbance and applies it to the MPC in the future control period. Based on the predicted future disturbance, the system calculates the optimal control signals required for the future. Thus, real-time control can be realized because the optimal signals are determined before the subsequent control period occurs. The proposed method can be implemented in MPC even if the control period is too short to optimize as long as the disturbance can be reasonably measured and predicted. In this study, the simulation approach was demonstrated using the engine start condition in an HEV powertrain. The importance of this study is that the limitation of MPC relevant to real-time optimization can be relaxed by applying our proposed method.

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Section: Related Workmentioning

confidence: 99%

Echo State Network Based Model Predictive Control for Active Vibration Control of Hybrid Electric Vehicle Powertrains

Ogawa

Takahashi

2021

Applied Sciences

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“…Hybrid techniques are also appearing in the literature, as shown in [24], where the authors employed a nonlinear model predictive control of an oil well with Echo State Networks (ESNs).…”

Section: Model Predictive Controlmentioning

confidence: 99%

“…Add an optimality cut (24) to O (p) and obtain O (p+1) ; Let F (p+1) := F (p) ; else Keep upper bound ub (p+1) := ub (p) ;…”

Section: Master Problem Of the Benders Decompositionmentioning

confidence: 99%

Decompositions for MPC of Linear Dynamic Systems with Activation Constraints

et al. 2020

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The interconnection of dynamic subsystems that share limited resources are found in many applications, and the control of such systems of subsystems has fueled significant attention from scientists and engineers. For the operation of such systems, model predictive control (MPC) has become a popular technique, arguably for its ability to deal with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller receiving the signals and performing the computations of output signals. However, the distributed structure of such interconnected subsystems is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decomposition to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) with activation constraints, which brings about organizational flexibility and distributed computation. Two general methods are considered for hierarchical control and optimization, namely Benders decomposition and outer approximation. Results are reported from a numerical analysis of the decompositions and a simulated application to energy management, in which a limited source of energy is distributed among batteries of electric vehicles.

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“…Results showed that the Echo State based control provided good performance for setpoint tracking and disturbance rejection. Later Jordanou et al [27] also proposed a model predictive controller that uses ESN for identification purposes. Their scheme is based on combining the ESN with a Practical Nonlinear Model Predictive Controller (PNMPC) and exhibited good performance for setpoint tracking, while obeying the constraints.…”

Section: Echo State Networkmentioning

confidence: 99%

Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

Dias¹,

Soares

Jäschke

et al. 2019

Processes

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The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information contents, the obtained ESN performance could be regarded as very good, even when longer prediction horizons were proposed.

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Nonlinear Model Predictive Control of an Oil Well with Echo State Networks

Cited by 20 publications

References 13 publications

Echo State Network Based Model Predictive Control for Active Vibration Control of Hybrid Electric Vehicle Powertrains

Echo State Network Based Model Predictive Control for Active Vibration Control of Hybrid Electric Vehicle Powertrains

Decompositions for MPC of Linear Dynamic Systems with Activation Constraints

Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

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