Nonlinear Model Predictive Control of Electrical Submersible Pumps based on Echo State Networks

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.     

Flood control of the Demer by using Model Predictive Control

It is shown how Model Predictive Control can be used for flood control of river systems modelled with real data. A linear model for the Demer, a river in Belgium, is derived, which is used inside the optimisation problem solved by the controller. This optimisation problem is formulated such that the controller can be used for set-point and flood control. A Kalman filter is used as a state estimator. Closed loop simulations performed with a full hydrodynamic model of the Demer in combination with historical rainfall data show that the proposed control scheme outperforms the current control strategy.     

Corrigendum to the paper ‘Linear Time Varying Model Predictive Control and its Application to Active Steering Systems: Stability Analysis and Experimental Validation’ (International Journal of Robust and Nonlinear Control 2008; 18(8):862–875)

This note is a corrigendum of the paper ‘Linear Time Varying Model Predictive Control and its Application to Active Steering Systems: Stability Analysis and Experimental Validation’, published on the volume 18, issue 8, pages 862–875 of the International Journal of Robust and Nonlinear Control in 2008. Next we point out a technical error in the proof of Lemma 2 of the paper, and provide the corrected version of the lemma. Copyright © 2011 John Wiley & Sons, Ltd.