Model predictive control of a multi-degree-of-freedom wave energy converter with model mismatch and prediction errors

The power captured by a wave energy converter (WEC) can be greatly increased through the use of a well-conceived wave-by-wave control strategy. Optimal strategies including Model Predictive Control (MPC) rely on a dynamic model of the WEC and prediction of the wave excitation force several seconds into the future. Both the modelling and prediction processes are subject to errors. This study investigates the impact of these errors on the performance of a WEC under MPC. Idealised simulations are conducted to establish a suitable prediction horizon and establish a performance benchmark against an optimally tuned passively damped system. Power increases of over 200% are seen. The assumptions of perfect prediction and system modelling are progressively removed, culminating in multi-body simulation of a specific multi-DOF submerged point absorber WEC with constrained MPC. Under realistic conditions, the power gain is a more modest 30% at best across the tested sea states, demonstrating that these errors have a significant impact on performance. However, the ability to use constraints to limit motion in high energy seas and the tunability of the control law are valuable attributes for practical deployment. Overall the performance gains demonstrate the benefits of such control strategies for application to multi-DOF WECs.</p

Model predictive control of a multi-degree-of-freedom wave energy converter with model mismatch and prediction errors

The power captured by a wave energy converter (WEC) can be greatly increased through the use of a well-conceived wave-by-wave control strategy. Optimal strategies including Model Predictive Control (MPC) rely on a dynamic model of the WEC and prediction of the wave excitation force several seconds into the future. Both the modelling and prediction processes are subject to errors. This study investigates the impact of these errors on the performance of a WEC under MPC. Idealised simulations are conducted to establish a suitable prediction horizon and establish a performance benchmark against an optimally tuned passively damped system. Power increases of over 200% are seen. The assumptions of perfect prediction and system modelling are progressively removed, culminating in multi-body simulation of a specific multi-DOF submerged point absorber WEC with constrained MPC. Under realistic conditions, the power gain is a more modest 30% at best across the tested sea states, demonstrating that these errors have a significant impact on performance. However, the ability to use constraints to limit motion in high energy seas and the tunability of the control law are valuable attributes for practical deployment. Overall the performance gains demonstrate the benefits of such control strategies for application to multi-DOF WECs.</p

Economic MPC with Modifier Adaptation using Transient Measurements

Producción CientíficaThis paper presents a method to estimate process dynamic gradients along the transient that combined with the idea of Modifier Adaptation (MA) improves the economic cost fuction of the examples presented. The gradient estimation method, called TMA, aims to reduce the large convergence time required to traditional MA in processes of slow dynamics. TMA is used with an economic predictive control with MA (eMPC+TMA) and was applied in two case studies: a simulation of the Williams-Otto reactor and a hybrid laboratory plant based on the Van de Vusse reactor. The results show that eMPC+TMA could reach the plant real steady-state optimum despite process-model mismatch, due to the inclusion of the effect of process dynamics in the TMA algorithm. Despite the estimation errors, the proposed methodology improved the profit of the experimental case study, with respect to the use of an eMPC with no modifiers, by about 20% for the unconstrained case, and by 130% in the constrained case.Junta de Castilla y León (CLU 2017-09 and UIC 233)FEDER - AEI (PGC2018-099312-B-C31

Analysis and structural design of on-line process optimization systems.

Imperial Users onl

Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference