scholarly journals Performance Measures in Model Predictive Control with Non-linear Prediction Horizon Time-discretization

2007 ◽  
Vol 43 (10) ◽  
pp. 883-891 ◽  
Author(s):  
Ravi GONDHALEKAR ◽  
Jun-ichi IMURA
Keyword(s):  
Model Predictive Control ◽  
Performance Measures ◽  
Predictive Control ◽  
Linear Prediction ◽  
Time Discretization ◽  
Prediction Horizon ◽  
Non Linear

Shortest-prediction-horizon non-linear model-predictive control

1998 ◽  
Vol 53 (2) ◽  
pp. 273-292 ◽  
Author(s):  
Sairam Valluri ◽  
Masoud Soroush ◽  
Masoud Nikravesh
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Prediction Horizon ◽  
Non Linear

scholarly journals Time Adaptivity in Model Predictive Control

2021 ◽  
Vol 90 (1) ◽  
Author(s):  
Alessandro Alla ◽  
Carmen Gräßle ◽  
Michael Hinze
Keyword(s):  
Optimal Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Error Control ◽  
Time Discretization ◽  
Posteriori Error ◽  
Parabolic Pdes ◽  
Prediction Horizon ◽  
Control Concept ◽  
Adaptive Time

AbstractThe core of the Model Predictive Control (MPC) method in every step of the algorithm consists in solving a time-dependent optimization problem on the prediction horizon of the MPC algorithm, and then to apply a portion of the optimal control over the application horizon to obtain the new state. To solve this problem efficiently, we propose a time-adaptive residual based a-posteriori error control concept based on the optimality system of this optimal control problem. This approach not only delivers an adaptive time discretization of the prediction horizon, but also suggests an adaptive time discretization of the application horizon, whose length could be either adaptive or fixed. We apply this concept for systems governed by linear parabolic PDEs and present several numerical examples which demonstrate the performance and the robustness of our adaptive MPC control concept.

Non-Linear Model Predictive Control Applied to a Generic Ocean-Wave Energy Extractor

2013 ◽  
Author(s):  
Nathan Tom ◽  
Ronald W. Yeung
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Wave Energy ◽  
Linear Optimization ◽  
Irregular Waves ◽  
Passive System ◽  
Prediction Horizon ◽  
Near Resonance ◽  
Non Linear ◽  
The Time Domain

This paper evaluates the theoretical application of nonlinear model predictive control (NMPC) to a model-scale point absorber for wave energy conversion. The NMPC strategy will be evaluated against a passive system, which utilizes no controller, using a performance metric based on the absorbed energy. The NMPC strategy was set up as a non-linear optimization problem utilizing IPOPT package to obtain a time varying optimal generator damping from the power-take-off (PTO) unit. This formulation is different from previous investigations in model predictive control, as the current methodology only allows the power-take-off unit to behave as a generator, thereby unable to return energy to the waves. Each strategy was simulated in the time domain for regular and irregular waves, the latter taken from a modified Pierson-Moskowitz spectrum. In regular waves, the performance advantages over a passive system appear at frequencies near resonance while at the lower and higher frequencies they become nearly equivalent. For irregular waves, the NMPC strategy lead to greater quantities of energy absorption than the passive system, though strongly dependent on the prediction horizon. It was found that the ideal NMPC strategy required a generator that could be turned on and off instantaneously, leading to sequences where the generator can be inactive for up to 50% of the wave period.

scholarly journals Reinforcement Learning of the Prediction Horizon in Model Predictive Control

2021 ◽  
Vol 54 (6) ◽  
pp. 314-320
Author(s):  
Eivind Bøhn ◽  
Sebastien Gros ◽  
Signe Moe ◽  
Tor Arne Johansen
Keyword(s):  
Reinforcement Learning ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Prediction Horizon

scholarly journals Stable Model Predictive Control Design: Sequential Approach

2011 ◽  
Vol 62 (2) ◽  
pp. 99-103
Author(s):  
Vojtech Veselý
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Design Method ◽  
Control Design ◽  
Sequential Design ◽  
Stable Model ◽  
Sequential Approach ◽  
Prediction Horizon ◽  
Guaranteed Cost ◽  
One Step

Stable Model Predictive Control Design: Sequential Approach The paper addresses the problem of output feedback stable model predictive control design with guaranteed cost. The proposed design method pursues the idea of sequential design for N prediction horizon using one-step ahead model predictive control design approach. Numerical examples are given to illustrate the effectiveness of the proposed method.

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