EXPLICIT PREDICTIVE CONTROL LAWS - On the Geometry of Feasible Domains and the Presence of Nonlinearities

In the dual-mode model predictive control (MPC) framework, the size of the stabilizable set, which is also the region of attraction, depends on the terminal constraint set. This paper aims to formulate a larger terminal set for enlarging the region of attraction in a nonlinear MPC. Given several control laws and their corresponding terminal invariant sets, a convex combination of the given sets is used to construct a time-varying terminal set. The resulting region of attraction is the union of the regions of attraction from each invariant set. Simulation results show that the proposed MPC has a larger stabilizable initial set than the one obtained when a fixed terminal set is used.

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Robustification of explicit predictive control laws

Proceedings of the 45th IEEE Conference on Decision and Control ◽

10.1109/cdc.2006.377506 ◽

2006 ◽

Cited By ~ 14

Author(s):

Sorin Olaru ◽

Pedro Rodriguez Ayerbe

Keyword(s):

Predictive Control ◽

Control Laws

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Set membership approximation of discontinuous nonlinear model predictive control laws

Automatica ◽

10.1016/j.automatica.2011.09.039 ◽

2012 ◽

Vol 48 (1) ◽

pp. 191-197 ◽

Cited By ~ 6

Author(s):

Lorenzo Fagiano ◽

Massimo Canale ◽

Mario Milanese

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Nonlinear Model ◽

Nonlinear Model Predictive Control ◽

Control Laws ◽

Set Membership

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Design of Robust Predictive Control Laws Using Set Membership Identified Models

Asian Journal of Control ◽

10.1002/asjc.560 ◽

2012 ◽

Vol 15 (6) ◽

pp. 1714-1722 ◽

Cited By ~ 5

Author(s):

M. Canale ◽

L. Fagiano ◽

M.C. Signorile

Keyword(s):

Predictive Control ◽

Control Laws ◽

Set Membership ◽

Robust Predictive Control

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Designs of Feedback Controllers for Fluid Flows Based On Model Predictive Control and Regression Analysis

Energies ◽

10.3390/en13061325 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1325 ◽

Cited By ~ 1

Author(s):

Yasuo Sasaki ◽

Daisuke Tsubakino

Keyword(s):

Regression Analysis ◽

Feedback Control ◽

Predictive Control ◽

Vortex Shedding ◽

Ridge Regression ◽

Output Feedback ◽

State Feedback ◽

Output Feedback Control ◽

State Feedback Control ◽

Control Laws

Complexity of online computation is a drawback of model predictive control (MPC) when applied to the Navier–Stokes equations. To reduce the computational complexity, we propose a method to approximate the MPC with an explicit control law by using regression analysis. In this paper, we extracted two state-feedback control laws and two output-feedback control laws for flow around a cylinder as a benchmark. The state-feedback control laws that feed back different quantities to each other were extracted by ridge regression, and the two output-feedback control laws, whose measurement output is the surface pressure, were extracted by ridge regression and Gaussian process regression. In numerical simulations, the state-feedback control laws were able to suppress vortex shedding almost completely. While the output-feedback control laws could not suppress vortex shedding completely, they moderately improved the drag of the cylinder. Moreover, we confirmed that these control laws have some degree of robustness to the change in the Reynolds number. The computation times of the control input in all the extracted control laws were considerably shorter than that of the MPC.

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Low EffortLiNuclear Fusion Plasma Control Using Model Predictive Control Laws

Mathematical Problems in Engineering ◽

10.1155/2015/527420 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 27

Author(s):

Izaskun Garrido ◽

Aitor J. Garrido ◽

Jesús A. Romero ◽

Edorta Carrascal ◽

Goretti Sevillano-Berasategui ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Fusion Energy ◽

Current Drive ◽

Current Profile ◽

Fusion Plasma ◽

Successful Operation ◽

Control Laws ◽

Internal Inductance ◽

Profile Control

One of the main problems of fusion energy is to achieve longer pulse duration by avoiding the premature reaction decay due to plasma instabilities. The control of the plasma inductance arises as an essential tool for the successful operation of tokamak fusion reactors in order to overcome stability issues as well as the new challenges specific to advanced scenarios operation. In this sense, given that advanced tokamaks will suffer from limited power available from noninductive current drive actuators, the transformer primary coil could assist in reducing the power requirements of the noninductive current drive sources needed for current profile control. Therefore, tokamak operation may benefit from advanced control laws beyond the traditionally used PID schemes by reducing instabilities while guaranteeing the tokamak integrity. In this paper, a novel model predictive control (MPC) scheme has been developed and successfully employed to optimize both current and internal inductance of the plasma, which influences the L-H transition timing, the density peaking, and pedestal pressure. Results show that the internal inductance and current profiles can be adequately controlled while maintaining the minimal control action required in tokamak operation.

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