scholarly journals Verification of model predictive control laws using weispfenning's quantifier elimination by virtual substitution algorithm

2016 ◽  
Author(s):  
K. Siaulys ◽  
J. M. Maciejowski
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Quantifier Elimination ◽  
Control Laws

scholarly journals A Nonlinear Model Predictive Control with Enlarged Region of Attraction via the Union of Invariant Sets

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2087
Author(s):  
Ismi Rosyiana Fitri ◽  
Jung-Su Kim
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Convex Combination ◽  
Invariant Sets ◽  
Region Of Attraction ◽  
Control Laws ◽  
Terminal Set ◽  
The One ◽  
The Given ◽  
Regions Of Attraction

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.

scholarly journals Low EffortLiNuclear Fusion Plasma Control Using Model Predictive Control Laws

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
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.

On the computation of linear model predictive control laws

Automatica ◽  
2010 ◽  
Vol 46 (6) ◽  
pp. 1035-1041 ◽  
Author(s):  
Francesco Borrelli ◽  
Mato Baotić ◽  
Jaroslav Pekar ◽  
Greg Stewart
Keyword(s):  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Laws

Fast distributed model predictive control combining ADMM, IPM and Riccati iteration

2021 ◽  
Vol 69 (2) ◽  
pp. 97-110
Author(s):  
Giuliano Costantini ◽  
Daniel Görges
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Formation Control ◽  
Substantial Reduction ◽  
Computation Time ◽  
Optimization Techniques ◽  
Distributed Model ◽  
Multi Agent Systems ◽  
Control Laws ◽  
Distributed Model Predictive Control

Abstract In model predictive control, the control action is found at each sampling time by solving an online optimization problem. Computationally, this step is very demanding, especially if compared to the evaluation of traditional control laws. This has limited the application of model predictive control to systems with slow dynamics for many years. Recently, several methods have been proposed in the literature which promise a substantial reduction of the computation time by either running the computation in parallel (distributed model predictive control) or exploiting the problem structure (fast model predictive control). A combination of these methods has not yet been considered in the literature. To achieve this goal, different optimization techniques need to be employed at once. The order of how these methods are applied matters. This paper considers fast distributed model predictive control combining the alternating direction method of multipliers (ADMM), the interior point method (IPM) and the Riccati iteration for a particular class of multi-agent systems for which the order of the methods can be arbitrarily changed. This leads to two different solver schemes where a trade-off arises between computation time and number of communications required to reach consensus. A simplified problem involving the formation control of a fleet of vehicles is considered at the end.

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