1,112 research outputs found
Robust Constrained Model Predictive Control using Linear Matrix Inequalities
The primary disadvantage of current design techniques for model predictive control (MPC) is their inability to deal explicitly with plant model uncertainty. In this paper, we present a new approach for robust MPC synthesis which allows explicit incorporation of the description of plant uncertainty in the problem formulation. The uncertainty is expressed both in the time domain and the frequency domain. The goal is to design, at each time step, a state-feedback control law which minimizes a "worst-case" infinite horizon objective function, subject to constraints on the control input and plant output. Using standard techniques, the problem of minimizing an upper bound on the "worst-case" objective function, subject to input and output constraints, is reduced to a convex optimization involving linear matrix inequalities (LMIs). It is shown that the feasible receding horizon state-feedback control design robustly stabilizes the set of uncertain plants under consideration. Several extensions, such as application to systems with time-delays and problems involving constant set-point tracking, trajectory tracking and disturbance rejection, which follow naturally from our formulation, are discussed. The controller design procedure is illustrated with two examples. Finally, conclusions are presented
Model predictive control application to spacecraft rendezvous in mars sample return scenario
Model Predictive Control (MPC) is an optimization-based control strategy that is considered extremely attractive in the autonomous space rendezvous scenarios. The Online Recon¦guration Control System and Avionics Architecture (ORCSAT) study addresses its applicability in Mars Sample Return (MSR) mission, including the implementation of the developed solution in a space representative avionic architecture system. With respect to a classical control solution High-integrity Autonomous RendezVous and Docking control system (HARVD), MPC allows a signi¦cant performance improvement both in trajectory and in propellant save. Furthermore, thanks to the online optimization, it allows to identify improvements in other areas (i. e., at mission de¦nition level) that could not be known a priori
Convex Model Predictive Control for Down-regulation Strategies in Wind Turbines
Wind turbine (WT) controllers are often geared towards maximum power
extraction, while suitable operating constraints should be guaranteed such that
WT components are protected from failures. Control strategies can be also
devised to reduce the generated power, for instance to track a power reference
provided by the grid operator. They are called down-regulation strategies and
allow to balance power generation and grid loads, as well as to provide
ancillary grid services, such as frequency regulation. Although this balance is
limited by the wind availability and grid demand, the quality of wind energy
can be improved by introducing down-regulation strategies that make use of the
kinetic energy of the turbine dynamics. This paper shows how the kinetic energy
in the rotating components of turbines can be used as an additional
degree-of-freedom by different down-regulation strategies. In particular we
explore the power tracking problem based on convex model predictive control
(MPC) at a single wind turbine. The use of MPC allows us to introduce a further
constraint that guarantees flow stability and avoids stall conditions.
Simulation results are used to illustrate the performance of the developed
down-regulation strategies. Notably, by maximizing rotor speeds, and thus
kinetic energy, the turbine can still temporarily guarantee tracking of a given
power reference even when occasional saturation of the available wind power
occurs. In the study case we proved that our approach can guarantee power
tracking in saturated conditions for 10 times longer than with traditional
down-regulation strategies.Comment: 6 pages, 2 figures, 61st IEEE Conference on Decision and Control 202
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