1,054 research outputs found
Towards parallelizable sampling-based Nonlinear Model Predictive Control
This paper proposes a new sampling-based nonlinear model predictive control
(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
N and linear in the number of samples. The idea of the proposed algorithm is to
use the sequence of predicted inputs from the previous time step as a warm
start, and to iteratively update this sequence by changing its elements one by
one, starting from the last predicted input and ending with the first predicted
input. This strategy, which resembles the dynamic programming principle, allows
for parallelization up to a certain level and yields a suboptimal nonlinear MPC
algorithm with guaranteed recursive feasibility, stability and improved cost
function at every iteration, which is suitable for real-time implementation.
The complexity of the algorithm per each time step in the prediction horizon
depends only on the horizon, the number of samples and parallel threads, and it
is independent of the measured system state. Comparisons with the fmincon
nonlinear optimization solver on benchmark examples indicate that as the
simulation time progresses, the proposed algorithm converges rapidly to the
"optimal" solution, even when using a small number of samples.Comment: 9 pages, 9 pictures, submitted to IFAC World Congress 201
Robust semi-explicit model predictive control for hybrid automata
In this paper we propose an on-line design technique for the target control problem of hybrid automata. First, we compute on-line the shortest path, which has the minimum discrete cost, from an initial state to the given target set. Next, we derive a controller which successfully drives the system from the initial state to the target set while minimizing a cost function. The (robust) model predictive control (MPC) technique is used when the current state is not within a guard set, otherwise the (robust) mixed-integer predictive control (MIPC) technique is employed. An on-line, semi-explicit control algorithm is derived by combining the two techniques and applied on a high-speed and energy-saving control problem of the CPU processing
Iterative Nonlinear Control of a Semibatch Reactor. Stability Analysis
This paper presents the application of Iterative
Nonlinear Model Predictive Control, INMPC, to a semibatch
chemical reactor. The proposed control approach is derived
from a model-based predictive control formulation which takes
advantage of the repetitive nature of batch processes. The
proposed controller combines the good qualities of Model
Predictive Control (MPC) with the possibility of learning from
past batches, that is the base of Iterative Control. It uses a
nonlinear model and a quadratic objective function that is
optimized in order to obtain the control law. A stability proof
with unitary control horizon is given for nonlinear plants that
are affine in control and have linear output map.
The controller shows capabilities to learn the optimal trajectory after a few iterations, giving a better fit than a linear
non-iterative MPC controller. The controller has applications in
repetitive disturbance rejection, because they do not modify
the model for control purposes. In this application, some
experiments with a disturbance in inlet water temperature has
been performed, getting good results.Ministerio de Ciencia y Tecnología DPI2004-07444-C04-0
On output feedback nonlinear model predictive control using high gain observers for a class of systems
In recent years, nonlinear model predictive control schemes have been derived that guarantee stability of the closed loop under the assumption of full state information. However, only limited advances have been made with respect to output feedback in connection to nonlinear predictive control. Most of the existing approaches for output feedback nonlinear model predictive control do only guarantee local stability. Here we consider the combination of stabilizing instantaneous NMPC schemes with high gain observers. For a special MIMO system class we show that the closed loop is asymptotically stable, and that the output feedback NMPC scheme recovers the performance of the state feedback in the sense that the region of attraction and the trajectories of the state feedback scheme are recovered for a high gain observer with large enough gain and thus leading to semi-global/non-local results
Real-time Projected Gradient-based Nonlinear Model Predictive Control with an Application to Anesthesia Control
Medical drug infusion problems pose a combination of challenges such as
nonlinearities from physiological models, model uncertainty due to inter- and
intra-patient variability, as well as strict safety specifications. With these
challenges in mind, we propose a novel real-time Nonlinear Model Predictive
Control (NMPC) scheme based on projected gradient descent iterations. At each
iteration, a small number of steps along the gradient of the NMPC cost is
taken, generating a suboptimal input which asymptotically converges to the
optimal input. We retrieve classical Lyapunov stability guarantees by
performing a sufficient number of gradient iterations until fulfilling a
stopping criteria. Such a real-time control approach allows for higher sampling
rates and faster feedback from the system which is advantageous for the class
of highly variable and uncertain drug infusion problems. To demonstrate the
controller's potential, we apply it to hypnosis control in anesthesia of two
interacting drugs. The controller successfully regulates hypnosis even under
disturbances and uncertainty and fulfils benchmark performance criteria
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