66,561 research outputs found
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Nonlinear Receding-Horizon Control of Rigid Link Robot Manipulators
The approximate nonlinear receding-horizon control law is used to treat the
trajectory tracking control problem of rigid link robot manipulators. The
derived nonlinear predictive law uses a quadratic performance index of the
predicted tracking error and the predicted control effort. A key feature of
this control law is that, for their implementation, there is no need to perform
an online optimization, and asymptotic tracking of smooth reference
trajectories is guaranteed. It is shown that this controller achieves the
positions tracking objectives via link position measurements. The stability
convergence of the output tracking error to the origin is proved. To enhance
the robustness of the closed loop system with respect to payload uncertainties
and viscous friction, an integral action is introduced in the loop. A nonlinear
observer is used to estimate velocity. Simulation results for a two-link rigid
robot are performed to validate the performance of the proposed controller.
Keywords: receding-horizon control, nonlinear observer, robot manipulators,
integral action, robustness
Stochastic Nonlinear Model Predictive Control with Efficient Sample Approximation of Chance Constraints
This paper presents a stochastic model predictive control approach for
nonlinear systems subject to time-invariant probabilistic uncertainties in
model parameters and initial conditions. The stochastic optimal control problem
entails a cost function in terms of expected values and higher moments of the
states, and chance constraints that ensure probabilistic constraint
satisfaction. The generalized polynomial chaos framework is used to propagate
the time-invariant stochastic uncertainties through the nonlinear system
dynamics, and to efficiently sample from the probability densities of the
states to approximate the satisfaction probability of the chance constraints.
To increase computational efficiency by avoiding excessive sampling, a
statistical analysis is proposed to systematically determine a-priori the least
conservative constraint tightening required at a given sample size to guarantee
a desired feasibility probability of the sample-approximated chance constraint
optimization problem. In addition, a method is presented for sample-based
approximation of the analytic gradients of the chance constraints, which
increases the optimization efficiency significantly. The proposed stochastic
nonlinear model predictive control approach is applicable to a broad class of
nonlinear systems with the sufficient condition that each term is analytic with
respect to the states, and separable with respect to the inputs, states and
parameters. The closed-loop performance of the proposed approach is evaluated
using the Williams-Otto reactor with seven states, and ten uncertain parameters
and initial conditions. The results demonstrate the efficiency of the approach
for real-time stochastic model predictive control and its capability to
systematically account for probabilistic uncertainties in contrast to a
nonlinear model predictive control approaches.Comment: Submitted to Journal of Process Contro
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
A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles
This paper proposes a real-time nonlinear model
predictive control (NMPC) strategy for direct yaw moment control
(DYC) of distributed drive electric vehicles (DDEVs). The NMPC
strategy is based on a control-oriented model built by integrating
a single track vehicle model with the Magic Formula (MF) tire
model. To mitigate the NMPC computational cost, the
continuation/generalized minimal residual (C/GMRES) algorithm
is employed and modified for real-time optimization. Since the
traditional C/GMRES algorithm cannot directly solve the
inequality constraint problem, the external penalty method is
introduced to transform inequality constraints into an
equivalently unconstrained optimization problem. Based on the
Pontryagin’s minimum principle (PMP), the existence and
uniqueness for solution of the proposed C/GMRES algorithm are
proven. Additionally, to achieve fast initialization in C/GMRES
algorithm, the varying predictive duration is adopted so that the
analytic expressions of optimally initial solutions in C/GMRES
algorithm can be derived and gained. A Karush-Kuhn-Tucker
(KKT) condition based control allocation method distributes the
desired traction and yaw moment among four independent
motors. Numerical simulations are carried out by combining
CarSim and Matlab/Simulink to evaluate the effectiveness of the
proposed strategy. Results demonstrate that the real-time NMPC
strategy can achieve superior vehicle stability performance,
guarantee the given safety constraints, and significantly reduce the
computational efforts
Mathematical control of complex systems 2013
Mathematical control of complex systems have already become an ideal research area for control engineers, mathematicians, computer scientists, and biologists to understand, manage, analyze, and interpret functional information/dynamical behaviours from real-world complex dynamical systems, such as communication systems, process control, environmental systems, intelligent manufacturing systems, transportation systems, and structural systems. This special issue aims to bring together the latest/innovative knowledge and advances in mathematics for handling complex systems. Topics include, but are not limited to the following: control systems theory (behavioural systems, networked control systems, delay systems, distributed systems, infinite-dimensional systems, and positive systems); networked control (channel capacity constraints, control over communication networks, distributed filtering and control, information theory and control, and sensor networks); and stochastic systems (nonlinear filtering, nonparametric methods, particle filtering, partial identification, stochastic control, stochastic realization, system identification)
Model predictive control techniques for hybrid systems
This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581
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