24,767 research outputs found
Stability Analysis of Piecewise Affine Systems with Multi-model Model Predictive Control
Constrained model predictive control (MPC) is a widely used control strategy,
which employs moving horizon-based on-line optimisation to compute the optimum
path of the manipulated variables. Nonlinear MPC can utilize detailed models
but it is computationally expensive; on the other hand linear MPC may not be
adequate. Piecewise affine (PWA) models can describe the underlying nonlinear
dynamics more accurately, therefore they can provide a viable trade-off through
their use in multi-model linear MPC configurations, which avoid integer
programming. However, such schemes may introduce uncertainty affecting the
closed loop stability. In this work, we propose an input to output stability
analysis for closed loop systems, consisting of PWA models, where an observer
and multi-model linear MPC are applied together, under unstructured
uncertainty. Integral quadratic constraints (IQCs) are employed to assess the
robustness of MPC under uncertainty. We create a model pool, by performing
linearisation on selected transient points. All the possible uncertainties and
nonlinearities (including the controller) can be introduced in the framework,
assuming that they admit the appropriate IQCs, whilst the dissipation
inequality can provide necessary conditions incorporating IQCs. We demonstrate
the existence of static multipliers, which can reduce the conservatism of the
stability analysis significantly. The proposed methodology is demonstrated
through two engineering case studies.Comment: 28 pages 9 figure
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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Computer assisted modelling of linear, integer and separable programming problems
For mathematical programming (MP) to have greater impact upon the decision making process, MP software systems must offer suitable support in terms of model communication and modelling techniques . In this paper modelling techniques that allow logical restrictions to be modelled in integer programming terms are described and their implications discussed. In
addition it is demonstrated that many classes of non-linearities which are not variable separable may be reformulated in piecewise linear form. It is shown that analysis of bounds is necessary in the following three important contexts: model reduction, formulation of logical restrictions as 0-1 mixed integer programs and reformulation of nonlinear programs as variable separable programs, It is observed that as well as incorporating an interface between the modeller and the optimiser there is a need to make available to the modeller software facilities which support the modelling techniques described here
Strong Stationarity Conditions for Optimal Control of Hybrid Systems
We present necessary and sufficient optimality conditions for finite time
optimal control problems for a class of hybrid systems described by linear
complementarity models. Although these optimal control problems are difficult
in general due to the presence of complementarity constraints, we provide a set
of structural assumptions ensuring that the tangent cone of the constraints
possesses geometric regularity properties. These imply that the classical
Karush-Kuhn-Tucker conditions of nonlinear programming theory are both
necessary and sufficient for local optimality, which is not the case for
general mathematical programs with complementarity constraints. We also present
sufficient conditions for global optimality.
We proceed to show that the dynamics of every continuous piecewise affine
system can be written as the optimizer of a mathematical program which results
in a linear complementarity model satisfying our structural assumptions. Hence,
our stationarity results apply to a large class of hybrid systems with
piecewise affine dynamics. We present simulation results showing the
substantial benefits possible from using a nonlinear programming approach to
the optimal control problem with complementarity constraints instead of a more
traditional mixed-integer formulation.Comment: 30 pages, 4 figure
N-fold integer programming in cubic time
N-fold integer programming is a fundamental problem with a variety of natural
applications in operations research and statistics. Moreover, it is universal
and provides a new, variable-dimension, parametrization of all of integer
programming. The fastest algorithm for -fold integer programming predating
the present article runs in time with the binary length of
the numerical part of the input and the so-called Graver complexity of
the bimatrix defining the system. In this article we provide a drastic
improvement and establish an algorithm which runs in time having
cubic dependency on regardless of the bimatrix . Our algorithm can be
extended to separable convex piecewise affine objectives as well, and also to
systems defined by bimatrices with variable entries. Moreover, it can be used
to define a hierarchy of approximations for any integer programming problem
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Linear, integer separable and fuzzy programming problems: a united approach towards automatic reformulation
For mathematical programming (MP) to have greater impact as a
decision tool, MP software systems must offer suitable support in
terms of model communication and modelling techniques. In this
paper modelling techniques that allow logical restrictions to be
modelled in integer programming terms are described and their
implications discussed. In addition it is demonstrated that many
classes of non-linearities which are not variable separable may be
after suitable algebraic manipulation put in a variable separable
form. The methods of reformulating the fuzzy linear programming
problem as a Max-Min problem is also introduced. It is shown that
analysis of bounds plays a key role in the following four important
contexts: model reduction, reformulation of logical restrictions
as 0-1 mixed integer programs, reformulation of nonlinear programs
as variable separable programs and reformulation of fuzzy linear
programs. It is observed that as well as incorporating an
interface between the modeller and the optimiser there is a need to
make available to the modeller software facilities which support the
model reformulation techniques described here
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