20,557 research outputs found
A sequential semidefinite programming method and an application in passive reduced-order modeling
We consider the solution of nonlinear programs with nonlinear
semidefiniteness constraints. The need for an efficient exploitation of the
cone of positive semidefinite matrices makes the solution of such nonlinear
semidefinite programs more complicated than the solution of standard nonlinear
programs. In particular, a suitable symmetrization procedure needs to be chosen
for the linearization of the complementarity condition. The choice of the
symmetrization procedure can be shifted in a very natural way to certain linear
semidefinite subproblems, and can thus be reduced to a well-studied problem.
The resulting sequential semidefinite programming (SSP) method is a
generalization of the well-known SQP method for standard nonlinear programs. We
present a sensitivity result for nonlinear semidefinite programs, and then
based on this result, we give a self-contained proof of local quadratic
convergence of the SSP method. We also describe a class of nonlinear
semidefinite programs that arise in passive reduced-order modeling, and we
report results of some numerical experiments with the SSP method applied to
problems in that class
Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator
A non-parametric technique for modeling the behavior of power amplifiers is
presented. The proposed technique relies on the principles of density
estimation using the kernel method and is suited for use in power amplifier
modeling. The proposed methodology transforms the input domain into an
orthogonal memory domain. In this domain, non-parametric static functions are
discovered using the kernel estimator. These orthogonal, non-parametric
functions can be fitted with any desired mathematical structure, thus
facilitating its implementation. Furthermore, due to the orthogonality, the
non-parametric functions can be analyzed and discarded individually, which
simplifies pruning basis functions and provides a tradeoff between complexity
and performance. The results show that the methodology can be employed to model
power amplifiers, therein yielding error performance similar to
state-of-the-art parametric models. Furthermore, a parameter-efficient model
structure with 6 coefficients was derived for a Doherty power amplifier,
therein significantly reducing the deployment's computational complexity.
Finally, the methodology can also be well exploited in digital linearization
techniques.Comment: Matlab sample code (15 MB):
https://dl.dropboxusercontent.com/u/106958743/SampleMatlabKernel.zi
On the interpretation and identification of dynamic Takagi-Sugenofuzzy models
Dynamic Takagi-Sugeno fuzzy models are not always easy to interpret, in particular when they are identified from experimental data. It is shown that there exists a close relationship between dynamic Takagi-Sugeno fuzzy models and dynamic linearization when using affine local model structures, which suggests that a solution to the multiobjective identification problem exists. However, it is also shown that the affine local model structure is a highly sensitive parametrization when applied in transient operating regimes. Due to the multiobjective nature of the identification problem studied here, special considerations must be made during model structure selection, experiment design, and identification in order to meet both objectives. Some guidelines for experiment design are suggested and some robust nonlinear identification algorithms are studied. These include constrained and regularized identification and locally weighted identification. Their usefulness in the present context is illustrated by examples
Efficient computation of inverse dynamics and feedback linearization for VSA-based robots
We develop a recursive numerical algorithm to compute the inverse dynamics of robot manipulators with an arbitrary number of joints, driven by variable stiffness actuation (VSA) of the antagonistic type. The algorithm is based on Newton-Euler dynamic equations, generalized up to the fourth differential order to account for the compliant transmissions, combined with the decentralized nonlinear dynamics of the variable stiffness actuators at each joint. A variant of the algorithm can be used also for implementing a feedback linearization control law for the accurate tracking of desired link and stiffness trajectories. As in its simpler versions, the algorithm does not require dynamicmodeling in symbolic form, does not use numerical approximations, grows linearly in complexity with the number of joints, and is suitable for online feedforward and real-time feedback control. A Matlab/C code is made available
3D modeling of the vane test on a power-law cement paste by means of the proper generalized decomposition
The eïŹective modeling of the ïŹow of fresh concrete materials in settings such as that of the vane test is a challenging process that is the object of ongoing research. Previous works modeled concrete and cement pastes as solids subjected to yielding or as Bingham or power-law ïŹuids, both in two or three dimensions [1, 2]. Of the existing models, those implementing power-law ïŹuids in three dimensions carry the best predictive ability considering the typically heterogeneous composition of concrete suspensions and the relatively complex three-dimensional features of their ïŹows.
In this work, we model the vane test in a power-law cement paste using the Proper Generalized Decomposition (PGD). In this framework, the three-dimensional problem is solved as a sequence of 2D Ă 1D problems, thus alleviating the curse of dimensionality. This choice is supported by experience from previous works using the PGD to simulate Non-Newtonian behavior using iterative resolutions [3, 4]. It is also particularly useful in addressing the inverse problem corresponding to the identiïŹcation of the material properties of cement pastes from experimental data, as this requires many direct resolutions of the forward problem. The use of the PGD is also appealing because the model parameters can be introduced as extra coordinates of the problem [5]
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
Crone control of a nonlinear hydraulic actuator
The CRONE control (fractional robust control) of a hydraulic actuator whose dynamic model is nonlinear is presented. An input-output linearization under diffeomorphism and feedback is first achieved for the nominal plant. The relevance of this linearization when the parameters of the plant vary is then analyzed using the Volterra input-output representation in the frequency domain. CRONE control based on complex fractional differentiation is finally applied to control the velocity of the input-output linearized model when parametric variations occur
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