Improving accuracy of parallel SLICOT model reduction routines for stable systems

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper shows part of the work carried out to develop parallel versions of the SLICOT routines for model reduction of stable systems. In particular, the routines that have been parallelised are those based on the solution of Lyapunov equations. The goal is to be able to work with larger unreduced models and also to obtain better performance in the reduction process. New routines have been developed using standard libraries to improve portability and efficiency. A preliminary version was released previously by the authors, which achieved high performance. However, accuracy improvements have been necessary in order to make the new routines similar to the sequential ones in this aspect. Routines presented in this paper preserve good performance obtained by the previous parallel implementation while maintaining high accuracy of sequential SLICOT routines.This work was partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2013-41049-PGuerrero López, D.; Román Moltó, JE. (2015). Improving accuracy of parallel SLICOT model reduction routines for stable systems. IEEE. https://doi.org/10.1109/MED.2015.7158781

Factorized solution of generalized stable Sylvester equations using many-core GPU accelerators

[EN] We investigate the factorized solution of generalized stable Sylvester equations such as those arising in model reduction, image restoration, and observer design. Our algorithms, based on the matrix sign function, take advantage of the current trend to integrate high performance graphics accelerators (also known as GPUs) in computer systems. As a result, our realisations provide a valuable tool to solve large-scale problems on a variety of platforms.We acknowledge support of the ANII - MPG Independent Research Group: "Efficient Hetergenous Computing" at UdelaR, a partner group of the Max Planck Institute in Magdeburg.Benner, P.; Dufrechou, E.; Ezzatti, P.; Gallardo, R.; Quintana-Ortí, ES. (2021). Factorized solution of generalized stable Sylvester equations using many-core GPU accelerators. The Journal of Supercomputing (Online). 77(9):10152-19164. https://doi.org/10.1007/s11227-021-03658-y101521916477

Streamlining of the state-dependent Riccati equation controller algorithm for an embedded implementation

In many practical control problems the dynamics of the plant to be controlled are nonlinear. However, in most cases the controller design is based on a linear approximation of the dynamics. One of the reasons for this is that, in general, nonlinear control design methods are difficult to apply to practical problems. The State Dependent Riccati Equation (SDRE) control approach is a relatively new practical approach to nonlinear control that has the simplicity of the classical Linear Quadratic control method. This approach has been recently applied to control experimental autonomous air vehicles with relative success. To make the SDRE approach practical in applications where the computational resources are limited and where the dynamic models are more complex it would be necessary to re-examine and streamline this control algorithm. The main objective of this work is to identify improvements that can be made to the implementation of the SDRE algorithm to improve its performance. This is accomplished by analyzing the structure of the algorithm and the underlying functions used to implement it. At the core of the SDRE algorithm is the solution, in real time, of an Algebraic Riccati Equation. The impact of the selection of a suitable algorithm to solve the Riccati Equation is analyzed. Three different algorithms were studied. Experimental results indicate that the Kleinman algorithm performs better than two other algorithms based on Newton’s method. This work also demonstrates that appropriately setting a maximum number of iterations for the Kleinman approach can improve the overall system performance without degrading accuracy significantly. Finally, a software implementation of the SDRE algorithm was developed and benchmarked to study the potential performance improvements of a hardware implementation. The test plant was an inverted pendulum simulation based on experimental hardware. Bottlenecks in the software implementation were identified and a possible hardware design to remove one such bottleneck was developed

Towards Active Car Body Suspension in Railway Vehicles

Today, most railway suspension systems are passive. The most wide-spread exception is active car body tilt systems, which are mounted in some high-speed trains. Replacing some of the passive suspension components with active could reduce the weight and cost of the vehicle. It may also improve passenger comfort without increasing the deflections within the suspension, or, similarly, allow the vehicle to be run at higher speeds or on less smooth tracks, with comfort and deflection kept at today’s levels. This thesis deals with background studies of a model of a railway vehicle, aiming towards actively controlling its vertical secondary suspension, i.e. the part of the suspension that is fitted vertically between the bogie frame and the car body. First, some requirements on the actuator, e.g. maximum forces, are studied, for some cases of replacing passive components with active. Those cases are: removing the antiroll bars, removing the pneumatic systems of the air-spring, and both combined, in all cases adding 2 actuators in the vertical direction for each bogie. The forces the actuators have to be able to deliver are high, but still within reason to implement. Also, the possibility to use a single-input single-output (SISO) control design is studied. It is found that neither input/output pairing, nor using stationary decoupling matrices, gives any promising results that a SISO control design could be based on. The coupling between the inputs and outputs is found to be both very high, and very frequency dependent. To make multiple-input multiple-output (MIMO) control design a feasible choice, the original nonlinear model with 330 states is linearized, and different methods of reducing this model are studied. A model reduction algorithm was developed, that was better suited to this problem than the two standard methods it was compared to. The new algorithm is both less computationally demanding, and for this model produces reduced models, that have gain curves that are closer to those of the full linear model, within the interesting frequency region. Finally, an attempt is made at designing a linear quadratic (LQ) control, and the difficulties with that control strategy on this particular model are discussed. Additional work is needed to fully understand the model, and to find a control law that offers an advantage over the fully passive system

A Householder-based algorithm for Hessenberg-triangular reduction

The QZ algorithm for computing eigenvalues and eigenvectors of a matrix pencil $A - \lambda B$ requires that the matrices first be reduced to Hessenberg-triangular (HT) form. The current method of choice for HT reduction relies entirely on Givens rotations regrouped and accumulated into small dense matrices which are subsequently applied using matrix multiplication routines. A non-vanishing fraction of the total flop count must nevertheless still be performed as sequences of overlapping Givens rotations alternately applied from the left and from the right. The many data dependencies associated with this computational pattern leads to inefficient use of the processor and poor scalability. In this paper, we therefore introduce a fundamentally different approach that relies entirely on (large) Householder reflectors partially accumulated into block reflectors, by using (compact) WY representations. Even though the new algorithm requires more floating point operations than the state of the art algorithm, extensive experiments on both real and synthetic data indicate that it is still competitive, even in a sequential setting. The new algorithm is conjectured to have better parallel scalability, an idea which is partially supported by early small-scale experiments using multi-threaded BLAS. The design and evaluation of a parallel formulation is future work

Algoritmos paralelos para la reducción de sistemas lineales de control estables

[EN] In the field of control theory, sometimes system models of big size (with many state variables) appear. When one of these high order systems needs to be simulated, studied or controlled, it is convenient to perform a previous work of model reduction in order to reduce the necessary (economic and temporal) costs. This process of obtaining a low order adequate representation of the original system usually has a high cost, because it has to be done with the original unreduced system. Thus, it is important to have high performance implementations for the problem of reducing linear control systems. In this thesis high performance implementations for some methods of model reduction have been developed. Current algorithms for model reduction of stable linear control systems and their implementation in the control library SLICOT have been analysed. New parallel algorithms for the methods strongly based on solving Lyapunov equations have been proposed. The new developed routines are incorporated in the high performance library for control PSLICOT. Apart from the main functions in charge of model reduction, all operations appearing in the problem and not having a high performance version yet have also been parallelised. One of these operations is the solution of Lyapunov equations in standard form. Parallel routines for solving these equations have been developed. These routines solve the equation obtaining directly the Cholesky factor of the solution, which fits better their application in model reduction. For this, Hammarling's method has been parallelised. The new routines solve in parallel and for dense matrices the four possible variants of standard Lyapunov equations: discrete and continuous versions, both transposed and not transposed. Interfaces offered by all the parallelised routines are similar to that of the existing routines in ScaLAPACK library, so they are easy to use from a user of this kind of libraries. The new routines work with the same data distribution used in this library: 2D block cyclic distribution, which allows many other distributions. Thanks to the developed work, now there are available high performance parallel routines to reduce linear control systems by using different variants of the Square-Root Balance & Truncate model reduction method: with or without balancing and with or without using the singular perturbation approximation formulas. They are parallel implementations of the same algorithms and methods used in the sequential routines of the SLICOT library. This allows to efficiently reduce models of linear control systems of big size. Moreover, existing software in ScaLAPACK for the eigenvalue problem has been improved by covering cases not treated there: the solution of the generalised problem (by transforming it into standard form, which is not always possible) and the computation of the eigenvectors. This part of the work has been applied to a real problem of simulation of oceanic flows. Here, it is necessary to compute all the eigenvalues and eigenvectors of a generalised eigenvalue problem with a very big dimension. As a consequence, enormous eigenvalue problems have been solved (with matrices of order greater than 400000), that could not be solved previously. Solving them allows to improve the precision in the studies of the behaviour of oceanic flows.[ES] En el campo de la teoría de control en ocasiones aparecen modelos de sistemas con un tamaño elevado (muchas variables de estado). Cuando se pretende simular, estudiar o controlar uno de estos sistemas de orden elevado, conviene realizar un trabajo previo de reducción del modelo del sistema con el propósito de reducir los costes (económicos/temporales) necesarios en un tratamiento posterior. El proceso de obtención de un sistema de orden reducido que represente adecuadamente el sistema original suele ser costoso, ya que necesariamente se tiene que hacer con el sistema original sin reducir. Por esto, resulta conveniente disponer de implementaciones de altas prestaciones para el problema de reducción de sistemas lineales de control. En esta tesis se han desarrollado implementaciones de altas prestaciones para algunos métodos de reducción de modelos. Se han analizado los algoritmos existentes para la reducción de modelos de sistemas lineales de control estables y sus implementaciones en la librería de control SLICOT. Se han propuesto nuevos algoritmos paralelos para los métodos cuyo núcleo principal es la resolución de ecuaciones de Lyapunov. Las nuevas rutinas desarrolladas se incorporan a la librería de computación de altas prestaciones para control PSLICOT. Aparte de las funciones principales a cargo de la reducción de modelos, se han tenido que paralelizar también todas aquellas operaciones numéricas que aparecen en este problema y de las que no se disponía de versiones de altas prestaciones. De estas operaciones, cabe destacar rutinas paralelas para la resolución de la ecuación de Lyapunov en su forma estándar obteniendo directamente el factor de Cholesky de la solución, que es lo que se necesita para la reducción de modelos. El método utilizado es una versión paralela del método de Hammarling. Las rutinas implementadas resuelven en paralelo y para matrices densas las cuatro variantes posibles de la ecuación de Lyapunov: en su forma discreta y continua, traspuestas y sin trasponer. Todas las rutinas paralelizadas ofrecen una interfaz como la de las rutinas de la librería ScaLAPACK, para que puedan ser usadas con facilidad por el usuario habituado a trabajar con este tipo de librerías. Se permiten las mismas distribuciones de datos que en esta librería: una distribución cíclica 2D por bloques, que engloba muchas otras distribuciones. Gracias al trabajo desarrollado, ahora se dispone de versiones paralelas de altas prestaciones para reducir sistemas lineales de control mediante diferentes variantes del método de balanceado y truncamiento de la raíz cuadrada (the Square-Root Balance & Truncate model reduction method): con o sin balanceado y con o sin usar las fórmulas de perturbación singular. Se trata de versiones paralelas de los mismos algoritmos y métodos que se utilizan en las versiones secuenciales de la librería SLICOT. Esto permitirá reducir de forma eficiente modelos de sistemas lineales de control de gran tamaño. También se ha mejorado la aplicabilidad del software existente en ScaLAPACK para el problema de valores propios cubriendo casos que no se contemplaban. Se ha trabajado en la solución del problema generalizado (mediante su transformación a forma estándar, lo que no es aplicable en todos los casos) y también en el cálculo de los vectores propios. Ambas operaciones se han utilizado en un problema real de simulación de flujos oceánicos. En esta aplicación se requiere el cálculo de todos los valores y vectores propios de un problema generalizado de gran dimensión. Como consecuencia, ha sido posible resolver problemas de valores propios generalizados enormes (con matrices de más de 400000 filas y columnas) que no habían podido resolverse con anterioridad, permitiendo así un estudio más preciso del comportamiento de las corrientes oceánicas.[CA] En el camp de la teoria de control de vegades apareixen models de sistemes amb un tamany elevat (moltes variables d'estat). Quan es pretén simular, estudiar o controlar un d'aquests sistemes d'ordre elevat, convé realitzar un treball previ de reducció del model del sistema amb el propòsit de reduir els costos (econòmics/temporals) necesaris en un tractament posterior. El procés d'obtenció d'un sistema d'ordre reduit que represente adequadament el sistema original sol ser costós, perque necessàriament ha de fer-se amb el sistema original sense reduir. Per aquest motiu, resulta convenient disposar d'implementacions d'altes prestacions per al problema de reducció de sistemes lineals de control. En aquesta tesis s'han desenvolupat implementacions d'altes prestacions per a alguns mètodes de reducció de models. S'han anal·litzat els algoritmes existents per a la reducció de models de sistemes lineals de control estables i les seues implementacions en la llibreria de control SLICOT. S'han proposat nous algoritmes paral·lels per als mètodes basats en la resolució d'equacions de Lyapunov. Les noves rutines desenvolupades s'incorporen a la llibreria de computació d'altes prestacions per a control PSLICOT. Apart de les funcions principals a càrrec de la reducció de models, s'han hagut de paral·le\-lit\-zar també totes aquelles operacions numèriques que apareixen en aquest problema i per a les que no es disposava de versions d'altes prestacions. D'aquestes operacions, destaquen rutines paral·leles per a la resolució de l'equació de Lyapunov en la seua forma estàndard obtenint directament el factor de Cholesky de la solució, que és el que es necessita per a la reducció de models. El mètode emprat és una versió paral·lela del mètode de Hammarling. Les rutines implementades resolen en paral·lel i per a matrius denses les quatre variants possibles de l'equació de Lyapunov: en la seua forma discreta i contínua, traspostes i sense trasposar. Totes les rutines paral·lelitzades ofereixen una interfaç com la de les rutines de la llibreria ScaLAPACK, per a que puguen ser usades fàcilment per l'usuari acostumat a treballar amb aquest tipus de llibreries. Es permeten les mateixes distribucions de dades que en aquesta llibreria: distribució cíclica 2D per blocs, que engloba moltes altres distribucions. Gràcies al treball desenvolupat, ara es disposa de versions paral·leles d'altes prestacions per a reduir sistemes lineals de control mitjançant diferents variants del mètode de balancejat i truncament de l'arrel quadrada (the Square-Root Balance & Truncate model reduction method): amb o sense balancejat i amb o sense usar les fórmules de perturbació singular. Son versions paral·leles dels mateixos algoritmes i mètodes que s'utilitzen en les versions sequencials de la llibreria SLICOT. Això permetrà reduir de forma eficient models de sistemes lineals de control de gran tamany. També s'ha mitjorat l'aplicabilitat del software existent en ScaLAPACK per al problema de valors propis cobrint casos que no es contemplaven. S'ha treballat en la solució del problema generalitzat (mitjançant la seua transformació a forma estàndard, cosa que no es pot fer sempre) i també en el càlcul dels vectors propis. Ambdues operacions s'han utilitzat en un problema real de simulació de fluxos oceànics. En aquesta aplicació es requereix el càlcul de tots els valors i vectors propis d'un problema generalitzat de gran dimensió. Com a conseqüència, ha sigut possible resoldre problemes de valors propis generalitzats molt grans (amb matrius de més de 400000 files i columnes) que no s'havien pogut resoldre anteriorment, permetent així un estudi més precís del comportament de les corrents oceàniques.Guerrero López, D. (2015). Algoritmos paralelos para la reducción de sistemas lineales de control estables [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59415TESI

Efficient Numerical Solution of Large Scale Algebraic Matrix Equations in PDE Control and Model Order Reduction

Matrix Lyapunov and Riccati equations are an important tool in mathematical systems theory. They are the key ingredients in balancing based model order reduction techniques and linear quadratic regulator problems. For small and moderately sized problems these equations are solved by techniques with at least cubic complexity which prohibits their usage in large scale applications. Around the year 2000 solvers for large scale problems have been introduced. The basic idea there is to compute a low rank decomposition of the quadratic and dense solution matrix and in turn reduce the memory and computational complexity of the algorithms. In this thesis efficiency enhancing techniques for the low rank alternating directions implicit iteration based solution of large scale matrix equations are introduced and discussed. Also the applicability in the context of real world systems is demonstrated. The thesis is structured in seven central chapters. After the introduction chapter 2 introduces the basic concepts and notations needed as fundamental tools for the remainder of the thesis. The next chapter then introduces a collection of test examples spanning from easily scalable academic test systems to badly conditioned technical applications which are used to demonstrate the features of the solvers. Chapter four and five describe the basic solvers and the modifications taken to make them applicable to an even larger class of problems. The following two chapters treat the application of the solvers in the context of model order reduction and linear quadratic optimal control of PDEs. The final chapter then presents the extensive numerical testing undertaken with the solvers proposed in the prior chapters. Some conclusions and an appendix complete the thesis