The problem of finding a reduced order model, optimal in the H² sense, to a given system model is a fundamental one in control system analysis and design. The addition of a H^∞ constraint to the H² optimal model reduction problem results in a more practical yet computationally more difficult problem. Without the global convergence of homotopy methods, both the H² optimal and the combined H²/H^∞ model reduction problems are very difficult. For both problems homotopy algorithms based on several formulations—input normal form; Ly, Bryson, and Cannon's 2 X 2 block parametrization; a new nonminimal parametrization—are developed and compared here. For the H² optimal model order reduction problem, these numerical algorithms are also compared with that based on Hyland and Bernstein's optimal projection equations. Both the input normal form and Ly form are very efficient compared to the over parametrization formulation and the optimal projection equations approach, since they utilize the minimal number of possible degrees of freedom. However, they can fail to exist or be very ill conditioned. The conditions under which the input normal form and the Ly form become ill conditioned are examined. The over-parametrization formulation solves the ill conditioning issue, and usually is more efficient than the approach based on solving the optimal projection equations for the H² optimal model reduction problem. However, the over-parametrization formulation introduces a very high order singularity at the solution, and it is doubtful whether this singularity can be overcome by using interpolation or other existing methods. Although there are numerous algorithms for solving Riccati equations, there still remains a need for algorithms which can operate efficiently on large problems and on parallel machines and which can be generalized easily to solve variants of Riccati equations. This thesis gives a new homotopy-based algorithm for solving Riccati equations on a shared memory parallel computer. The central part of the algorithm is the computation of the kernel of the Jacobian matrix, which is essential for the corrector iterations along the homotopy zero curve. Using a Schur decomposition the tensor product structure of various matrices can be efficiently exploited. The algorithm allows for efficient parallelization on shared memory machines. The linear-quadratic-Gaussian (LQG) theory has engendered a systematic approach to synthesize high performance controllers for nominal models of complex, multi-input multioutput systems and hence it is a breakthrough in modern control theory. Homotopy algorithms for both full and reduced-order LQG controller design problems with an H^∞ constraint on disturbance attenuation are developed. The H^∞ constraint is enforced by replacing the covariance Lyapunov equation by a Riccati equation whose solution gives an upper bound on H² performance. The numerical algorithm, based on homotopy theory, solves the necessary conditions for a minimum of the upper bound on H² performance. The algorithms are based on two minimal parameter formulations: Ly, Bryson, and Cannon's 2 X 2 block parametrization and the input normal Riccati form parametrization. An over-parametrization formulation is also proposed. Numerical experiments suggest that the combination of a globally convergent homotopy method with a minimal parameter formulation applied to the upper bound minimization gives excellent results for mixed-norm synthesis. / Ph. D.

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<p>In order to design robust controllers, H-infinity synthesis is a common tool to use. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the plant, the problem is no longer convex and it is then relatively hard to solve. These problems become very complex, even when the order of the system to be controlled is low.</p><p>The approach used in the thesis is based on formulating the constraint on the maximum order of the plant as a polynomial equation. By using the fact that the polynomial is non-negative on the feasible set, the problem is reformulated as an optimization problem where the nonconvex polynomial function is to be minimized over a convex set defined by linear matrix inequalities.</p><p>To solve this optimization problem, two methods have been proposed. The first method is a barrier method and the second one is a method based on a primal-dual framework. These methods have been evaluated on several problems and compared with a well-known method found in the literature. To motivate this choice of method, we have made a brief survey of available methods available for solving the same or related problems.</p><p>The proposed methods emerged as the best methods among the three for finding lower order controllers with the same or similar performance as the full order controller. When the aim is to find the lowest order controller with no worse than +50% increase in the closed loop H-infinity norm, then the three compared methods perform equally well.</p>

<p>Dynamiken för en starkt accelerande robot har modellerats. Modellen linjäriseras så att roboten beskrivs som ett linjärt tidsvariabelt system. Denna representation beskriver roboten väl då robotens anblåsningsvinkel, vilket är vinkeln mellan robotkroppen och robotens hastighet, är liten. Eftersom det ej är möjligt att mäta alla robotens tillstånd har en observatör tagits fram i form av ett Kalmanfilter. Problematik vid framtagandet av observatören diskuteras i rapporten. Den linjära tidsvariabla modellen har använts till att ta fram två regulatorer. En LQ-regulator och en H_∞-regulator. Hur dessa tas fram och vilka problem som finns diskuteras i rapporten. För att kunna se fördelar och nackdelar beträffande prestanda och robusthet har en mängd tester gjort. Testerna visar på olika fördelar hos de olika reglersystemen. Till exempel är det lättare att få bra prestanda med LQ-regulatorn än H_∞-regulatorn om systemet som styrs stämmer bra överens med systemet som använts vid reglerdesignen. H_∞-regulatorn har bättre förmåga att anpassa sig till modellförändringar givet att observatören gör bra skattningar. Det är dock svårt att utnämna en generell vinnare.</p>

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 135-140).

In order to design robust controllers, H-infinity synthesis is a common tool to use. The controllers that result from these algorithms are typically of very high order, which complicates implementation. However, if a constraint on the maximum order of the controller is set, that is lower than the order of the plant, the problem is no longer convex and it is then relatively hard to solve. These problems become very complex, even when the order of the system to be controlled is low. The approach used in the thesis is based on formulating the constraint on the maximum order of the plant as a polynomial equation. By using the fact that the polynomial is non-negative on the feasible set, the problem is reformulated as an optimization problem where the nonconvex polynomial function is to be minimized over a convex set defined by linear matrix inequalities. To solve this optimization problem, two methods have been proposed. The first method is a barrier method and the second one is a method based on a primal-dual framework. These methods have been evaluated on several problems and compared with a well-known method found in the literature. To motivate this choice of method, we have made a brief survey of available methods available for solving the same or related problems. The proposed methods emerged as the best methods among the three for finding lower order controllers with the same or similar performance as the full order controller. When the aim is to find the lowest order controller with no worse than +50% increase in the closed loop H-infinity norm, then the three compared methods perform equally well.

This thesis is divided in to two different parts. The first part includes examination of the measurementsystem of an industrial robot using a resolver sensor. The main focus is on methods for suppressing noise in the angularvelocity signal without increasing timedelay. Five different methods are investigated. Three of these are based on oversampling: burstsamplingmethod, meanvaluemethod and correlationmethod. The meanvaluemethod and the correlationmethod have given good results. The two other methods are: extended kalmanfiltering and computation of the angular velocity without using numeric method to compute the angle. Extended kalmanfilter gives the overall best result. In part two the control structure design of an industrial robot has been studied and how different sampling times and motorinertias affect the disturbance rejection and stability of the control loop. Different control structure designs have also been studied with the aim to suppress disturbances. Mainly H-infinity and GIMC designs have been compared with an ordinary PID controller. Here it can be shown that both the H-infinity and the GIMC controller yields good disturbance rejection. But both the methods lack in the robustness of model uncertainty.

The objective of adaptive control is to design a controller that can adjust its behaviour to tolerate uncertain or time-varying parameters. An adaptive controller typically consists of a linear time-invariant (LTI) compensator together with a tuning mechanism which adjusts the compensator parameters and yields a nonlinear controller. Because of the nonlinearity, the transient closed-loop behaviour is often poor and the control signal may become unduly large. Although the initial objective of adaptive control was to deal with time-varying plant parameters, most classical adaptive controllers cannot handle rapidly changing parameters. Recently, the use of a linear periodic (LP) controller has been proposed as a new approach in the field of model reference adaptive control [1]. In this new approach, instead of estimating plant parameters, the “ideal control signal” (what the control signal would be if the plant parameters and states were measurable) is estimated. The resulting controller has a number of desirable features: (1) it handles rapid changes in the plant parameters, (2) it provides nice transient behaviour of the closed-loop system, (3) it guarantees that the effect of the initial conditions declines to zero exponentially, and (4) it generates control signals which are modest in size. Although the linear periodic controller (LPC) has the above advantages, it has some imperfections. In order to achieve the desirable features, a rapidly varying control signal and a small sampling period are used. The rapidly time-varying control signal requires fast actuators which may not be practical. The second weakness of the LPC [1] is poor noise rejection behaviour. The small sampling period results in large controller gains and correspondingly poor noise sensitivity, since there is a clear trade-off between tracking and noise tolerance. As the last drawback, this controller requires knowledge of the exact plant relative degree. Here we extend this work in several directions: (i) In [1], the infinity-norm is used to measure the signal size. Here we redesign the controller to yield a new version which provides comparable results when the more common 2-norm is used to measure signal size, (ii) A key drawback of the controller of [1] is that the control signal moves rapidly. Here we redesign the control law to significantly alleviate this problem, (iii) The redesigned controller can handle large parameter variation and in the case that the sign of high frequency gain is known, the closed-loop system is remarkably noise-tolerant, (iv) We prove that in an important special case, we can replace the requirement of knowledge of the exact relative degree with that of an upper bound on the relative degree, at least from the point of view of providing stability, and (v) A number of approaches to improve the noise behaviour of the controller are presented. Reference: [1] D. E. Miller, “A New Approach to Model Reference Adaptive Control”, IEEE Transaction on Automatic Control, Vol. 48, No. 5, pages 743-756, May 2003.

Dynamiken för en starkt accelerande robot har modellerats. Modellen linjäriseras så att roboten beskrivs som ett linjärt tidsvariabelt system. Denna representation beskriver roboten väl då robotens anblåsningsvinkel, vilket är vinkeln mellan robotkroppen och robotens hastighet, är liten. Eftersom det ej är möjligt att mäta alla robotens tillstånd har en observatör tagits fram i form av ett Kalmanfilter. Problematik vid framtagandet av observatören diskuteras i rapporten. Den linjära tidsvariabla modellen har använts till att ta fram två regulatorer. En LQ-regulator och en H∞-regulator. Hur dessa tas fram och vilka problem som finns diskuteras i rapporten. För att kunna se fördelar och nackdelar beträffande prestanda och robusthet har en mängd tester gjort. Testerna visar på olika fördelar hos de olika reglersystemen. Till exempel är det lättare att få bra prestanda med LQ-regulatorn än H∞-regulatorn om systemet som styrs stämmer bra överens med systemet som använts vid reglerdesignen. H∞-regulatorn har bättre förmåga att anpassa sig till modellförändringar givet att observatören gör bra skattningar. Det är dock svårt att utnämna en generell vinnare.