LMI approach to mixed performance objective controllers: application to Robust ℋ2 Synthesis

The problem of synthesizing a controller for plants subject to arbitrary, finite energy disturbances and white noise disturbances via Linear Matrix Inequalities (LMIs) is presented. This is achieved by considering white noise disturbances as belonging to a constrained set in ℓ2. In the case of where only white noise disturbances are present, the procedure reduces to standard ℋ2 synthesis. When arbitrary, finite energy disturbances are also present, the procedure may be used to synthesize general mixed performance objective controllers, and for certain cases, Robust ℋ2 controllers

Optimal Output Modification and Robust Control Using Minimum Gain and the Large Gain Theorem

When confronted with a control problem, the input-output properties of the system to be controlled play an important role in determining strategies that can or should be applied, as well as the achievable closed-loop performance. Optimal output modification is a process in which the system output is modified in such a manner that the modified system has a desired input-output property and the modified output is as similar as possible to a specified desired output. The first part of this dissertation develops linear matrix inequality (LMI)-based optimal output modification techniques to render a linear time-invariant (LTI) system minimum phase using parallel feedforward control or strictly positive real by linearly interpolating sensor measurements. H-ininifty-optimal parallel feedforward controller synthesis methods that rely on the input-output system property of minimum gain are derived and tested on a numerical example. The H2- and H-infinity-optimal sensor interpolation techniques are implemented in numerical simulations of noncolocated elastic mechanical systems. All mathematical models of physical systems are, to some degree, uncertain. Robust control can provide a guarantee of closed-loop stability and/or performance of a system subject to uncertainty, and is often performed using the well-known Small Gain Theorem. The second part of this dissertation introduces the lessor-known Large Gain Theorem and establishes its use for robust control. A proof of the Large Gain Theorem for LTI systems using the familiar Nyquist stability criterion is derived, with the goal of drawing parallels to the Small Gain Theorem and increasing the understanding and appreciation of this theorem within the control systems community. LMI-based robust controller synthesis methods using the Large Gain Theorem are presented and tested numerically on a robust control benchmark problem with a comparison to H-infinity robust control. The numerical results demonstrate the practicality of performing robust control with the Large Gain Theorem, including its ability to guarantee an uncertain closed-loop system is minimum phase, which is a robust performance problem that previous robust control techniques could not solve.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143934/1/caverly_1.pd

Design of Low-Order Controllers using Optimization Techniques

In many applications, especially in the process industry, low-level controllers are the workhorses of the automated production lines. The aim of this study has been to provide simple tuning procedures, either optimization-based methods or tuning rules, for design of low-order controllers. The first part of this thesis deals with PID tuning. Design methods or both SISO and MIMO PID controllers based on convex optimization are presented. The methods consist of solving a nonconvex optimization problem by deriving convex approximations of the original problem and solving these iteratively until convergence. The algorithms are fast because of the convex approximations. The controllers obtained minimize low-frequency sensitivity subject to constraints that ensure robustness to process variations and limitations of control signal effort. The second part of this thesis deals with tuning of feedforward controllers. Tuning rules that minimize the integrated-squared-error arising from measurable step disturbances are derived for a controller that can be interpreted as a filtered and possibly time-delayed PD controller. Using a controller structure that decouples the effects of the feedforward and feedback controllers, the controller is optimal both in open and closed loop settings. To improve the high-frequency noise behavior of the feedforward controller, it is proposed that the optimal controller is augmented with a second-order filter. Several aspects on the tuning of this filter are discussed. For systems with PID controllers, the response to step changes in the reference can be improved by introducing set-point weighting. This can be interpreted as feedforward from the reference signal to the control signal. It is shown how these weights can be found by solving a convex optimization problem. Proportional set-point weight that minimizes the integrated-absolute-error was obtained for a batch of over 130 different processes. From these weights, simple tuning rules were derived and the performance was evaluated on all processes in the batch using five different feedback controller tuning methods. The proposed tuning rules could improve the performance by up to 45% with a modest increase in actuation

Contributions to impedance shaping control techniques for power electronic converters

El conformado de la impedancia o admitancia mediante control para convertidores electrónicos de potencia permite alcanzar entre otros objetivos: mejora de la robustez de los controles diseñados, amortiguación de la dinámica de la tensión en caso de cambios de carga, y optimización del filtro de red y del controlador en un solo paso (co-diseño). La conformación de la impedancia debe ir siempre acompañada de un buen seguimiento de referencias. Por tanto, la idea principal es diseñar controladores con una estructura sencilla que equilibren la consecución de los objetivos marcados en cada caso. Este diseño se realiza mediante técnicas modernas, cuya resolución (síntesis del controlador) requiere de herramientas de optimización. La principal ventaja de estas técnicas sobre las clásicas, es decir, las basadas en soluciones algebraicas, es su capacidad para tratar problemas de control complejos (plantas de alto orden y/o varios objetivos) de una forma considerablemente sistemática. El primer problema de control por conformación de la impedancia consiste en reducir el sobreimpulso de tensión ante cambios de carga y minimizar el tamaño de los componentes del filtro pasivo en los convertidores DC-DC. Posteriormente, se diseñan controladores de corriente y tensión para un inversor DC-AC trifásico que logren una estabilidad robusta del sistema para una amplia variedad de filtros. La condición de estabilidad robusta menos conservadora, siendo la impedancia de la red la principal fuente de incertidumbre, es el índice de pasividad. En el caso de los controladores de corriente, el impacto de los lazos superiores en la estabilidad basada en la impedancia también se analiza mediante un índice adicional: máximo valor singular. Cada uno de los índices se aplica a un rango de frecuencias determinado. Finalmente, estas condiciones se incluyen en el diseño en un solo paso del controlador de un convertidor back-to-back utilizado para operar generadores de inducción doblemente alimentados (aerogeneradores tipo 3) presentes en algunos parques eólicos. Esta solución evita los problemas de oscilación subsíncrona, derivados de las líneas de transmisión con condensadores de compensación en serie, a los que se enfrentan estos parques eólicos. Los resultados de simulación y experimentales demuestran la eficacia y versatilidad de la propuesta.Impedance or admittance shaping by control for power electronic converters allows to achieve among other objectives: robustness enhancement of the designed controls, damped voltage dynamics in case of load changes, and grid filter and controller optimization in a single step (co-design). Impedance shaping must always be accompanied by a correct reference tracking performance. Therefore, the main idea is to design controllers with a simple structure that balance the achievement of the objectives set in each case. This design is carried out using modern techniques, whose resolution (controller synthesis) requires optimization tools. The main advantage of these techniques over the classical ones, i.e. those based on algebraic solutions, is their ability to deal with complex control problems (high order plants and/or several objectives) in a considerably systematic way. The first impedance shaping control problem is to reduce voltage overshoot under load changes and minimize the size of passive filter components in DC-DC converters. Subsequently, current and voltage controllers for a three-phase DC-AC inverter are designed to achieve robust system stability for a wide variety of filters. The least conservative robust stability condition, with grid impedance being the main source of uncertainty, is the passivity index. In the case of current controllers, the impact of higher loops on impedance-based stability is also analyzed by an additional index: maximum singular value. Each of the indices is applied to a given frequency range. Finally, these conditions are included in the one-step design of the controller of a back-to-back converter used to operate doubly fed induction generators (type-3 wind turbines) present in some wind farms. This solution avoids the sub-synchronous oscillation problems, derived from transmission lines with series compensation capacitors, faced by these wind farms. Simulation and experimental results demonstrate the effectiveness and versatility of the proposa

Application of Mixed-Norm Optimal Control to a Multi-Objective Active Suspension Problem

Mixed norm optimal control synthesis is used to solve a multiobjective suspension problem. The objective is to develop a controller for an active suspension system onboard a tractor semitrailer vehicle. The problem is first approached by using H2 and H∞ optimization. It is shown that by combining both techniques into one mixed norm optimization method, it is possible to exploit the strengths of each norm to provide superior performance. Two H2/H∞ designs were completed. One design concentrated on optimal performance at one design condition. The second design concentrated on providing the best performance possible at a medium load configuration, while maintaining robust stability at the extreme load configurations

Relaxing Fundamental Assumptions in Iterative Learning Control

Iterative learning control (ILC) is perhaps best decribed as an open loop feedforward control technique where the feedforward signal is learned through repetition of a single task. As the name suggests, given a dynamic system operating on a finite time horizon with the same desired trajectory, ILC aims to iteratively construct the inverse image (or its approximation) of the desired trajectory to improve transient tracking. In the literature, ILC is often interpreted as feedback control in the iteration domain due to the fact that learning controllers use information from past trials to drive the tracking error towards zero. However, despite the significant body of literature and powerful features, ILC is yet to reach widespread adoption by the control community, due to several assumptions that restrict its generality when compared to feedback control. In this dissertation, we relax some of these assumptions, mainly the fundamental invariance assumption, and move from the idea of learning through repetition to two dimensional systems, specifically repetitive processes, that appear in the modeling of engineering applications such as additive manufacturing, and sketch out future research directions for increased practicality: We develop an L1 adaptive feedback control based ILC architecture for increased robustness, fast convergence, and high performance under time varying uncertainties and disturbances. Simulation studies of the behavior of this combined L1-ILC scheme under iteration varying uncertainties lead us to the robust stability analysis of iteration varying systems, where we show that these systems are guaranteed to be stable when the ILC update laws are designed to be robust, which can be done using existing methods from the literature. As a next step to the signal space approach adopted in the analysis of iteration varying systems, we shift the focus of our work to repetitive processes, and show that the exponential stability of a nonlinear repetitive system is equivalent to that of its linearization, and consequently uniform stability of the corresponding state space matrix.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133232/1/altin_1.pd

Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate physical models for these plants using first principles may be impossible. With the increased developments in the computing world, large amounts of measured data can be easily collected and stored for processing purposes. Data can also be collected and used in an on-line fashion. Thus it would be very sensible to make full use of this data for controller design, performance evaluation, and stability analysis. The design methods imposed in this work ensure that the dynamics of a system are captured in an experiment and avoids the problem of unmodeled dynamics associated with parametric models. The devised methods consider robust designs for both linear-time-invariant (LTI) single-input-single-output (SISO) systems and certain classes of nonlinear systems. In this dissertation, a data-driven approach using the frequency response function of a system is proposed for designing robust controllers with H∞ performance. Necessary and sufficient conditions are derived for obtaining H∞ performance while guaranteeing the closed-loop stability of a system. A convex optimization algorithm is implemented to obtain the controller parameters which ensure system robustness; the controller is robust with respect to the frequency-dependent uncertainties of the frequency response function. For a certain class of nonlinearities, the proposed method can be used to obtain a best-linear-approximation with an associated frequency dependent uncertainty to guarantee the stability and performance for the underlying linear system that is subject to nonlinear distortions. The concepts behind these design methods are then used to devise necessary and sufficient conditions for ensuring the closed-loop stability of systems with sector-bounded nonlinearities. The conditions are simple convex feasibility constraints which can be used to stabilize systems with multi-model uncertainty. Additionally, a method is proposed for obtaining H∞ performance for an approximate model (i.e., describing function) of a sector-bounded nonlinearity. This work also proposes several data-driven methods for designing robust fixed-structure controllers with H∞ performance. One method considers the solution to a non-convex problem, while another method convexifies the problem and implements an iterative algorithm to obtain the local solution (which can also consider H2 performance). The effectiveness of the proposed method(s) is illustrated by considering several case studies that require robust controllers for achieving the desired performance. The main applicative work in this dissertation is with respect to a power converter control system at the European Organization for Nuclear Research (CERN) (which is used to control the current in a magnet to produce the desired field in controlling particle trajectories in accelerators). The proposed design methods are implemented in order to satisfy the challenging performance specifications set by the application while guaranteeing the system stability and robustness using data-driven design strategies