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

<i>H</i>₂ and mixed <i>H</i>₂/<i>H</i>_∞ Stabilization and Disturbance Attenuation for Differential Linear Repetitive Processes

Repetitive processes are a distinct class of two-dimensional systems (i.e., information propagation in two independent directions) of both systems theoretic and applications interest. A systems theory for them cannot be obtained by direct extension of existing techniques from standard (termed 1-D here) or, in many cases, two-dimensional (2-D) systems theory. Here, we give new results towards the development of such a theory in H2 and mixed H2/H∞ settings. These results are for the sub-class of so-called differential linear repetitive processes and focus on the fundamental problems of stabilization and disturbance attenuation

Second-order odd-harmonic repetitive control and its application to active filter control

High order repetitive control has been introduced toovercomeperformance decay of repetitive control systems undervarying frequency of the signals to be tracked/rejected orimproving the interhamonic behavior. However, most highorder repetitive internal models used to improve frequencyuncertainty are unstable, as a consequence practicalimplementations are more difficult. In this work a stable,second order odd-harmonic repetitive control system ispresented and studied.The proposed internal model has been implemented andvalidated in a shunt active filter current controller. Thishigh order controller allows dealing with the gridfrequency variations without using adaptive schemes

Multi-Objective Iterative Learning Control: An Advanced ILC Approach for Application Diversity.

While ILC has been applied to repetitive applications in manufacturing, chemical processing, and robotics, several key assumptions limit the extension of ILC to various applications. Conventional ILC focuses on improving the performance of a single metric, such as tracking performance through iterative updates of the time domain control input. The application range is limited to systems that satisfy the assumption of iteration invariance of the plant, reference signal, initial conditions, and disturbances. We aim to relax this assumption to gain significant advantages. More specifically we focus on relaxing the strict reference tracking requirement to address multiple performance metrics and define the stability bounds across temporal and spatial domains. The aim of this research is expanding the application space of ILC towards non-traditional applications. Chapter III presents an initial framework to provide the foundation for the multi-objective ILC. This framework is validated by simulation and experimental tests with a wheeled mobile robot. Chapter IV extends the initial framework from the temporal domain to the spatial domain. The initial framework is generalized to address four classifications of performance objectives. Stability and performance analysis for each classification is provided. Simulation results on a high-resolution additive manufacturing system validate the extended framework. For the generalized framework, we present a distributed approach in which additional objectives are considered separately. Chapter V evaluates the difference between this distributed approach, and a centralized approach in which the objectives are combined into a single matrix depending on the classification. Chapter VI extends the multi-objective ILC to incorporate a region-based tracking problem in which reference uncertainty is addressed through the development of a bounded region. A multi-objective region-to-region ILC is developed and validated by a simulation of a surveillance problem with an UAV and multiple unattended ground sensors. Comparisons with point-to-point ILC, region-to-region ILC, and multi-objective region-based ILC demonstrate the performance flexibility that can be achieved when leveraging the regions. This dissertation provides new approaches for relaxing the classical assumption of iteration invariant reference tracking. New stability and convergence analysis is provided, resulting in a design methodology for multi-objective ILC. These approaches are validated by simulation and experimental results.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120875/1/ingyulim_1.pd

Software reliability and dependability: a roadmap

Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t

Optimized Quantification of Spin Relaxation Times in the Hybrid State

Purpose: The analysis of optimized spin ensemble trajectories for relaxometry in the hybrid state. Methods: First, we constructed visual representations to elucidate the differential equation that governs spin dynamics in hybrid state. Subsequently, numerical optimizations were performed to find spin ensemble trajectories that minimize the Cram\'er-Rao bound for $T_1$-encoding, $T_2$-encoding, and their weighted sum, respectively, followed by a comparison of the Cram\'er-Rao bounds obtained with our optimized spin-trajectories, as well as Look-Locker and multi-spin-echo methods. Finally, we experimentally tested our optimized spin trajectories with in vivo scans of the human brain. Results: After a nonrecurring inversion segment on the southern hemisphere of the Bloch sphere, all optimized spin trajectories pursue repetitive loops on the northern half of the sphere in which the beginning of the first and the end of the last loop deviate from the others. The numerical results obtained in this work align well with intuitive insights gleaned directly from the governing equation. Our results suggest that hybrid-state sequences outperform traditional methods. Moreover, hybrid-state sequences that balance $T_1$- and $T_2$-encoding still result in near optimal signal-to-noise efficiency. Thus, the second parameter can be encoded at virtually no extra cost. Conclusion: We provide insights regarding the optimal encoding processes of spin relaxation times in order to guide the design of robust and efficient pulse sequences. We find that joint acquisitions of $T_1$ and $T_2$ in the hybrid state are substantially more efficient than sequential encoding techniques.Comment: 10 pages, 5 figure

CPU Resource Management and Noise Filtering for PID Control

The first part of the thesis deals with adaptive CPU resource management for multicore platforms. The work was done as a part of the resource manager component of the adaptive resource management framework implemented in the European ACTORS project. The framework dynamically allocates CPU resources for the applications. The key element of the framework is the resource manager that combines feedforward and feedback algorithms together with reservation techniques. The resource requirements of the applications are provided through service level tables. Dynamic bandwidth allocation is performed by the resource manager which adapts applications to changes in resource availability, and adapts the resource allocation to changes in application requirements. The dynamic bandwidth allocation allows to obtain real application models through the tuning and update of the initial service level tables. The second part of the thesis deals with the design of measurement noise filters for PID control. The design is based on an iterative approach to calculate the filter time constant, which requires the information in terms of an FOTD model of the process. Tuning methods such as Lambda, SIMC, and AMIGO are used to obtain the controller parameters. New criteria based on the trade-offs between performance, robustness, and attenuation of measurement noise are proposed for assessment of the design. Simple rules for calculating the filter time constant based on the nominal process model and the nominal controller are then derived, thus, eliminating the need for iteration. Finally, a complete tuning procedure is proposed. The tuning procedure accounts for the effects of filtering in the nominal process. Hence, the added dynamics are included in the filtered process model, which is then used to recalculate the controller tuning parameters

Human capital and innovation:the importance of the optimal organizational task structure

Management literature has identified high-skilled human capital as a crucial dimension of innovation processes at the firm level. In this study, we introduce an alternative view of human capital based on the tasks that firms’ workers perform. We propose a measure of cognitive analytical and interpersonal tasks: the degree of abstractism. We argue that the level of abstractism of a firm has an effect on a firm's propensity to innovate and on its product innovation performance. We hypothesize that while the degree of abstractism has a linear positive relationship with the propensity to innovate, the relationship between abstractism and product innovation performance follows an inverted u-shaped relationship. We find partial support to our hypotheses using data from more than six thousand Portuguese firms. We discuss how these results change our understanding of the relationship between human capital and innovation at the firm level

MORA - an architecture and programming model for a resource efficient coarse grained reconfigurable processor

This paper presents an architecture and implementation details for MORA, a novel coarse grained reconfigurable processor for accelerating media processing applications. The MORA architecture involves a 2-D array of several such processors, to deliver low cost, high throughput performance in media processing applications. A distinguishing feature of the MORA architecture is the co-design of hardware architecture and low-level programming language throughout the design cycle. The implementation details for the single MORA processor, and benchmark evaluation using a cycle accurate simulator are presented

Adaptive Compensation Strategy For The Tracking/Rejection of Signals with Time-Varying Frequency in Digital Repetitive Control Systems

Digital repetitive control is a technique which al- lows to track periodic references and/or reject peri- odic disturbances. Repetitive controllers are usually de- signed assuming a fixed frequency for the signals to be tracked/rejected, its main drawback being a dramatic per- formance decay when this frequency varies. A usual ap- proach to overcome the problem consists of an adap- tive change of the sampling time according to the refer- ence/disturbance period variation. However, this sam- pling period adaptation implies parametric changes af- fecting the closed-loop system behavior, that may compro- mise the system stability. This article presents a design strategy which allows to compensate for the parametric changes caused by sampling period adjustment. Stabil- ity of the digital repetitive controller working under time- varying sampling period is analyzed. Theoretical devel- opments are illustrated with experimental results.Peer ReviewedPostprint (published version