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

Iterative learning fault-tolerant control for differential time-delay batch processes in finite frequency domains

This paper develops a fault-tolerant iterative learning control law for a~class of~linear time-delay differential batch processes with actuator faults using the repetitive process setting. Once the dynamics are expressed in this setting, stability analysis and control law design makes use of the generalized Kalman-Yakubovich-Popov (KYP) lemma in the form of the corresponding linear matrix inequalities (LMIs). In particular, sufficient conditions for the existence of a fault-tolerant control law are developed together with design algorithms for the associated matrices. Under the action of this control law the ILC dynamics have a monotonicity property in terms of an error sequence formed from the difference between the supplied reference trajectory and the outputs produced. An extension to robust control against structured time-varying uncertainties is also developed. Finally, a simulation based case study on the model of a~two-stage chemical reactor with delayed recycle is given to demonstrate the feasibility and effectiveness of the new designs

Measurements, Models, Systems and Design

531 s.

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

DESIGN AND CONVERGENCE OF A TIME-VARYING ITERATIVE LEARNING CONTROL LAW

ABSTRACT This paper presents a novel linear time-varying (LTV) iterative learning control law that can provide additional performance while maintaining the robustness and convergence properties comparable to those obtained using traditional frequency domain design techniques. Design aspects of causal and non-causal linear time-invariant (LTI), along with the proposed LTV, ILC update laws are discussed and demonstrated using a simplified example. Asymptotic as well as monotonic convergence, robustness and performance characteristics of such systems are considered, and an equivalent condition to the frequency domain convergence condition is presented for the time-varying ILC. Lastly the ILC algorithm developed here is implemented on a Microscale Robotic Deposition system to provide experimental verification. Markov parameters of Q, L, P INTRODUCTION There is a large class of control applications where the same task is performed repeatedly. An obvious example is robotic applications where the control objective is to track the same command over and over. Typically, in this type of situation, the system would also encounter the same disturbance and nonlinear effects each time. Under these conditions, traditional feedback controllers would generate the same set of errors at each recurrence of the control task. It is obvious that performance of such systems can be improved significantly by using a controller that learns from the previous experience. Ideally, one would expect that a good controller of this type should generate a control effort that improves the system's performance with each iteration. Another natural notion is that such a controller would generate high frequency control effort when the reference (and/or disturbance) has significant high frequency components and the control effort would be of low frequency when the reference/disturbance are relatively constant. This paper combines these three ideas in proposing a timevarying Iterative Learning Control (ILC) law that can improve the performance of a given system, while retaining good robustness properties, by tuning the control law for the system's reference/disturbance signals. This type of a control law is especially beneficial for systems with localized hard nonlinearities such as stiction, or systems with reference trajectories and/or disturbances that have significant variation in their frequency content during different portions of the period. Monotonic convergence conditions are presented to check when such a design results in improved control with each iteration. It is assumed that the control is implemented digitally, and therefore all analysis and modeling is performed in the discretetime domain. The general linear ILC structure and associate

Application of soft computing techniques to estimate the effective Young\u27s Modulus of thin film materials

This research aims at characterizing and predicting the Young’s Modulus of thin film materials that are utilized in Microelectromechanical systems (MEMS). Recent studies indicate that the mechanical properties such as Young’s Modulus of thin films are significantly different from the bulk values. Due to the lack of proper understanding of the physics in the micro-scale domain the state-of-art estimation techniques are unreliable and often unfit for use for predicating the mechanical behavior of slight modifications of existing designs as well as new designs. This disadvantage limits the MEMS designers to physical prototyping which is cost ineffective and time consuming. As a result there is an immediate need for alternative techniques that can learn the complex relationship between the various parameters and predict the effective Young’s Modulus of the thin films materials. The proposed technique attempts to solve this problem using empirical estimation techniques that utilize soft computing techniques for the estimation as well as the prediction of the effective Young’s Modulus. As a proof of concept, effective Young’s Modulus of Aluminum and TetrathylOrthoSilicate (TEOS) thin films were computed by fabricating and analyzing self-deformed micromachined bilayer cantilevers. In the estimation phase, 2D search and micro Genetic algorithm were studied and in the prediction phase, back propagation based Neural networks and One Dimensional Radial Basis Function Networks (1D-RBFN) were studied. The performance of all combinations of these soft computing techniques is studied. Based on the results, we conclude that performance of the soft computing techniques is superior to the existing methods. In addition, the effective values generated using this methodology is comparable to the values reported in the literature. Given a finite number of data samples, the combination of 1D-RBFN (prediction phase) and GA (estimation phase) presented the best results. Due to these advantages, this methodology is foreseen to be an essential tool for developing accurate models that can estimate the mechanical behavior of thin films

Building a Strong Undergraduate Research Culture in African Universities

Africa had a late start in the race to setting up and obtaining universities with research quality fundamentals. According to Mamdani [5], the first colonial universities were few and far between: Makerere in East Africa, Ibadan and Legon in West Africa. This last place in the race, compared to other continents, has had tremendous implications in the development plans for the continent. For Africa, the race has been difficult from a late start to an insurmountable litany of problems that include difficulty in equipment acquisition, lack of capacity, limited research and development resources and lack of investments in local universities. In fact most of these universities are very recent with many less than 50 years in business except a few. To help reduce the labor costs incurred by the colonial masters of shipping Europeans to Africa to do mere clerical jobs, they started training ―workshops‖ calling them technical or business colleges. According to Mamdani, meeting colonial needs was to be achieved while avoiding the ―Indian disease‖ in Africa -- that is, the development of an educated middle class, a group most likely to carry the virus of nationalism. Upon independence, most of these ―workshops‖ were turned into national ―universities‖, but with no clear role in national development. These national ―universities‖ were catering for children of the new African political elites. Through the seventies and eighties, most African universities were still without development agendas and were still doing business as usual. Meanwhile, governments strapped with lack of money saw no need of putting more scarce resources into big white elephants. By mid-eighties, even the UN and IMF were calling for a limit on funding African universities. In today‘s African university, the traditional curiosity driven research model has been replaced by a market-driven model dominated by a consultancy culture according to Mamdani (Mamdani, Mail and Guardian Online). The prevailing research culture as intellectual life in universities has been reduced to bare-bones classroom activity, seminars and workshops have migrated to hotels and workshop attendance going with transport allowances and per diems (Mamdani, Mail and Guardian Online). There is need to remedy this situation and that is the focus of this paper

Dissipative stability theory for linear repetitive processes with application in iterative learning control

Abstract-This paper develops a new set of necessary and sufficient conditions for the stability of linear repetitive processes, based on a dissipative setting for analysis. These conditions reduce the problem of determining whether a linear repetitive process is stable or not to that of checking for the existence of a solution to a set of linear matrix inequalities (LMIs). Testing the resulting conditions only requires computations with matrices whose entries are constant in comparison to alternatives where frequency response computations are required