Feedback linearization of nonlinear differential-algebraic control systems

In this paper, we study feedback linearization problems for nonlinear differential-algebraic control systems (DACSs). We consider two kinds of feedback equivalences, namely, the external feedback equivalence, which is defined (locally) on the whole generalized state space, and the internal feedback equivalence, which is defined on the locally maximal controlled invariant submanifold (i.e., on the set where solutions exist). Necessary and sufficient conditions are given for the locally internal and the locally external feedback linearizability of DACSs with the help of a notion called the explicitation with driving variables, which attaches a class of ordinary differential equation control systems (ODECSs) to a given DACS. We show that the feedback linearizability of a DACS is closely related to the involutivity of the linearizability distributions of the explicitation systems. Finally, we apply our results of feedback linearization of DACSs to an academical example and a constrained mechanical system.Comment: 20 page

Novel techniques for the design and practical realization of switched-capacitor circuits in deep-submicron CMOS technologies

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaSwitches presenting high linearity are more and more required in switched-capacitor circuits,namely in 12 to 16 bits resolution analog-to-digital converters. The CMOS technology evolves continuously towards lower supply voltages and, simultaneously, new design techniques are necessary to fulfill the realization of switches exhibiting a high dynamic range and a distortion compatible with referred resolutions. Moreover, with the continuously downing of the sizes, the physic constraints of the technology must be considered to avoid the excessive stress of the devices when relatively high voltages are applied to the gates. New switch-linearization techniques, with high reliability, must be necessarily developed and demonstrated in CMOS integrated circuits. Also, the research of new structures of circuits with switched-capacitor is permanent. Simplified and efficient structures are mandatory, adequate to the new demands emerging from the proliferation of portable equipments, necessarily with low energy consumption while assuring high performance and multiple functions. The work reported in this Thesis comprises these two areas. The behavior of the switches under these new constraints is analyzed, being a new and original solution proposed, in order to maintain the performance. Also, proposals for the application of simpler clock and control schemes are presented, and for the use of open-loop structures and amplifiers with localfeedback. The results, obtained in laboratory or by simulation, assess the feasibility of the presented proposals

Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 1: Modeling

The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of different models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction. The "natural" dynamic model, derived by the rigidity constraint and the perspective projection, is first reduced by explicitly decoupling structure (depth) from motion. Then implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for all models seen so far in the literature, but we can also derive novel ones

Moving Towards Analog Functional Safety

Over the past century, the exponential growth of the semiconductor industry has led to the creation of tiny and complex integrated circuits, e.g., sensors, actuators, and smart power systems. Innovative techniques are needed to ensure the correct functionality of analog devices that are ubiquitous in every smart system. The standard ISO 26262 related to functional safety in the automotive context specifies that fault injection is necessary to validate all electronic devices. For decades, standardizing fault modeling, injection and simulation mainly focused on digital circuits and disregarding analog ones. An initial attempt is being made with the IEEE P2427 standard draft standard that started to give this field a structured and formal organization. In this context, new fault models, injection, and abstraction methodologies for analog circuits are proposed in this thesis to enhance this application field. The faults proposed by the IEEE P2427 standard draft standard are initially evaluated to understand the associated fault behaviors during the simulation. Moreover, a novel approach is presented for modeling realistic stuck-on/off defects based on oxide defects. These new defects proposed are required because digital stuck-at-fault models where a transistor is frozen in on-state or offstate may not apply well on analog circuits because even a slight variation could create deviations of several magnitudes. Then, for validating the proposed defects models, a novel predictive fault grouping based on faulty AC matrices is applied to group faults with equivalent behaviors. The proposed fault grouping method is computationally cheap because it avoids performing DC or transient simulations with faults injected and limits itself to faulty AC simulations. Using AC simulations results in two different methods that allow grouping faults with the same frequency response are presented. The first method is an AC-based grouping method that exploits the potentialities of the S-parameters ports. While the second is a Circle-based grouping based on the circle-fitting method applied to the extracted AC matrices. Finally, an open-source framework is presented for the fault injection and manipulation perspective. This framework relies on the shared semantics for reading, writing, or manipulating transistor-level designs. The ultimate goal of the framework is: reading an input design written in a specific syntax and then allowing to write the same design in another syntax. As a use case for the proposed framework, a process of analog fault injection is discussed. This activity requires adding, removing, or replacing nodes, components, or even entire sub-circuits. The framework is entirely written in C++, and its APIs are also interfaced with Python. The entire framework is open-source and available on GitHub. The last part of the thesis presents abstraction methodologies that can abstract transistor level models into Verilog-AMS models and Verilog- AMS piecewise and nonlinear models into C++. These abstracted models can be integrated into heterogeneous systems. The purpose of integration is the simulation of heterogeneous components embedded in a Virtual Platforms (VP) needs to be fast and accurate

Data Conversion Within Energy Constrained Environments

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

On Observer-Based Control of Nonlinear Systems

Filtering and reconstruction of signals play a fundamental role in modern signal processing, telecommunications, and control theory and are used in numerous applications. The feedback principle is an important concept in control theory. Many different control strategies are based on the assumption that all internal states of the control object are available for feedback. In most cases, however, only a few of the states or some functions of the states can be measured. This circumstance raises the need for techniques, which makes it possible not only to estimate states, but also to derive control laws that guarantee stability when using the estimated states instead of the true ones. For linear systems, the separation principle assures stability for the use of converging state estimates in a stabilizing state feedback control law. In general, however, the combination of separately designed state observers and state feedback controllers does not preserve performance, robustness, or even stability of each of the separate designs. In this thesis, the problems of observer design and observer-based control for nonlinear systems are addressed. The deterministic continuous-time systems have been in focus. Stability analysis related to the Positive Real Lemma with relevance for output feedback control is presented. Separation results for a class of nonholonomic nonlinear systems, where the combination of independently designed observers and state-feedback controllers assures stability in the output tracking problem are shown. In addition, a generalization to the observer-backstepping method where the controller is designed with respect to estimated states, taking into account the effects of the estimation errors, is presented. Velocity observers with application to ship dynamics and mechanical manipulators are also presented

Systematic Controller Design for Dynamic 3D Bipedal Robot Walking.

Virtual constraints and hybrid zero dynamics (HZD) have emerged as a powerful framework for controlling bipedal robot locomotion, as evidenced by the robust, energetically efficient, and natural-looking walking and running gaits achieved by planar robots such as Rabbit, ERNIE, and MABEL. However, the extension to 3D robots is more subtle, as the choice of virtual constraints has a deciding effect on the stability of a periodic orbit. Furthermore, previous methods did not provide a systematic means of designing virtual constraints to ensure stability. This thesis makes both experimental and theoretical contributions to the control of underactuated 3D bipedal robots. On the experimental side, we present the first realization of dynamic 3D walking using virtual constraints. The experimental success is achieved by augmenting a robust planar walking gait with a novel virtual constraint for the lateral swing hip angle. The resulting controller is tested in the laboratory on a human-scale bipedal robot (MARLO) and demonstrated to stabilize the lateral motion for unassisted 3D walking at approximately 1 m/s. MARLO is one of only two known robots to walk in 3D with stilt-like feet. On the theoretical side, we introduce a method to systematically tune a given choice of virtual constraints in order to stabilize a periodic orbit of a hybrid system. We demonstrate the method to stabilize a walking gait for MARLO, and show that the optimized controller leads to improved lateral control compared to the nominal virtual constraints. We also describe several extensions of the basic method, allowing the use of a restricted Poincaré map and the incorporation of disturbance rejection metrics in the optimization. Together, these methods comprise an important contribution to the theory of HZD.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113370/1/bgbuss_1.pd

Salience Estimation and Faithful Generation: Modeling Methods for Text Summarization and Generation

This thesis is focused on a particular text-to-text generation problem, automatic summarization, where the goal is to map a large input text to a much shorter summary text. The research presented aims to both understand and tame existing machine learning models, hopefully paving the way for more reliable text-to-text generation algorithms. Somewhat against the prevailing trends, we eschew end-to-end training of an abstractive summarization model, and instead break down the text summarization problem into its constituent tasks. At a high level, we divide these tasks into two categories: content selection, or “what to say” and content realization, or “how to say it” (McKeown, 1985). Within these categories we propose models and learning algorithms for the problems of salience estimation and faithful generation. Salience estimation, that is, determining the importance of a piece of text relative to some context, falls into a problem of the former category, determining what should be selected for a summary. In particular, we experiment with a variety of popular or novel deep learning models for salience estimation in a single document summarization setting, and design several ablation experiments to gain some insight into which input signals are most important for making predictions. Understanding these signals is critical for designing reliable summarization models. We then consider a more difficult problem of estimating salience in a large document stream, and propose two alternative approaches using classical machine learning techniques from both unsupervised clustering and structured prediction. These models incorporate salience estimates into larger text extraction algorithms that also consider redundancy and previous extraction decisions. Overall, we find that when simple, position based heuristics are available, as in single document news or research summarization, deep learning models of salience often exploit them to make predictions, while ignoring the arguably more important content features of the input. In more demanding environments, like stream summarization, where heuristics are unreliable, more semantically relevant features become key to identifying salience content. In part two, content realization, we assume content selection has already been performed and focus on methods for faithful generation (i.e., ensuring that output text utterances respect the semantics of the input content). Since they can generate very fluent and natural text, deep learning- based natural language generation models are a popular approach to this problem. However, they often omit, misconstrue, or otherwise generate text that is not semantically correct given the input content. In this section, we develop a data augmentation and self-training technique to mitigate this problem. Additionally, we propose a training method for making deep learning-based natural language generation models capable of following a content plan, allowing for more control over the output utterances generated by the model. Under a stress test evaluation protocol, we demonstrate some empirical limits on several neural natural language generation models’ ability to encode and properly realize a content plan. Finally, we conclude with some remarks on future directions for abstractive summarization outside of the end-to-end deep learning paradigm. Our aim here is to suggest avenues for constructing abstractive summarization systems with transparent, controllable, and reliable behavior when it comes to text understanding, compression, and generation. Our hope is that this thesis inspires more research in this direction, and, ultimately, real tools that are broadly useful outside of the natural language processing community