Ford Motor Company (United States)

1. Introduction. Control System Design Steps. Adaptive Control. A Brief History. 2. Models for Dynamic Systems. Introduction. State-Space Models. Input/Output Models. Plant Parametric Models. Problems. 3. Stability. Introduction. Preliminaries. Input/Output Stability. Lyapunov Stability. Positive Real Functions and Stability. Stability of LTI Feedback System. Problems. 4. On-Line Parameter Estimation. Introduction. Simple Examples. Adaptive Laws with Normalization. Adaptive Laws with Projection. Bilinear Parametric Model. Hybrid Adaptive Laws. Summary of Adaptive Laws. Parameter Convergence Proofs. Problems. 5. Parameter Identifiers and Adaptive Observers. Introduction. Parameter Identifiers. Adaptive Observers. Adaptive Observer with Auxiliary Input. Adaptive Observers for Nonminimal Plant Models. Parameter Convergence Proofs. Problems. 6. Model Reference Adaptive Control. Introduction. Simple Direct MRAC Schemes. MRC for SISO Plants. Direct MRAC with Unnormalized Adaptive Laws. Direct MRAC with Normalized Adaptive Laws. Indirect MRAC. Relaxation of Assumptions in MRAC. Stability Proofs in MRAC Schemes. Problems. 7. Adaptive Pole Placement Control. Introduction. Simple APPC Schemes. PPC: Known Plant Parameters. Indirect APPC Schemes. Hybrid APPC Schemes. Stabilizability Issues and Modified APPC. Stability Proofs. Problems. 8. Robust Adaptive Laws. Introduction. Plant Uncertainties and Robust Control. Instability Phenomena in Adaptive Systems. Modifications for Robustness: Simple Examples. Robust Adaptive Laws. Summary of Robust Adaptive Laws. Problems. 9. Robust Adaptive Control Schemes. Introduction. Robust Identifiers and Adaptive Observers. Robust MRAC. Performance Improvement of MRAC. Robust APPC Schemes. Adaptive Control of LTV Plants. Adaptive Control for Multivariable Plants. Stability Proofs of Robust MRAC Schemes. Stability Proofs of Robust APPC Schemes. Problems. Appendices. Swapping Lemmas. Optimization Techniques. Bibliography. Index. License Agreement and Limited Warranty.

This paper has two purposes: 1) to clarify the relationship between the quantum theory of radiation, where the electromagnetic field-expansion coefficients satisfy commutation relations, and the semiclassical theory, where the electromagnetic field is considered as a definite function of time rather than as an operator; and 2) to apply some of the results in a study of amplitude and frequency stability in a molecular beam maser. In 1), it is shown that the semiclassical theory, when extended te take into account both the effect of the field on the molecules and the effect of the molecules on the field, reproduces almost quantitatively the same laws of energy exchange and coherence properties as the quantized field theory, even in the limit of one or a few quanta in the field mode. In particular, the semiclassical theory is shown to lead to a prediction of spontaneous emission, with the same decay rate as given by quantum electrodynamics, described by the Einstein A coefficients. In 2), the semiclassical theory is applied to the molecular beam maser. Equilibrium amplitude and frequency of oscillation are obtained for an arbitrary velocity distribution of focused molecules, generalizing the results obtained previously by Gordon, Zeiger, and Townes for a singel-velocity beam, and by Lamb and Helmer for a Maxwellian beam. A somewhat surprising result is obtained; which is that the measurable properties of the maser, such as starting current, effective molecular Q, etc., depend mostly on the slowest 5 to 10 per cent of the molecules. Next we calculate the effect of amplitude and frequency of oscillation, of small systematic perturbations. We obtain a prediction that stability can be improved by adjusting the system so that the molecules emit all their energy h Ω to the field, then reabsorb part of it, before leaving the cavity. In general, the most stable operation is obtained when the molecules are in the process of absorbing energy from the radiation as they leave the cavity, most unstable when they are still emitting energy at that time. Finally, we consider the response of an oscillating maser to randomly time-varying perturbations. Graphs are given showing predicted response to a small superimposed signal of a frequency near the oscillation frequency. The existence of "noise enhancing" and "noise quieting" modes of operation found here is a general property of any oscillating system in which amplitude is limited by nonlinearity.

For turbulent flows at relatively low speeds there exists an excellent mathematical model in the incompressible Navier–Stokes equations. Why then is the 'problem of turbulence' so difficult? One reason is that these nonlinear partial differential equations appear to be insoluble, except through numerical simulations, which offer useful approximations but little direct understanding. Three recent developments offer new hope. First, the discovery by experimentalists of coherent structures in certain turbulent flows. Secondly, the suggestion that strange attractors and other ideas from finite-dimensional dynamical systems theory might play a role in the analysis of the governing equations. And, finally, the introduction of the Karhunen-Loève or proper orthogonal decomposition. This book introduces these developments and describes how they may be combined to create low-dimensional models of turbulence, resolving only the coherent structures. This book will interest engineers, especially in the aerospace, chemical, civil, environmental and geophysical areas, as well as physicists and applied mathematicians concerned with turbulence.

Study of the time dependence of physical properties in the transformation range of glass is complicated by the “memory effect” and the inherent nonlinearity which are characteristic of structural relaxation. A multiparameter model of structural relaxation is presented that differs from earlier models in that it takes account of both these effects. This model fits available experimental data well; these data were obtained for the most part by observing the evolution of properties (such as density or refractive index) following a step change in temperature. The present model also permits prediction of the physical properties of glass subjected to arbitrary and more complex temperature‐time histories. It should, therefore, also be useful in the rational design of heat treating processes such as annealing.

7R21. Practical Methods for Optimal Control using Nonlinear Programming. - JT Betts (Res and Tech Div, Boeing Co, Seattle WA). SIAM, Philadelphia. 2001. 190 pp. ISBN 0-89871-488-5. $51.00. Reviewed by I Kolmanovsky (Sci Res Lab, MD-2036, Ford Motor Co, 2101 Village Rd, Dearborn MI 48124).System models are routinely developed in engineering and other disciplines for system analysis and simulation. They now find widespread use in industry in all phases of design and development of technological systems. Model-based trajectory optimization and optimal control are among key tools that facilitate finding better ways to operate and control complex engineering systems. They are also becoming essential in the design phase, to determine system parameters which meet stringent performance objectives and constraints. In this regard, this book is quite timely. Its detailed treatment of methods and strategies used to solve such optimal control and trajectory optimization problems (complete with in-depth discussion of implementation, tricks, and “what can go wrong” issues) will be useful to optimization practitioners and insightful to researchers focused on various aspects of optimization. The focus of the book is a family of techniques (often referred to as direct methods) wherein the optimal control problem is converted into a finite-dimensional optimization problem using method of transcription. The finite-dimensional problem can be solved using nonlinear programming techniques. The basic idea of transcription is to treat the values of the state and control variables at discrete-time instants as free variables to which constrained, nonlinear programming optimization can then be applied. The constraints are inherited from the original optimal control problem formulation and are also induced by the discretization/integration of the differential equations governing the system dynamics. Efficient computational strategies can be developed if the nonlinear programming algorithm used in the second stage takes advantage of the structure of the problem (sparsity), which is induced as a result of integrating the dynamics equations in the first phase. The book is very readable. In part, it is due to its theorem-free format while relying instead on a detailed discussion and illustration of how to setup numerical methods and strategies to solve optimal control problems, what can happen, and what can go wrong. Enough detail is given so that the reader also gets a good flavor of the nature of rigorous theoretical results (found in the references provided in the book). The book is organized as follows. The first chapter reviews the main ideas and techniques of nonlinear programming, including unconstrained minimization methods (Newton and quasi-Newton methods) and constrained optimization techniques (such as Sequential Quadratic Programming or SQP). The review is concise and self-contained, providing the reader quick access to the main ideas used in high-performance optimization codes. Practical issues of what can go wrong such as infeasible constraints, discontinuous objective functions, rank-deficient constraints, and constraint redundancy are illustrated with specific examples. Practical remedies to handle these difficulties are discussed. Chapter 2 discusses large scale nonlinear programming problems and, in particular, the case when the Hessian matrix and Jacobian matrix are sparse, that is most of their elements are zero. In this situation, sparse finite differences can be used to approximate first and second derivatives in a computationally efficient fashion. This and the resulting sparse SQP algorithm are described in detail. Chapter 3 reviews several classical numerical methods for solving initial and boundary value problems for ordinary differential equations. It is the central idea of the book that numerical methods for solving (integrating or discretizing) ODEs give rise to nonlinear programming problems which are sparse, and the sparsity properties depend on a particular method used for discretization. Thus after the transcription, it is possible to take advantage of a particular sparse structure (induced by a particular discretization method) in solving the nonlinear programming problem. This is basically the subject of Chapter 4. What can go wrong is discussed including how to recognize the emergence of singular arcs and discontinuous control, as well as consideration of the issues involved in problems with state constraints. Strategies to switch from lower order ODE integration routines (used at the beginning) to higher order routines as well as mesh refinement strategies are detailed. Chapter 5 reports several of the numerical case studies that the author successfully solved in the past using the techniques described in the book. They are examples of realistic trajectory optimization problems, primarily drawn from the aerospace field. They are, respectively: Space shuttle reentry trajectory optimization, minimum time to climb for an aeroplane, low-thrust orbit transfer for a spacecraft, and two burn orbit transfer for a spacecraft, as well as trajectory optimization for an industrial robot and a multibody mechanism. These case studies provide a good illustration of the techniques described in the book and describe additional strategies to deal with particular issues and problem formulations. On a critical side, the book only covers one particular class of techniques for solving optimal control problems. Methods based on dynamic programming (which provide optimal control policy in a feedback form) and methods based on calculated gradients through the solution of adjoint equations are not covered. The latter are discussed, with an argument that the adjoint equation approach may not often be applied to the complex models anyway since they may not admit a readily available symbolic representation adequate for the development of linearized equations. The techniques described in the book are implemented in software called SOCS, commercially available from Boeing. This software is briefly described in the Appendix. A limited version of this software (trial version) or other computational codes for the reader to experiment with would amplify the appeal of this book, but are presently not available. In summary, Practical Methods for Optimal Control using Nonlinear Programming will be useful and is recommended to researchers and engineers in industry and academia whose projects involve solving optimal control/trajectory optimization problems. The book can also be used, as a supplemental text, in graduate-level university courses on optimal control and numerical methods in optimal control. It should also be possible to develop a special topics graduate-level course based on the material in this book, supplemented with theoretical details from the reference literature.

Additive manufacturing (AM), particularly Selective Laser Melting (SLM) has enabled development of lattice structures with unique properties. Through control of various parameters lattice structures can produce unique mechanical, electrical, thermal and acoustic properties, and have received much research attention. Despite the increasing volume of published data on the mechanical response of specific SLM lattice structures, there exists no overarching analysis. This work addresses this identified deficiency by providing a comprehensive summary of the experimental data reported on the mechanical response of SLM lattice structures. The design, fabrication and performance of SLM lattice structures are reviewed and the quality of data reported is analysed to inform best-practice for future studies. This comprehensive data summary enables meta-analysis of the reported mechanical performance of SLM lattice structures, providing insight into the bounds of their technical capabilities. Correlations were identified between the relative density and mechanical properties of many unit cell topologies consistent with the predictions of the Gibson-Ashby model, indicating its usefulness in describing and predicting the behaviour of SLM lattice structures. This review provides designers with a compiled resource of experimental data and design for AM tools to inform future design applications of SLM lattice structures and facilitates their further commercial adoption.

A criterion is proposed for determining the onset of ferromagnetism in a material as its temperature is lowered from a region in which the linearity of its magnetic moment versus field isotherm gives an indication of paramagnetism. Within the limits of validity of a molecular field treatment, the Curie temperature is shown to be in general indicated by the third power of the magnetization being proportional to the internal magnetic field. The method has been employed to redetermine the Curie point of nickel from the data of Weiss and Forrer, of ${\mathrm{Fe}}_{3}$${\mathrm{O}}_{4}$ from the data of Smith and of some alloys from the data of Kaufmann and his collaborators and the author.

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

This paper presents the results of a series of experiments in which a giant pulsed ruby laser is used to study several different nonlinear optical effects arising from an induced optical polarization third order in the electric field strength. The various phenomena studied are special cases of either frequency mixing or intensity-dependent changes in the complex refractive index, including Raman laser action at a focus. A wide range of crystalline and isotropic materials was studied. The theory for these effects is extended to cover resonant interactions. The experimental results are interpreted in terms of simplified models, and quantitative values for the nonlinear polarizability coefficients are given. The rather large experimental uncertainties in these coefficients are discussed.

We first provide an overview of 3-D shape measurement us- ing various optical methods. Then we focus on structured light tech- niques where various optical configurations, image acquisition tech- niques, data postprocessing and analysis methods and advantages and limitations are presented. Several industrial application examples are presented. Important areas requiring further R&D are discussed. Finally, a comprehensive bibliography on 3-D shape measurement is included, although it is not intended to be exhaustive. © 2000 Society of Photo-Optical Instrumentation Engineers. (S0091-3286(00)00101-X)

The capability indices Cp, CPU, CPL, k and Cpk are presented and related to process parameters. These indices are shown to form a complementary system of measures of process performance, and can be used with bilateral and unilateral tolerances, with or without target values. A number of Japanese industries currently use the five indices and the U.S. automotive industry has started using these measures in a number of areas. Various applications of the indices are discussed along with statistical sampling considerations.

Metal–organic frameworks have received significant attention as a new class of adsorbents for natural gas storage; however, inconsistencies in reporting high-pressure adsorption data and a lack of comparative studies have made it challenging to evaluate both new and existing materials. Here, we briefly discuss high-pressure adsorption measurements and review efforts to develop metal–organic frameworks with high methane storage capacities. To illustrate the most important properties for evaluating adsorbents for natural gas storage and for designing a next generation of improved materials, six metal–organic frameworks and an activated carbon, with a range of surface areas, pore structures, and surface chemistries representative of the most promising adsorbents for methane storage, are evaluated in detail. High-pressure methane adsorption isotherms are used to compare gravimetric and volumetric capacities, isosteric heats of adsorption, and usable storage capacities. Additionally, the relative importance of increasing volumetric capacity, rather than gravimetric capacity, for extending the driving range of natural gas vehicles is highlighted. Other important systems-level factors, such as thermal management, mechanical properties, and the effects of impurities, are also considered, and potential materials synthesis contributions to improving performance in a complete adsorbed natural gas system are discussed.

Widespread adoption of hydrogen as a vehicular fuel depends critically upon the ability to store hydrogen on-board at high volumetric and gravimetric densities, as well as on the ability to extract/insert it at sufficiently rapid rates. As current storage methods based on physical means--high-pressure gas or (cryogenic) liquefaction--are unlikely to satisfy targets for performance and cost, a global research effort focusing on the development of chemical means for storing hydrogen in condensed phases has recently emerged. At present, no known material exhibits a combination of properties that would enable high-volume automotive applications. Thus new materials with improved performance, or new approaches to the synthesis and/or processing of existing materials, are highly desirable. In this critical review we provide a practical introduction to the field of hydrogen storage materials research, with an emphasis on (i) the properties necessary for a viable storage material, (ii) the computational and experimental techniques commonly employed in determining these attributes, and (iii) the classes of materials being pursued as candidate storage compounds. Starting from the general requirements of a fuel cell vehicle, we summarize how these requirements translate into desired characteristics for the hydrogen storage material. Key amongst these are: (a) high gravimetric and volumetric hydrogen density, (b) thermodynamics that allow for reversible hydrogen uptake/release under near-ambient conditions, and (c) fast reaction kinetics. To further illustrate these attributes, the four major classes of candidate storage materials--conventional metal hydrides, chemical hydrides, complex hydrides, and sorbent systems--are introduced and their respective performance and prospects for improvement in each of these areas is discussed. Finally, we review the most valuable experimental and computational techniques for determining these attributes, highlighting how an approach that couples computational modeling with experiments can significantly accelerate the discovery of novel storage materials (155 references).

Powdered samples of the type Ce1−xRExO2−y, where RE=La, Pr, Nd, Eu, Gd, and Tb, are synthesized over the range 0≤x≤0.5 starting from nitrate solutions of the rare earths. X-ray diffraction and Raman scattering are used to analyze the samples. These compounds, at least in the low doping regime and for strictly trivalent dopants, form solid solutions that maintain the fluorite structure of CeO2 with a change in lattice constant that is approximately proportional to the dopant ionic radius. The single allowed Raman mode, which occurs at 465 cm−1 in pure CeO2, is observed to shift to lower frequency with increasing doping level for all the rare earths. However, after correcting for the Grüneisen shift from the lattice expansion, the frequency shift is actually positive for all the strictly trivalent ions. In addition, the Raman line broadens and becomes asymmetric with a low frequency tail, and a new broad feature appears in the spectrum at ∼570 cm−1. These changes in the Raman spectrum are attributed to O vacancies, which are introduced into the lattice whenever a trivalent RE is substituted for Ce4+. This conclusion is supported by a simple model calculation of the effects of O vacancies on the Raman spectrum. The model uses a Green’s function technique with the vacancies treated as point defects with zero mass.

Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

We describe a new method for the direct examination of three-dimensional bone structure in vitro based on high-resolution computed tomography (CT). Unlike clinical CT, a three-dimensional reconstruction array is created directly, rather than a series of two-dimensional slices. All structural indices commonly determined from two-dimensional histologic sections can be obtained nondestructively from a large number of slices in each of three orthogonal directions. This permits a comprehensive description of structural variation within a specimen and greatly facilitates the study of structural anisotropy. A measure of three-dimensional connectivity (Euler number/tissue volume) has been determined for the first time in human cancellous bone and shown to correlate with several two-dimensional histomorphometric indices. The method has the potential for overcoming many of the limitations of current approaches to the study of bone architecture at the microscopic level.

We have used a neutron interferometer to observe the quantum-mechanical phase shift of neutrons caused by their interaction with Earth's gravitational field.Received 14 April 1975DOI:https://doi.org/10.1103/PhysRevLett.34.1472©1975 American Physical Society

An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.

We briefly describe the Ordered Weighted Averaging (OWA) operator and discuss a methodology for learning the associated weighting vector from observational data. We then introduce a more general type of OWA operator called the Induced Ordered Weighted Averaging (IOWA) Operator. These operators take as their argument pairs, called OWA pairs, in which one component is used to induce an ordering over the second components which are then aggregated. A number of different aggregation situations have been shown to be representable in this framework. We then show how this tool can be used to represent different types of aggregation models.

Lithium ion batteries (LIBs) have transformed the consumer electronics (CE) sector and are beginning to power the electrification of the automotive sector. The unique requirements of the vehicle application have required design considerations beyond LIBs suitable for CE. The historical progress of LIBs since commercialization is compared against automotive application goals and requirements. Vehicle-driven battery targets are discussed and informed by a set of international research groups and existing production electric vehicles' performance. The opportunities and challenges remaining for the transition of LIBs suitable for CE to the automotive sector are assessed in terms of energy, life, cost, safety, and fast charge capability.