Nesta dissertação é apresentado um estudo comparativo entre seis controladores H \'infinito\' não lineares aplicados em um robô móvel com rodas. Três estratégias de controle são avaliadas. Na primeira, o modelo do robô é considerado completamente conhecido. Na segunda, o modelo matemático é considerado desconhecido e é realizada uma estimativa baseada em métodos inteligentes. E finalmente, na terceira estratégia, o modelo nominal é conhecido e técnicas inteligentes são usadas para estimar somente incertezas paramétricas do robô. As técnicas inteligentes usadas são baseadas em redes neurais e em lógica fuzzy. Esses controladores são resolvidos através de desigualdades matriciais lineares (DMLs) e equações algébricas de Riccati. Todos os resultados obtidos são baseados em dados experimentais. / This dissertation is present a comparative study between six nonlinear H \'infinity\' controllers applied to a wheeled mobile robot. Three control strategies are adopted. In the first, the model of the robot is considered completely known. In the second, the mathematical model is considered unknown and is accomplished an estimate based on intelligent methods. And finally, in the third strategy, the nominal model is known and intelligent techniques are used only to estimate parametric uncertainties of the robot. The intelligent techniques used are based in neural networks and in fuzzy logic. These controllers are solved via linear matrix inequalities (LMIs) and algebraic Riccati equations. All results obtained are based in experimental data.

Biomimetic flapping-wing vehicles have attracted recent interest because of their numerous potential military and civilian applications. In this dissertation is described the design of a multi-agent adaptive controller for such a vehicle. This controller is responsible for estimating the vehicle pose (position and orientation) and then generating four parameters needed for split-cycle control of wing movements to correct pose errors. These parameters are produced via a subsumption architecture rule base. The control strategy is fault tolerant. Using an online learning process, an agent continuously monitors the vehicle's behavior and initiates diagnostics if the behavior has degraded. This agent can then autonomously adapt the rule base if necessary. Each rule base is constructed using a combination of extrinsic and intrinsic evolution. Details of the vehicle, the multi-agent system architecture, agent task scheduling, rule base design, and vehicle control are provided.

The Intelligent Control Paradigm for software architecture is the result of this work. The Viable Software Approach is developed as an instance of the paradigm. The approach uses the Viable System Model as the basis for software system architecture. The result is a model-based architecture and approach for developing software systems by piecemeal adaptation with the goal that they become adaptive systems at runtime. Software built in this manner is called Viable Software. Viable Software represents a unifying class of self-controlling software that is an “intelligent” control system. Cybernetics, Control Theory, and Complexity Theory are the background for this work, and aspects relevant to this work are presented. These results are related to software architecture and software engineering. Rationale for the selection of the Viable System Model as a basis for software systems is given. The Viable System Model is described. The model is restated as an Alexanderian “pattern language” to make it more accessible to software engineering. A Viable Software Approach is proposed and expressed in the form of a Product Line Architecture that arranges the Viable System Model, the Viable Software Architecture, a Viable Component Framework, and a Component Transfer Protocol into a system for generative programming. An important result is the formalisation of the pattern of the Viable System into the interface specifications of the Viable Component. Three case studies illustrate the approach. The first is an analysis and extension of the Groove collaboration system. This study shows how the approach is used to map an existing system into the Viable Software Architecture and add fuzzy-adaptive user interface controllers. The second study presents the design and detailed software construction of an adaptive camera controller as part of a smart environment. The final study shows how a Business-to-Business e-Commerce system can be evolved and an expert system-based controller developed to implement business contracts.

This thesis presents the design of various controllers for a highly maneuverable, high performance aircraft, namely the modified F-18. The aircraft was required to perform high angle-of-attack maneuvers, for which the aircraft behaves in as a highly nonlinear system. An adaptive PID controller was used to control the aircraft through these high angle-of-attack maneuvers. Several nonlinear controllers were then developed based on the adaptive PID control, and were tested for robustness. This thesis also looks at an improvement in the aircraft which may improve performance in high angle-of-attack maneuvers. The contributions of this thesis are in the areas of control, in general, and specifically in the area of aircraft control. Successful application of linear adaptive control and nonlinear control were presented. In the area of aircraft control, controllers were presented which produce good performance for high angle-of-attack maneuvers, while maintaining implementability. Also, some insight is gained into what aircraft changes could improve performance. / Graduation date: 1995

The explosion of information and information technology has led many firms to evolve a dispersed product development process with people and organizations spread throughout the world. To coordinate such dispersed processes managers attempt to establish a culture that implicitly rewards product development teams based on their ability to perform against a set of strategic metrics such as customer satisfaction, time to market, defect reduction, or platform reuse. Many papers have focused on selecting the right metrics and establishing the culture. In this paper we focus on a practical method to fine-tune a firm's relative emphasis on the metrics that they have chosen. In particular, we seek to advise a firm whether to increase or decrease their emphasis on each metric such that the change in emphasis improves profits. Using a thermostat analogy we apply an adaptive control feedback mechanism in which we estimate the incremental improvements in priorities that will increase profits. Iterations of adaptive control seek to maximize profits even if the environment is changing. We demonstrate the metric thermostat’s use in an application to a firm with over $20 billion in revenue. In developing the metric thermostat we recognize that there are hundreds of detailed actions, such as the use of the house of quality and the use of robust design, among which the product development team must choose. We also recognize that they will act in their own best interests to choose the actions that maximize their own implicit rewards as determined by the metrics. Management need not observe or dictate these detailed actions, but rather control the process by establishing the culture that sets the implicit weights on the metrics. The thermostat works by changing those implicit weights. We define the problem, introduce the adaptive control mechanism, modify “agency” theory to deal with incremental changes about an operating point, and derive methods that are practical and robust in light of the data that firms have available. Our methods include statistical estimation and internal surveys. The mathematics identify the critical few parameters that need be determined and highlight how to estimate them. Both the measures and the estimation are illustrated in our initial application to a large officeequipment firm. The metrics thermostat suggests that this firm has about the right emphasis on timeto- market, but has overshot on platform reuse and has lost its focus on customer satisfaction. We describe how the firm reacted to the recommendations and changed its organization. We describe additional ongoing applications with the US Air Force, the US Navy, and a major automobile and truck manufacturer. / This research was funded by the Center for Innovation in Product Development (CIPD) and the International Center for Research on the Management of Technology (ICRMOT), M.I.T.

The lungs are particularly vulnerable to acute, critical illness. Respiratory failure can result not only from primary lung pathology, such as pneumonia, but also as a secondary consequence of heart failure or inflammatory illness, such as sepsis or trauma. When this occurs, it is essential to support patients with mechanical ventilation while the fundamental disease process is addressed. The goal of mechanical ventilation is to ensure adequate ventilation, which involves a magnitude of gas exchange that leads to the desired blood level of carbon dioxide, and adequate oxygenation that ensures organ function. Achieving these goals is complicated by the fact that mechanical ventilation can actually cause acute lung injury, either by inflating the lungs to excessive volumes or by using excessive pressures to inflate the lungs. Thus, the challenge to mechanical ventilation is to produce the desired blood levels of carbon dioxide and oxygen without causing further acute lung injury. In this research, we develop an analysis and control synthesis framework for a pressure-limited respirator and lung mechanics system using compartment models. Specifically, a general mathematical model is developed for the dynamic behavior of a multicompartment respiratory system. Then, based on this multicompartment model, an optimal respiratory pattern is characterized using classical calculus of variations minimization techniques for inspiratory and expiratory breathing cycles. Furthermore, model predictive controller frameworks are designed to track the given optimal respiratory air flow pattern while satisfying control input amplitude and rate constrains.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

Dynamic Collaborative Driving is the concept of decentralized multi-vehicle automated driving where vehicles form dynamic local area networks within which information is shared to build a dynamic data representation of the environment to improve road usage and safety. The vision is to have networks of cars spanning multiple lanes forming these dynamic networks so as to optimize traffic flow while maintaining safety as each vehicle travels to its destinations. A basic requirement of any vehicle participating in dynamic collaborative driving is longitudinal and lateral control. Without this capability, higher-level coordination is not possible. This thesis investigates the issue of the control of an automobile in the context of a Dynamic Collaborative Driving system. Each vehicle involved is considered a complex composite nonlinear system. Therefore a complex nonlinear model of the vehicle dynamics is formulated and serves as the control system design platform. Due to the nonlinear nature of the vehicle dynamics, a nonlinear approach to control is used to achieve longitudinal and lateral control of the vehicle. This novel approach combines the use of reinforcement learning: a modern machine learning technique, with adaptive control and preview control techniques. This thesis presents the design of both the longitudinal and lateral control systems which serves as a basis for Dynamic Collaborative Driving. The results of the reinforcement learning phase and the performance of the adaptive control systems for single automobile performance as well as the performance in a multi-vehicle platoon is presented.

Future wireless networks will be required to support multimedia traffic in addition to traditional best-effort network services. Supporting multimedia traffic on wired networks presents a large number of design problems, particularly for networks that run connectionless data transport protocols such as the TCP/IP protocol suite. These problems are magnified for wireless links, as the quality of such links varies widely and uncontrollably. This dissertation presents new tools developed for the design and realization of wireless networks including, for the first time, analytical channel models for predicting the efficacy of error control codes, interleaving schemes, and signalling protocols, and several novel algorithms for matching and adapting system parameters (such as error control and frame length) to time-varying channels and Quality of Service (QoS) requirements.

The objective of this dissertation is to incorporate the concept of optimality to multi-modal control and apply the theoretical results to obtain successful navigation strategies for autonomous mobile robots. The main idea in multi-modal control is to breakup a complex control task into simpler tasks. In particular, number of control modes are constructed, each with respect to a particular task, and these modes are combined according to some supervisory control logic in order to complete the overall control task. This way of modularizing the control task lends itself particularly well to the control of autonomous mobile robot, as evidenced by the success of behavior-based robotics. Many challenging and interesting research issues arise when employing multi-modal control. This thesis aims to address these issues within an optimal control framework. In particular, the contributions of this dissertation are as follows: We first addressed the problem of inferring global behaviors from a collection of local rules (i.e., feedback control laws). Next, we addressed the issue of adaptively varying the multi-modal control system to further improve performance. Inspired by adaptive multi-modal control, we presented a constructivist framework for the learning from example problem. This framework was applied to the DARPA sponsored Learning Applied to Ground Robots (LAGR) project. Next, we addressed the optimal control of multi-modal systems with infinite dimensional constraints. These constraints are formulated as multi-modal, multi-dimensional (M3D) systems, where the dimensions of the state and control spaces change between modes to account for the constraints, to ease the computational burdens associated with traditional methods. Finally, we used multi-modal control strategies to develop effective navigation strategies for autonomous mobile robots. The theoretical results presented in this thesis are verified by conducting simulated experiments using Matlab and actual experiments using the Magellan Pro robot platform and the LAGR robot. In closing, the main strength of multi-modal control lies in breaking up complex control task into simpler tasks. This divide-and-conquer approach helps modularize the control system. This has the same effect on complex control systems that object-oriented programming has for large-scale computer programs, namely it allows greater simplicity, flexibility, and adaptability.