This volume deals with his formative years and early professional life. This period constitutes the making of a leader of global stature. His profound odyssey of thought leadership started at the age of 16, and moved through the University of Zimbabwe (UZ), where he graduated with a BSc (Hons) in Electrical Engineering. A statement he wrote as a student leader led to the unprecedented closure of UZ. He was injured and detained. His journey of ideas then proceeded to the University of Oxford, where he obtained an MSc in Computer Engineering and a PhD in Robotics and Mechatronics. The next stop was the United States, where he was a Research Scientist at NASA, Professor at MIT and Management Consultant at McKinsey. The book ends with his return to the continent in 2002, equipped with Pan-African, business- and technology-driven developmental strategies and paradigms.
Written to inspire and cultivate the ability to design and analyse feasible control algorithms for a wide range of engineering applications, this comprehensive text covers the theoretical and practical principles involved in the design and analysis of control systems. This second edition introduces 4IR adoption strategies for traditional intelligent control, including new techniques of implementing control systems. It provides improved coverage of the characteristics of feedback control, root-locus analysis, frequency-response analysis, state space methods, digital control systems and advanced controls, including updated worked examples and problems. Features: Describes very timely applications and contains a good mix of theory, application, and computer simulation. Covers all the fundamentals of control systems. Takes a transdisciplinary and cross-disciplinary approach. Explores updates for 4IR (Industry 4.0) and includes better experiments and illustrations for nonlinear control systems. Includes homework problems, case studies, examples, and a solutions manual. This book is aimed at senior undergraduate and graduate students, professional engineers and academic researchers, in interrelated engineering disciplines such as electrical, mechanical, aerospace, mechatronics, robotics and other AI-based systems.
Decentralized Estimation and Control for Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia. Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted. Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources. Decentralized Estimation and Control for Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation. The text discusses: Generalizing the linear Information filter to the problem of estimation for nonlinear systems Developing a decentralized form of the algorithm Solving the problem of fully connected topologies by using generalized model distribution where the nodal system involves only locally relevant states Reducing computational requirements by using smaller local model sizes Defining internodal communication Developing estima
The fusion of information from sensors with different physical characteristics, such as sight, touch, sound, etc., enhances the understanding of our surroundings and provides the basis for planning, decision-making, and control of autonomous and intelligent machines.The minimal representation approach to multisensor fusion is based on the use of an information measure as a universal yardstick for fusion. Using models of sensor uncertainty, the representation size guides the integration of widely varying types of data and maximizes the information contributed to a consistent interpretation.In this book, the general theory of minimal representation multisensor fusion is developed and applied in a series of experimental studies of sensor-based robot manipulation. A novel application of differential evolutionary computation is introduced to achieve practical and effective solutions to this difficult computational problem.
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