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Low Rank Approximation
Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation; machine learning for information retrieval and clustering; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; and psychometrics for factor analysis. Software implementation of the methods is given, making the theory directly applicable in practice. All numerical examples are included in demonstration files giving hands-on experience and exercises and MATLAB® examples assist in the assimilation of the theory.
Active Braking Control Systems Design for Vehicles
Active Braking Control Design for Road Vehicles focuses on two main brake system technologies: hydraulically-activated brakes with on–off dynamics and electromechanical brakes, tailored to brake-by-wire control. The physical differences of such actuators enjoin the use of different control schemes so as to be able fully to exploit their characteristics. The authors show how these different control approaches are complementary, each having specific peculiarities in terms of either performance or of the structural properties of the closed-loop system. They also consider other problems related to the design of braking control systems, namely: • longitudinal vehicle speed estimation and its relationship with braking control system design; • tire–road friction estimation; • direct estimation of tire–road contact forces via in-tire sensors, providing a treatment of active vehicle braking control from a wider perspective linked to both advanced academic research and industrial reality.
Distributed Coordination of Multi-agent Networks
Distributed Coordination of Multi-agent Networks introduces problems, models, and issues such as collective periodic motion coordination, collective tracking with a dynamic leader, and containment control with multiple leaders, and explores ideas for their solution. Solving these problems extends the existing application domains of multi-agent networks; for example, collective periodic motion coordination is appropriate for applications involving repetitive movements, collective tracking guarantees tracking of a dynamic leader by multiple followers in the presence of reduced interaction and partial measurements, and containment control enables maneuvering of multiple followers by multiple leaders.
Modelling and Control of an Autonomous Two-Wheeled Vehicle
With respect to the future urban mobility, modern electrical bicycles, advanced motorcycles and innovative two-wheeled vehicles are arresting enormous amount of attention. Especially, model-based control and optimal trajectory planning for such vehicles are important to the research and development of the future. Therefore, a reliable and yet usable vehicle model as well as a systematic approach to motion control for two-wheeled vehicles are essential, to which this work makes a contribution. Currently available two-wheeled vehicle models are mostly either too complex to be used for a systematic control synthesis, or too simple such that the physical behaviour of the vehicle is no more repre...
Advances in Dynamics and Control
Presenting research papers contributed by experts in dynamics and control, Advances in Dynamics and Control examines new ideas, reviews the latest results, and investigates emerging directions in the rapidly-growing field of aviation and aerospace. Exploring a wide range of topics, key areas discussed include:* rotorcraft dynamics* stabilization of
Autonomous Mobile Robots
It has long been the goal of engineers to develop tools that enhance our ability to do work, increase our quality of life, or perform tasks that are either beyond our ability, too hazardous, or too tedious to be left to human efforts. Autonomous mobile robots are the culmination of decades of research and development, and their potential is seemingly unlimited. Roadmap to the Future Serving as the first comprehensive reference on this interdisciplinary technology, Autonomous Mobile Robots: Sensing, Control, Decision Making, and Applications authoritatively addresses the theoretical, technical, and practical aspects of the field. The book examines in detail the key components that form an aut...
Resiligence
The Resili(g)ence publication is part of the outputs produced within the European project KAAU, Knowledge Alliance for Advanced Urbanism (www.ka-au.net), Erasmus + program, and consists of two volumes: the first Resili(g)ence Intelligent Cities / Resilient Landscapes offers reflections on the general framework and on the theme of resilience applied to intelligent cities and the landscape, while the second volume GOA Resili(g)ent City, analyzes the case study of Genoa. The term Resili(g)ence is presented in these pages as a neologism coined by the team responsible of the GIC.Lab (DAD-UNIGE) to define those strategies of approach to our cities and environments of life, exchange and relationshi...
Robust Adaptive Dynamic Programming
A comprehensive look at state-of-the-art ADP theory and real-world applications This book fills a gap in the literature by providing a theoretical framework for integrating techniques from adaptive dynamic programming (ADP) and modern nonlinear control to address data-driven optimal control design challenges arising from both parametric and dynamic uncertainties. Traditional model-based approaches leave much to be desired when addressing the challenges posed by the ever-increasing complexity of real-world engineering systems. An alternative which has received much interest in recent years are biologically-inspired approaches, primarily RADP. Despite their growing popularity worldwide, until ...
Nonlinear Model Predictive Control
Nonlinear Model Predictive Control is a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. NMPC schemes with and without stabilizing terminal constraints are detailed and intuitive examples illustrate the performance of different NMPC variants. An introduction to nonlinear optimal control algorithms gives insight into how the nonlinear optimisation routine – the core of any NMPC controller – works. An appendix covering NMPC software and accompanying software in MATLAB® and C++(downloadable from www.springer.com/ISBN) enables readers to perform computer experiments exploring the possibilities and limitations of NMPC.
