Recent Progress In Robotics Viable Robotic Service To Human
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Recent Progress in Robotics: Viable Robotic Service to Human
Author: Sukhan Lee
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-01-14
th This volume is an edition of the papers selected from the 13 International Conference on Advanced Robotics, ICAR 2007, held in Jeju, Korea, August 22-25, 2007, with the theme: “Viable Robotics Service to Human. ” It is intended to deliver readers the most recent technical progress in robotics, in particular, toward the advancement of robotic service to human. To ensure its quality, this volume took only 28 papers out of the 214 papers accepted for publication for ICAR 2007. The selection was based mainly on the technical merit, but also took into consideration whether the subject represents a theme of current interest. For the final inclusion, authors of the selected papers were requested for another round of revision and expansion. In this volume, we organize the 28 contributions into three chapters. Chapter 1 covers Novel Mechanisms, Chapter 2 deals with perception guided navigation and manipulation, and Chapter 3 addresses human-robot interaction and intelligence. Chapters 1, 2 and 3 consist of 7, 13 and 8 contributions, respectively. For the sake of clarity, Chapter 2 is divided further into two parts with Part 1 for Perception Guided Navigation and Part 2 for Perception Guided Manipulation. Chapter 3 is also divided into two parts with Part 1 for Human- Robot Interaction and Part 2 for Intelligence. For the convenience of readers, a ch- ter summary is introduced as an overview in the beginning of each chapter. The chapter summaries were prepared by Dr. Munsang Kim for Chapter 1, Prof.
Linear, Time-varying Approximations to Nonlinear Dynamical Systems
Linear, Time-varying Approximations to Nonlinear Dynamical Systems introduces a new technique for analysing and controlling nonlinear systems. This method is general and requires only very mild conditions on the system nonlinearities, setting it apart from other techniques such as those – well-known – based on differential geometry. The authors cover many aspects of nonlinear systems including stability theory, control design and extensions to distributed parameter systems. Many of the classical and modern control design methods which can be applied to linear, time-varying systems can be extended to nonlinear systems by this technique. The implementation of the control is therefore simple and can be done with well-established classical methods. Many aspects of nonlinear systems, such as spectral theory which is important for the generalisation of frequency domain methods, can be approached by this method.
Automotive Model Predictive Control
Author: Luigi Del Re
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-03-11
Automotive control has developed over the decades from an auxiliary te- nology to a key element without which the actual performances, emission, safety and consumption targets could not be met. Accordingly, automotive control has been increasing its authority and responsibility – at the price of complexity and di?cult tuning. The progressive evolution has been mainly ledby speci?capplicationsandshorttermtargets,withthe consequencethat automotive control is to a very large extent more heuristic than systematic. Product requirements are still increasing and new challenges are coming from potentially huge markets like India and China, and against this ba- ground there is wide consensus both in the industry and academia that the current state is not satisfactory. Model-based control could be an approach to improve performance while reducing development and tuning times and possibly costs. Model predictive control is a kind of model-based control design approach which has experienced a growing success since the middle of the 1980s for “slow” complex plants, in particular of the chemical and process industry. In the last decades, severaldevelopments haveallowedusing these methods also for “fast”systemsandthis hassupporteda growinginterestinitsusealsofor automotive applications, with several promising results reported. Still there is no consensus on whether model predictive control with its high requi- ments on model quality and on computational power is a sensible choice for automotive control.