Robust synchronization for 2-D discrete-time coupled dynamical networks

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 IEEEIn this paper, a new synchronization problem is addressed for an array of 2-D coupled dynamical networks. The class of systems under investigation is described by the 2-D nonlinear state space model which is oriented from the well-known Fornasini–Marchesini second model. For such a new 2-D complex network model, both the network dynamics and the couplings evolve in two independent directions. A new synchronization concept is put forward to account for the phenomenon that the propagations of all 2-D dynamical networks are synchronized in two directions with influence from the coupling strength. The purpose of the problem addressed is to first derive sufficient conditions ensuring the global synchronization and then extend the obtained results to more general cases where the system matrices contain either the norm-bounded or the polytopic parameter uncertainties. An energy-like quadratic function is developed, together with the intensive use of the Kronecker product, to establish the easy-to-verify conditions under which the addressed 2-D complex network model achieves global synchronization. Finally, a numerical example is given to illustrate the theoretical results and the effectiveness of the proposed synchronization scheme.This work was supported in part by the National Natural Science Foundation of China under Grants 61028008 and 61174136, the International Science and Technology Cooperation Project of China under Grant No. 2009DFA32050, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Predictive disturbance management in manufacturing control systems

The manufacturing systems are dynamic, non-linear and often chaotic environments, subject to the occurrence of unexpected disturbances that leads to deviations from the initial plans and usually degrades the performance of the system. The treatment of exceptions and disturbances is one major requirement to the next generation of intelligent manufacturing control systems, that should be able to treat emergency as a normal situation. In this paper, a predictive disturbance management approach that transforms the traditional “fail and recover” practices into “predict and prevent” practices, improving the control system performance, will be presented. The predictive mechanism is based in the frequency analysis of each type of disturbance to find repetitive patterns in their occurrence

On the importance of nonlinear modeling in computer performance prediction

Computers are nonlinear dynamical systems that exhibit complex and sometimes even chaotic behavior. The models used in the computer systems community, however, are linear. This paper is an exploration of that disconnect: when linear models are adequate for predicting computer performance and when they are not. Specifically, we build linear and nonlinear models of the processor load of an Intel i7-based computer as it executes a range of different programs. We then use those models to predict the processor loads forward in time and compare those forecasts to the true continuations of the time seriesComment: Appeared in "Proceedings of the 12th International Symposium on Intelligent Data Analysis

The role of visual management in collaborative integrated planning and control for engineer-to-order building systems

Visual Management is a key approach in the implementation of Lean Production. It emphasizes the importance of developing easy-to-understand visual devices for sharing knowledge within an organization. Such visual devices can play an important role in mitigating the complexity of engineer-to-order production systems. A major difficulty in managing engineer-to-order prefabricated building systems is the need to integrate planning and control of different processes, such as design, fabrication and assembly on site, in a multiple project environment. This paper reports preliminary findings on the implementation of visual devices for collaborative and integrated planning and control in a Steel Fabricator, which designs, fabricates and assembles steel structures. The aim of this paper is to understand how visual management tools can contribute to improve the effectiveness of planning and control in this environment. A set of visual devices have been used in the planning and control system in this company, including a panel that makes available information about 200 simultaneous contracts in an easy-to-understand way. The implementation of those tools has enhanced the participation of different people in the planning process from operational levels or from the different production units

Modeling of hyper-adaptability: from motor coordination to rehabilitation

Hyper-adaptability is an ability of humans and animals to adapt to large-scale changes in the nervous system or the musculoskeletal system, such as strokes and spinal cord injuries. Although this adaptation may involve similar neural processes with normal adaptation to usual environmental and body changes in daily lives, it can be fundamentally different because it requires ‘construction’ of the neural structure itself and ‘reconstitution’ of sensorimotor control rules to compensate for the changes in the nervous system. In this survey paper, we aimed to provide an overview on how the brain structure changes after brain injury and recovers through rehabilitation. Next, we demonstrated the recent approaches used to apply computational and neural network modeling to recapitulate motor control and motor learning processes. Finally, we discussed future directions to bridge the gap between conventional physiological and modeling approaches to understand the neural and computational mechanisms of hyper-adaptability and its applications to clinical rehabilitation