870 resultados para Rough fuzzy controller
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随着电子技术和计算机技术的不断发展,工业生产过程的控制系统正在向着智能化、数字化和网络化的方向发展。传统的集散控制方式和计算机分层控制方式已经开始让位于智能终端与网络结合的总线网络控制方式。当今,在工厂中过程控制环境下的分布式自动化系统变得越来越复杂,尤其系统内部的各设备之间需要快速交换大量的信息,以便实现对被控系统更为精确的控制和提供一些辅助的评价函数。这就意味着要不断增加带宽和提高通信速率以满足网络通信的需要。在现有的多种可利用网络设备中,CAN总线以其清晰的定义、极高的可靠性及其独特的设计,被认为是最能有效地解决这一问题的途径之一。而且市场上基于通信技术的产品中,就实时性考虑,由于CAN总线采用的非表意性的通信方式,因此其结构更为简单,实时性更好。基于此背景,我们以CAN总线作为通信媒介,将分布于各控制现场的传感器、执行器和控制器有序地连接起来,构成了一个基于CAN总线的分布式局域网络控制系统。本文首先介绍了基于CAN总线的分布式数据采集与控制系统的总体结构。然后从硬件方面描述了基于CAN总线的通信协议转换单元、数据采集单元和输出控制单元的功能、硬件配置及各单元功能的具体实现过程,给出了各单元的性能指标。软件方面,以C语言作为平台,开发了基于CAN总线的上位计算机管理与监控软件,实现了对整个网络设备的系统管理和系统控制功能。对于该总线系统,作者运用了PID控制和模糊控制算法实现了对水箱液位的控制,达到了理想的效果。基于CAN总线的控制系统很好地解决了集散控制系统难以解决的难题,模糊控制的应用能很好地把总线控制系统应用到具有非线性、大时滞和难于获得精确模型的控制系统中。
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We describe the automatic synthesis of a global nonlinear controller for stabilizing a magnetic levitation system. The synthesized control system can stabilize the maglev vehicle with large initial displacements from an equilibrium, and possesses a much larger operating region than the classical linear feedback design for the same system. The controller is automatically synthesized by a suite of computational tools. This work demonstrates that the difficult control synthesis task can be automated, using programs that actively exploit knowledge of nonlinear dynamics and state space and combine powerful numerical and symbolic computations with spatial-reasoning techniques.
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This thesis presents methods for implementing robust hexpod locomotion on an autonomous robot with many sensors and actuators. The controller is based on the Subsumption Architecture and is fully distributed over approximately 1500 simple, concurrent processes. The robot, Hannibal, weighs approximately 6 pounds and is equipped with over 100 physical sensors, 19 degrees of freedom, and 8 on board computers. We investigate the following topics in depth: distributed control of a complex robot, insect-inspired locomotion control for gait generation and rough terrain mobility, and fault tolerance. The controller was implemented, debugged, and tested on Hannibal. Through a series of experiments, we examined Hannibal's gait generation, rough terrain locomotion, and fault tolerance performance. These results demonstrate that Hannibal exhibits robust, flexible, real-time locomotion over a variety of terrain and tolerates a multitude of hardware failures.
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X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen. Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 2007.
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X. Fu, Q. Shen and R. Zhao. 'Towards fuzzy compositional modelling,' In Proceedings of the 16th International Conference on Fuzzy Systems, 2007, pp. 1233-1238. Sponsorship: EPSRC
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X. Fu and Q. Shen. 'Knowledge representation for fuzzy model composition', in Proceedings of the 21st International Workshop on Qualitative Reasoning, 2007, pp. 47-54. Sponsorship: EPSRC
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R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006.
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Z. Huang and Q. Shen. Fuzzy interpolative reasoning via scale and move transformation. IEEE Transactions on Fuzzy Systems, 14(2):340-359.
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K. Rasmani and Q. Shen. Data-driven fuzzy rule generation and its application for student academic performance evaluation. Applied Intelligence, 25(3):305-319, 2006.
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M. Galea and Q. Shen. Iterative vs Simultaneous Fuzzy Rule Induction. Proceedings of the 14th International Conference on Fuzzy Systems, pages 767-772.
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K. Rasmani and Q. Shen. Subsethood-based Fuzzy Rule Models and their Application to Student Performance Classification. Proceedings of the 14th International Conference on Fuzzy Systems, pages 755-760, 2005.
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M. Galea, Q. Shen and J. Levine. Evolutionary approaches to fuzzy modelling. Knowledge Engineering Review, 19(1):27-59, 2004.
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Z. Huang and Q. Shen. Preserving Piece-wise Linearity in Fuzzy Interpolation. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 105-112.
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M. Galea and Q. Shen. Fuzzy rules from ant-inspired computation. Proceedings of the 13th International Conference on Fuzzy Systems, pages 1691-1696, 2004.
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M. Galea and Q. Shen. FRANTIC - A system for inducing accurate and comprehensible fuzzy rules. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 136-143.