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学术报告

发布时间:2017-12-29  浏览次数:16

报告时间: 9:00am-11:30am, 20180110

报告地点: 61号楼4172会议室


报告人

题目

周至宏(Jyh-Horng Chou)教授(台湾高雄应用科技大学,IEEE Fellow

Applications of Computational Intelligence to Industry 4.0

林法正(Faa-Jeng Lin)教授  (台湾中央大学,IEEE Fellow

伺服马达的驱动与智能型控制

蔡清池(Ching-Chih Tsai)教授(台湾中兴大学,IEEE Fellow

Intelligent Adaptive Model Predictive Control Methods with Their Applications to Industrial processes and Intelligent Mobile Robots


周至宏, 台湾高雄应用科技大学教授,IEEE Fellow, IET Fellow, 研究领域为:系统动力与控制,稳健优化技术,人工智能,资讯技术与系统整合,自动化系统整合技术,数值分析与计算数学,已发表相关期刊和会议论文400余篇。


报告简介:

 “工业4.0”是德国于汉诺威工业展时提出的技术发展策略,它旨在打造一个将网络技术、软件技术、物联网(Internet of Things)技术、云计算技术与大数据(Big Data)技术整合起来的完全数字化的智能制造生产系统。“工业4.0”是将工业自动化与信息智能化相融合,以达到智能化生产的目标。2013年德国联邦教育及研究部、联邦经济及科技部,将其纳入德国“高技术战略2020”十大项目。自德国提出“工业4.0”概念后,各国纷纷制定工业升级发展计划,台湾地区随即制定了“生产4.0”、“智能机械”计划等产业发展方向。在智能化的技术发展与应用方面,与计算智能(Computational Intelligence)息息相关。  

此次演讲将针对“工业4.0”、智能机械等,以其精神、技术与发展、技术程度的检视为研讨议题,分享学者个人的思路与观点,以及分享研究团队在产业界的研发技术与应用成果。


Faa-Jeng Lin, Professor in National Central University, IEEE Fellow, IET Fellow. His research interests include intelligent control theories (fuzzy systems, neural networks and evolutionary computation), nonlinear control theories (adaptive and sliding-modZgbE'EZ applications, AC motor servo drives, ultrasonic motor drives, wind turbine generation systems, inverters/converters, DSP-based computer control systems and microgrid. He has published 202 SCI journal papers including 89 IEEE Trans. papers and 125 conference papers and 15 patents in the areas of intelligent control, nonlinear control, motor drives, and mechatronics.


Report Content: Intelligent control systems including fuzzy and neural network systems have attracted the growing interest of researches and engineers in various scientific and engineering areas. This talk focuses on the development of several intelligent control systems for various applications of servo motor drives including hybrid supervisory control using recurrent fuzzy neural network controller for tracking periodic inputs, DSP-based cross-coupled synchronous control for dual linear motors via intelligent complementary sliding mode control, intelligent double integral sliding-mode control for five-degree-of-freedom active magnetic bearing and fault tolerant control for six-phase PMSM drive system via intelligent complementary sliding mode control using TSKFNN-AMF.


Ching-Chih Tsai, Professor in National Chung-Hsing University, IEEE Fellow, IET Fellow. His current interests include advanced nonlinear control methods, deep model predictive control, fuzzy control, neural-network control, advanced mobile robotics, intelligent service robotics, intelligent mechatronics, intelligent learning control methods with their applications to industrial processes and intelligent machinery. Dr. Tsai has published more than 500 technical papers, and seven patents in the fields of control theory, systems technology and applications.


Report Content: By incorporating the merits of fuzzy modeling and recurrent fuzzy neural networks, this talk will present you four new adaptive predictive control methods of a class of nonlinear discrete-time time-delay systems not only for guaranteed stability but also for precise setpoint tracking and disturbance rejection. The four methods are a generalized predictive control method using recurrent fuzzy neural networks, an intelligent adaptive two-degrees-of-freedom control by combining a Takagi-Sugeno-Kang (TSK) type recurrent fuzzy neural network adaptive inverse model feedforward controller with a stochastic adaptive model reference predictive controller an adaptive predictive proportional-integral-derivative (PID) control approach by utilizing recurrent wavelet neural networks, and an adaptive predictive PID control via fuzzy wavelet neural networks. Through many simulations and experiments, the four methods are shown effective and useful. Last but not least, adaptive model predictive control methods with deep reinforcement learning are briefly introduced and shown effective for intelligent vehicle and mobile robots.


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