As modern industrial and manufacturing systems get more and more complex, it becomes difficult to model and analyze them. On one hand, existing methods are not efficient enough in handling complexity at such a scale. On the other hand, vast amount of data are being produced everyday by industrial processes. It is desired to have methods that can handle complex systems and the vast amount of data. In this presentation, we will introduce a new mechanism for intelligent control of real-world large scale and complex problems using the concept of parallel control and management based on the ACP approach, that is, artificial systems as models, computational experiments for analysis, and parallel execution for control and management of complex systems. Neural networks, fuzzy logic and other methods in computational intelligence are critical to the implementation of parallel control and management. Particularly, we will discuss the use of adaptive dynamic programming (ADP), agent-based control (ABC), data-based decision-making and machine learning in ACP-based methods as well as some examples of their

real-world applications.

 

刘德荣目前主要从事智能控制理论及应用、人工神经网络、计算神经科学、电力系统运行与控制、汽车发动机控制等方面的研究工作。共发表了9本书和70多篇国际杂志论文。获得过圣母大学的麦克尔*博克奖学金；斯蒂文斯理工学院的哈维*戴维斯杰出教学奖；美国国家科学基金会教授早期事业发展奖；伊利诺依大学的大学学者奖。2008年，获得了中国国家自然科学基金委"海外杰出青年合作研究基金"。曾任IEEE控制系统学会会议编辑委员会成员、IEEE电路与系统汇刊-I的编委、IEEE信号处理汇刊编委、IEEE神经网络汇刊编委和快报编辑、Automatica的编委、IEEE计算智能杂志编委、IEEE电路与系统杂志编委、IEEE计算智能学会电子快报编辑。现任IEEE神经网络汇刊主编、IEEE控制系统技术汇刊编委、神经计算杂志编委、国际神经系统杂志编委、控制科学与工程杂志编委、《中国科学：信息科学》编委、《控制工程》编委。曾担任过IEEE计算智能学会的理事和IEEE电路与系统学会的神经网络技术委员会主席。于2008年创立IEEE计算智能学会的的近似动态规划和增强式学习技术委员会并任该技术委员会主席。于2009年当选为国际神经网络学会的理事。