Keynotes
1 Prof. Xinghuo Yu, RMIT University, Australia
Talk Title: Dealing with Complexity in Cyber-Physical-Social Systems with Simplexity
Abstract: Cyber-Physical-Social Systems (CPSS) represent a broad range of future complex, multidisciplinary, physically and societally aware next-generation engineered systems that integrate embedded computing technologies (cyber part) into the physical world taking social and human considerations. A typical scenario is the future CPSS in Energy where smart grids integrate with social, economic and environmental models to form an energy eco-system that is vital to the future of industry, economy, society and the Nature. Such setting results in enormously increased complexity which requires an innovative way of thinking in dealing with modelling, control and optimisation with efficiency and effectiveness.In this talk, we will first outline some recent developments in CPSS and their technological challenges. We will then discuss a potential ‘simplexity’ problem solving paradigm which advocates simple solutions for complex problems. The implications of adopting such paradigm in dealing with complexity in CPSS will be discussed, and some potential future methods inspired by the Nature will be speculated.A number of real-world cases, including some of our own research, will be used as case studies.
2 Xiaohong Guan, Xi'an Jiaotong University, China
3 Prof. Yang Shi, University of Victoria, Canada
Talk Title: Distributed Model Predictive Control for Cyber-Physical Systems
Abstract: Advanced control technologies for cyber-physical systems have received great attention in the control community due to its wide application areas. Network-induced limitations may be caused by the presence of a communication channel, or because of the efficient assignment of power and other limited resources. Cyber-physical systems represent a large class of smart systems that encompass cyberand physical components, seamlessly integrated and closely interacting to autonomously sense and manipulate the changing state of the physical system. These systems involve a high degree of complexity at numerous spatial and temporal scales and highly networked communications integrating computational and physical components. Model predictive control (MPC) is a promising paradigm for high-performance and cost-effective control of networked and distributed cyber-physical systems. This talk will firstly summarize the major application requirements and challenges to tackle and innovate in designing, implementing, deploying and operating cyber-physical systems. Further, distributed MPC design methods will be presented. Finally, the application of distribute MPC methods will be discussed.
4 Prof. Zhongsheng Hou, Qingdao University, China
Talk Title: PID and Its Puzzles——MFAC and Progress
Abstract: Many practical processes generate and store a huge amount of process data, which contains all the valuable information of the process operations and the equipment. How to use these process data, both on-line and off-line, to directly determine the controller structure, tune the controller parameter, design the output prediction, make the performance assessment, etc., would have great significance when the process models are unavailable. Therefore, the establishment on the data driven control theory is an urgent and important issue both for the theoretical development and field applications of the control theory. This talk includes four parts. The first is a brief survey on the existing problems of PID controller; The second is the dynamic linearization data modeling method for nonlinear systems; The third part will present the model free adaptive control (MFAC), including the indirect MFAC, the direct MFAC, and its progress; The final one is the MFAC application to a benchmark problem.
5 Prof. Wei Ren, University of California, Riverside, USA
Talk Title: Distributed dynamic state estimation in sensor networks: Consistency, confidence, and convergence
Abstract: The problem of distributed dynamic state estimation using networked local agents with sensing and communication abilities, has become a popular research area in recent years due to its wide range of applications such as target tracking, region monitoring and area surveillance. Specifically, we consider the scenario where the local agents take local measurements and communicate with only their nearby neighbors to estimate the state of interest in a cooperative and fully distributed manner. A distributed hybrid information fusion (DHIF) algorithm is proposed in the scenario where the process model of the target and the sensing models of the local agents are linear and time varying. The proposed DHIF algorithm is shown to be fully distributed and hence scalable, to be run in an automated manner and hence adaptive to locally unknown changes in the network, to have agents communicate for only once during each sampling time interval and hence inexpensive in communication, and to be able to track the interested state with uniformly upper bounded estimate error covariance. It is also explored very mild conditions on general directed time-varying graphs and joint network observability/detectability to guarantee the stochastic stability of the proposed algorithm.