"The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them," says Liu with MIT and NEU in a recent article on "Nature". The complexity of the real world keeps growing and raises the question "how much detail of a system do we need to understand in a particular task?". This concern dictates the amount of effort we should employ while modeling the system and the quality of the control strategy accordingly. 

This website will take both aspects into account, i.e. the technique of control and modeling, and the analysis of necessary complexity in doing so. We aim to develop a universal methodology that covers the theory development and the application on real systems, which will cost-effectively save the effort to understand a system from a fundamental perspective. Unlike the traditional methodology that evaluate the effort-cost in the middle of the theory application. We will start by developing a theory that is suitable for a system according to its feature. The systems concerned in this site are notorious for their high-dimensional and high-order complexity and requires the balance between cost and efficiency. We will develop a unique method to approach and seeking a new and feasible way to understand, and ultimately control. The unique systems investigated here are including: engines; human drivers; robots; vehicles.

Other than the effectiveness of control, this site also hits on a more realistic question? "how robust my solution can sustain throughout the evolution of time and environment?" Does it have the power of accuracy throughout its long life time? Will the compromise in the controller deteriorate the overall performance? and by how much and to what degree? Will the damage propagate through the mechanical or electronic bodies of the system? And what is the mechanism? how can we control the damage by inserting a key robust node in the "network" so that the error propagation can be neutralized. We will explore the fundamentals of the network and topology theory in answering this questions. We will also look into the real examples in social network, social dynamics and economics where huge-scale and multi-degrees-embedded nodes intertwines and mutually connected.

This site is developed by Max Zheng Qu, who is a recent graduate from University of Michigan, worked with Geely Automobile Research Institute, and now becomes a new Ph.D student at MIT. This site will archive the previous works by the author and vision for modeling and control of complex system and network .
 
This site is co-authored by Ye Wang, who is a Ph.D candidate in Boston University on Economics. She generously shares her profound knowledge in Sociology and Economics, which impacts the another degree of freedom in terms of the application of network control theory. 

The content will come soon and progressively. The content has been filled in following sections: Engine; Driver; Robotics.