Vikas Chandan

  • Curriculum Vitae
  • vchanda2(at)illinois(dot)edu
  • Doctoral Candidate (Expected Graduation – May 2013)
  • M.S. – University of Illinois at Urbana-Champaign, 2010
  • B. Tech. (Hons.) – Indian Institute of Technology, Kharagpur, India, 2006
  • Research Interests:  Large scale thermal management systems, Model predictive control, Decentralized control, Optimal control, Clustering

 

The overall focus of my research within the Alleyne Research Group is on the modeling and control of large scale energy management systems. Some specific research efforts based on this theme are described below.

Decentralized Thermal Control of Building Systems

Buildings account for around 40% of the total energy consumption and contribute more than one-third in greenhouse emissions in the United States. My doctoral research aims to develop modeling and control tools for intelligent energy management in large scale thermal management systems such as buildings.

Energy requirements for heating and cooling of buildings constitute a major fraction of end use energy consumed. Therefore, it is important to provide the occupant comfort requirements in buildings in an energy efficient manner. However, buildings are large scale complex systems, susceptible to sensor, actuator or communication network failures in their thermal control infrastructure, that can affect their performance in terms of occupant comfort and energy efficiency. The degree of decentralization in the control architecture determines a fundamental trade-off between performance and robustness. My doctoral research studies the problem of thermal control of buildings from the perspective of partitioning them into clusters for decentralized control, to balance underlying performance and robustness requirements. We attempt to derive measures of deviation in performance and robustness between centralized and decentralized architectures and investigate the use of appropriate clustering algorithms  to determine decentralized control architectures which provide a satisfactory trade-off between the underlying performance and robustness objectives.

My research also studies the problem of decentralized control design based on the architectures obtained using the above methodologies. We are exploring the use of decentralized extended state observers to address the issue of unavailability of unknown states and disturbances in the system. We also aim to validate and verify the proposed control architecture selection and decentralized control design methodologies in simulation in real world multi-zone building environments.

Modeling and Control of Hydronic Building HVAC Systems

For my masters’ thesis, I studied the modeling and distributed control of water-based (hydronic) energy management systems which are often used for campus and district heating and cooling applications. Energy efficient operation of such systems requires intelligent energy management strategies, which necessitates an understanding of the complex dynamical interactions among its components from a mathematical and physical perspective. In this work, I applied concepts from linear graph theory to model complex hydronic networks. Further, time-scale decomposition techniques were employed to obtain a more succinct representation of the overall system dynamics. The proposed model was then used to design model predictive control (MPC) strategies which were compared with traditional feedback control schemes using a simulated chilled water system as a case study. In particular, a distributed predictive control approach was proposed which was found to result the best trade-off in the multidimensional evaluation framework of `regulation’, `optimality’, `reliability’ and `computational complexity’.

For my research overview slides, click here.

Selected Publications

For a complete list of publications, see my CV.