Numerous future military and civilian applications of intelligent systems will make use of several distributed subsystems working in cooperation and coordination to achieve their objectives. The major advantage of designing of such systems is that the overall system would possess qualities such as performance, adaptability, and robustness to uncertainties and failures that will far exceed the achievable qualities of individual subsystems.

While, the technological framework to develop such cooperative distributed systems has been facilitated by advances in communication, computation, and sensing, these systems have been developed in an ad-hoc manner. A systematic design of these systems has huge potential in terms of enhancement of their performance and efficiency. Realizing a control framework of such complex systems presents considerable challenges in terms of modeling and predicting the behavior of such systems, processing vast amount of information, and obtaining control laws which are scalable.

People in the CDS lab are engaged in developing new decentralized control and information processing algorithms at the intersection of control theory, probability theory, estimation theory, statistical mechanics, and mathematical biology.