Control and Signal Processing Lab Seminar Series - Wednesday 10th of October

Seminar/Forum

Control and Signal Processing Lab Seminar Series - Wednesday 10th of October

Richard Newton Room
Electrical and Electronic Engineering Building 193

Booking not required

Further Details

T: (03) 9035 8028

cskey@unimelb.edu.au

Dr. Alireza Farhadi, Research Fellow, Electrical and Electronic Engineering Department, The University of Melbourne will present Performance, Information Pattern Trade-Offs and Computational Complexity Analysis of a Consensus Based Distributed Optimization Method

This talk is concerned with a cross-disciplinary approach for the convergence of computer science and control in large scale networked control systems. In this talk a simple consensus based distributed optimization method is presented which approximates the solution of a linear quadratic optimal control problem subject to constraints using distributed decision makers subject to non-classical information pattern. This method can be seen as a mechanism for distributing the computational load of the centralized control to distributed decision makers, which work together in parallel and approximate the optimal solution. Distributed decision makers are constrained in terms of the pattern of local computation and information exchange, as a mechanism for managing the corresponding overheads. Feasibility (i.e., constraints satisfaction by the approximated solutions), convergence, and optimality of the method are shown. The computational complexity of this method is compared with the centralized method for Australia’s automated irrigation networks. It is shown that for automated irrigation networks, the computational complexity of the centralized method in terms of the number of subsystems is of the order of six, while the computational complexity of the proposed consensus based distributed optimization method in general is quadratic. Therefore, as will be shown, there is a significant advantage in terms of computational complexity in using the proposed consensus based distributed optimization method in large scale networks. Trade-offs between number of subsystems, computational complexity, and tuning parameters of the consensus based distributed optimization method, are also illustrated for Australia’s automated irrigation networks.

All Seminar/Forums