Optimization of Long-Run Average-Flow Cost in Networks With Time-Varying Unknown DemandBauso, D., Blanchini, F. & Pesenti, R., Jan-2010, In : IEEE Transactions on Automatic Control. 55, 1, p. 20-31 12 p.
Research output: Contribution to journal › Article › Academic › peer-review
We consider continuous-time robust network flows with capacity constraints and unknown but bounded time-varying demand. The problem of interest is to design a control strategy off-line with no knowledge of the demand realization. Such a control strategy regulates the flow on-line as a function of the realized demand.
We address both the case of systems without and with buffers. The main novelty in this work is that we consider a convex cost which is a function of the long-run average-flow and average-demand. We distinguish a worst-case scenario where the demand is the worst-one from a deterministic scenario where the demand has a neutral behavior. The resulting strategies are called min-max or deterministically optimal respectively. The main contribution are constructive methods to design either min-max or deterministically optimal strategies. We prove that while the min-max optimal strategy is memoryless, i.e., it is a piece-wise affine function of the current demand, deterministically optimal strategy must keep memory of the average flow up to the current time.
|Number of pages||12|
|Journal||IEEE Transactions on Automatic Control|
|Publication status||Published - Jan-2010|
- Average flow cost, flow control, gradient-based control, min-max optimality, uncertain demand, MULTI-INVENTORY SYSTEMS, ROBUST OPTIMIZATION, DESIGN, INPUTS