Sequential Synthesis of Distributed Controllers for Cascade Interconnected Systems

We consider the problem of designing distributed controllers to ensure passivity of a large-scale interconnection of linear subsystems connected in a cascade topology. The control design process needs to be carried out at the subsystem-level with no direct knowledge of the dynamics of other subsystems in the interconnection. We present a distributed approach to solve this problem, where subsystem-level controllers are locally designed in a sequence starting at one end of the cascade using only the dynamics of the particular subsystem, coupling with the immediately preceding subsystem and limited information from the preceding subsystem in the cascade to ensure passivity of the interconnected system up to that point. We demonstrate that this design framework also allows for new subsystems to be compositionally added to the interconnection without requiring redesign of the pre-existing controllers.Comment: Accepted to appear in the proceedings of the American Control Conference (ACC) 201

INVALS: An Efficient Forward Looking Inventory Allocation System

We design an Inventory Allocation System (INVALS) that, for each item-store combination, plans the quantity to be allocated from a warehouse replenishing multiple stores using trailers, while respecting the typical supply-chain constraints. We formulate a linear objective function which when maximised computes the allocation by considering not only the immediate store needs, but also its future expected demand. Such forward-looking allocation significantly improves the labour and trailer utilisation at the warehouse. To reduce overstocking, we adapt from our objective to prioritise allocating those items in excess which are sold faster at the stores, keeping the days of supply (DOS) to a minimum. For the proposed formulation, which is an instance of Mixed Integer Linear Programming (MILP), we present a scalable algorithm using the concepts of submodularity and optimal transport theory by: (i) sequentially adding trailers to stores based on maximum incremental gain, (ii) transforming the resultant linear program (LP) instance to an instance of capacity constrained optimal transport (COT), solvable using double entropic regularization and incurring the same computational complexity as the Sinkhorn algorithm. When compared against the planning engine that does the allocation only for immediate store needs, INVALS increases on an average the labour utilization by 34.70 and item occupancy in trailers by 37.08. The DOS distribution is also skewed to the left indicating that higher demand items are allocated in excess, reducing the days they are stocked. We empirically observed that for ~ 90% of replenishment cycles, the allocation results from INVALS are identical to the globally optimal MILP solution

Mixed Voltage Angle and Frequency Droop Control for Transient Stability of Interconnected Microgrids with Loss of PMU Measurements

We consider the problem of guaranteeing transient stability of a network of interconnected angle droop controlled microgrids, where voltage phase angle measurements from phasor measurement units (PMUs) may be lost, leading to poor performance and instability. In this paper, we propose a novel mixed voltage angle and frequency droop control (MAFD) framework to improve the reliability of such angle droop controlled microgrid interconnections. In this framework, when the phase angle measurement is lost at a microgrid, conventional frequency droop control is temporarily used for primary control in place of angle droop control. We model the network of interconnected microgrids with the MAFD architecture as a nonlinear switched system. We then propose a dissipativity-based distributed secondary control design to guarantee transient stability of this network under arbitrary switching between angle droop and frequency droop controllers. We demonstrate the performance of this control framework by simulation on a test 123-feeder distribution network.Comment: American Control Conference (ACC), 202

A scalable solution for the extended multi-channel facility location problem

We study the extended version of the non-uniform, capacitated facility location problem with multiple fulfilment channels between the facilities and clients, each with their own channel capacities and service cost. Though the problem has been extensively studied in the literature, all the prior works assume a single channel of fulfilment, and the existing methods based on linear programming, primal-dual relationships, local search heuristics etc. do not scale for a large supply chain system involving millions of decision variables. Using the concepts of sub-modularity and optimal transport theory, we present a scalable algorithm for determining the set of facilities to be opened under a cardinality constraint. By introducing various schemes such as: (i) iterative facility selection using incremental gain, (ii) approximation of the linear program using novel multi-stage Sinkhorn iterations, (iii) creation of facilities one for each fulfilment channel etc., we develop a fast but a tight approximate solution, requiring $\mathcal{O}\left(\frac{3+k}{m}ln\left(\frac{1}{\epsilon}\right)\right)$ instances of optimal transport problems to select k facilities from m options, each solvable in linear time. Our algorithm is implicitly endowed with all the theoretical guarantees enjoyed by submodular maximisation problems and the Sinkhorn distances. When compared against the state-of-the-art commercial MILP solvers, we obtain a 100-fold speedup in computation, while the difference in objective values lies within a narrow range of 3%