Srinivasa M. Salapaka

Mayank Baranwal, Srinivasa M. Salapaka, Murti V. Salapaka

This paper addresses the problem of output voltage regulation for multiple DC-DC converters connected to a grid, and prescribes a robust scheme for sharing power among different sources. Also it develops a method for sharing 120 Hz ripple among DC power sources in a prescribed proportion, which accommodates the different capabilities of DC power sources to sustain the ripple. We present a decentralized control architecture, where a nested (inner-outer) control design is used at every converter. An interesting aspect of the proposed design is that the analysis and design of the entire multi-converter system can be done using an equivalent single converter system, where the multi-converter system inherits the performance and robustness achieved by a design for the single-converter system. Another key aspect of this work is that the voltage regulation problem is addressed as a disturbance-rejection problem, where {\em unknown} load current is viewed as an external signal, and thus, no prior information is required on the nominal loading conditions. The control design is obtained using robust optimal-control framework. Case studies presented show the enhanced performance of prescribed optimal controllers.

Mayank Baranwal, Srinivasa M. Salapaka

One of the most important challenges facing an electric grid is to incorporate renewables and distributed energy resources (DERs) to the grid. Because of the associated uncertainties in power generations and peak power demands, opportunities for improving the functioning and reliability of the grid lie in the design of an efficient, yet pragmatic distributed control framework with guaranteed robustness margins. This paper addresses the problem of output voltage regulation for multiple DC-DC converters connected to a grid, and prescribes a robust scheme for sharing power among different sources. More precisely, we develop a control architecture where, unlike most standard control frameworks, the desired power ratios appear as reference signals to individual converter systems, and not as internal parameters of the system of parallel converters. This makes the proposed approach suited for scenarios when the desired power ratios vary rapidly with time. Additionally, the proposed control framework is suitable to both centralized and decentralized implementations, i.e., the same control architecture can be employed for voltage regulation irrespective of the availability of common load-current (or power) measurement, without the need to modify controller parameters. The control design is obtained using robust optimal-control framework. Case studies presented show the enhanced performance of prescribed optimal controllers for voltage regulation and power sharing.

Mayank Baranwal, Alireza Askarian, Srinivasa M. Salapaka

This paper addresses the problem of output voltage regulation for multiple DC/DC converters connected to a microgrid, and prescribes a scheme for sharing power among different sources. This architecture is structured in such a way that it admits quantifiable analysis of the closed-loop performance of the network of converters; the analysis simplifies to studying closed-loop performance of an equivalent {\em single-converter} system. The proposed architecture allows for the proportion in which the sources provide power to vary with time; thus overcoming limitations of our previous designs. Additionally, the proposed control framework is suitable to both centralized and decentralized implementations, i.e., the same control architecture can be employed for voltage regulation irrespective of the availability of common load-current (or power) measurement, without the need to modify controller parameters. The performance becomes quantifiably better with better communication of the demanded load to all the controllers at all the converters (in the centralized case); however guarantees viability when such communication is absent. Case studies comprising of battery, PV and generic sources are presented and demonstrate the enhanced performance of prescribed optimal controllers for voltage regulation and power sharing.

Salar Basiri, Dhananjay Tiwari, Srinivasa M. Salapaka

We propose a scalable deep learning framework for parametrized sequential decision-making (ParaSDM), where multiple agents jointly optimize discrete action policies and shared continuous parameters. A key subclass of this setting arises in Facility-Location and Path Optimization (FLPO), where multi-agent systems must simultaneously determine optimal routes and facility locations, aiming to minimize the cumulative transportation cost within the network. FLPO problems are NP-hard due to their mixed discrete-continuous structure and highly non-convex objective. To address this, we integrate the Maximum Entropy Principle (MEP) with a neural policy model called the Shortest Path Network (SPN)-a permutation-invariant encoder-decoder that approximates the MEP solution while enabling efficient gradient-based optimization over shared parameters. The SPN achieves up to 100$\times$ speedup in policy inference and gradient computation compared to MEP baselines, with an average optimality gap of approximately 6% across a wide range of problem sizes. Our FLPO approach yields over 10$\times$ lower cost than metaheuristic baselines while running significantly faster, and matches Gurobi's optimal cost with annealing at a 1500$\times$ speedup-establishing a new state of the art for ParaSDM problems. These results highlight the power of structured deep models for solving large-scale mixed-integer optimization tasks.

Mayank Baranwal, Brian Roehl, Srinivasa M. Salapaka

This paper presents a novel and efficient heuristic framework for approximating the solutions to the multiple traveling salesmen problem (m-TSP) and other variants on the TSP. The approach adopted in this paper is an extension of the Maximum-Entropy-Principle (MEP) and the Deterministic Annealing (DA) algorithm. The framework is presented as a general tool that can be suitably adapted to a number of variants on the basic TSP. Additionally, unlike most other heuristics for the TSP, the framework presented in this paper is independent of the edges defined between any two pairs of nodes. This makes the algorithm particularly suited for variants such as the close-enough traveling salesman problem (CETSP) which are challenging due to added computational complexity. The examples presented in this paper illustrate the effectiveness of this new framework for use in TSP and many variants thereof.