Amin Kargarian

Milad Hasanzadeh, Amin Kargarian, Mehdi Farasat

This paper proposes a dynamic quantum-assisted co-design framework for nonlinear closed-loop systems in which controller parameters and Lyapunov-certificate parameters are redesigned jointly at successive decision epochs. Unlike conventional nonlinear control designs that typically tune controller gains offline and verify stability separately, the proposed method embeds performance improvement and Lyapunov-based stability synthesis within a unified online optimization loop. The main novelty is a two-step computational structure that first contracts the continuous admissible search region around the current operating condition using a Black-Hole-based calibration procedure and then constructs a finite binary representation only over this calibrated region. The encoded objective is obtained from sampled nonlinear closed-loop evaluations and approximated by a local quadratic pseudo-Boolean surrogate, enabling an Ising-type Hamiltonian representation suitable for quantum-assisted optimization. Quantum imaginary time evolution is then used to explore the encoded Hamiltonian, and the resulting candidate bitstrings are decoded into continuous controller and Lyapunov parameters. To reduce dependence on the surrogate model, the decoded candidates are re-evaluated using the original nonlinear closed-loop cost and Lyapunov penalties before the final update is applied. The framework can accommodate different Lyapunov decay specifications by modifying the stability penalty and is validated on first-order nonlinear consensus, second-order nonlinear consensus, and induction-motor drive control examples. The implementation code used to generate the reported results is available at \href{https://github.com/LSU-RAISE-LAB/DQCLS-NS}{GitHub}.

Fouad Hasan, Amin Kargarian

Benders decomposition is widely used to solve large mixed-integer problems. This paper takes advantage of machine learning and proposes enhanced variants of Benders decomposition for solving two-stage stochastic security-constrained unit commitment (SCUC). The problem is decomposed into a master problem and subproblems corresponding to a load scenario. The goal is to reduce the computational costs and memory usage of Benders decomposition by creating tighter cuts and reducing the size of the master problem. Three approaches are proposed, namely regression Benders, classification Benders, and regression-classification Benders. A regressor reads load profile scenarios and predicts subproblem objective function proxy variables to form tighter cuts for the master problem. A criterion is defined to measure the level of usefulness of cuts with respect to their contribution to lower bound improvement. Useful cuts that contain the necessary information to form the feasible region are identified with and without a classification learner. Useful cuts are iteratively added to the master problem, and non-useful cuts are discarded to reduce the computational burden of each Benders iteration. Simulation studies on multiple test systems show the effectiveness of the proposed learning-aided Benders decomposition for solving two-stage SCUC as compared to conventional multi-cut Benders decomposition.