Real-Time Model Predictive Control for Shipboard Power Management Using the IPA-SQP Approach

• Published in: IEEE transactions on control systems technology Vol. 23; no. 6; pp. 2129 - 2143
• Main Authors: Hyeongjun Park, Jing Sun, Pekarek, Steven, Stone, Philip, Opila, Daniel, Meyer, Richard, Kolmanovsky, Ilya, DeCarlo, Raymond
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Dynamical systems; Electric power generation; Integrated perturbation analysis and sequential quadratic programming (IPA-SQP); integrated power system (IPS); Marine vehicles; Mathematical models; model predictive control (MPC); Optimization; Performance evaluation; Perturbation methods; Power management; power management control (PMC); Power system dynamics; Predictive control; Quadratic programming; Real time; real-time optimization; Real-time systems; real-time optimization; power management control (PMC); Integrated perturbation analysis and sequential quadratic programming (IPA-SQP); integrated power system (IPS); model predictive control (MPC)
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2015.2402233

Shipboard integrated power systems, the key enablers of ship electrification, call for effective power management control (PMC) to achieve optimal and reliable operation in dynamic environments under hardware limitations and operational constraints. The design of PMC can be treated naturally in a model predictive control (MPC) framework, where a cost function is minimized over a prediction horizon subject to constraints. The real-time implementation of MPC-based PMC, however, is challenging due to computational complexity of the numerical optimization. In this paper, an MPC-based PMC for a shipboard power system is developed and its real-time implementation is investigated. To meet the requirements for real-time computation, an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) algorithm is applied to solve a constrained MPC optimization problem. Several operational scenarios are considered to evaluate the performance of the proposed PMC solution. Simulations and experiments show that real-time optimization, constraint enforcement, and fast load following can be achieved with the IPA-SQP algorithm. Different performance attributes and their tradeoffs can be coordinated through proper tuning of the design parameters.