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In this paper, a modified sequential quadratic programming (SQP)-based model predictive control (MPC) algorithm is developed and implemented for power plant cycling applications. These applications are challenging both in terms of controller performance and computational cost due to their associated high dimensionality. The modified SQP algorithm is based on the backtracking line search framework, employing a group of relaxed step acceptance conditions for faster convergence. The developed MPC with the proposed modified SQP algorithm is compared to a dynamic matrix control (DMC)-based linear MPC, a classical SQP-based nonlinear MPC, and a direct transcription-based nonlinear MPC in terms of tracking performance and computational efficiency. Successful plantwide MPC implementation scenarios for a supercritical pulverized coal-fired power plant with monoethanolamine-based carbon capture and renewable power penetration are presented. The closed-loop results show that the proposed modified SQP-based nonlinear MPC improves the computational efficiency by over 20% with similar tracking performance when compared to the implemented classical SQP-based and direct transcription-based nonlinear MPC controllers, for similar implementation platforms.
He et al. (Tue,) studied this question.