ILC-based two-layer strategy for economic performance improvement in industrial MPC systems. (December 2021) - Shi, Yao; Zhang, Zhiming; Xie, Lei; Su, Hongye
Abstract: The conventional LQG based economic performance design has found its difficulty in industrial application and so far, there is still no systematic and effective way to improve economic performance. As learned from the LQG benchmark performance assessment method, the economic performance improvement in MPC systems can be realized through adjusting controller parameters in addition to the well-known setpoints change approach. Therefore, we take advantage of LQG and iterative learning control (ILC) to propose a new two-layer periodical economic performance improvement strategy applicable in industrial MPC systems. By dividing the whole time into multiple intervals called periods and optimize the performance periodically, the economics finally reach its optimal. Promoted twice in a certain period, the performance acquires its first promotion through fixed variance obtained from the lower MPC layer, which transforms the nonlinear economic performance function (EPF) of LQG into a linear one. The ILC-based weight coefficients adjustment algorithm then provides the parameters to the MPC controller in the next period with the updating principle based on the idea of minimizing the tracking error between the current controller economic performance and an optimal one, which realizes the second performance improvement. Room for the second promotion is analyzed and convergence of the algorithm is proved. Finally, the effectiveness and applicability of the strategy are verified via a typical industrial separation process. Highlights: The presented scheme proposes a practical strategy to improve the performance of the MPC controller. The Riccati equation is avoided and a reachable economic performance is ensured. The proposed method provides specific guidance rules for weight coefficients, helping engineers determine proper parameters for better economic performance, which makes sense to practical significance. The proposed method abandons the tuning parameter and directly changes the weight coefficients, which is more universal and more realistic.