1#. • Basis Weight Control System
nonchaotic particle swarm optimization (SNPSO) algorithm in developing a novel method for identifying the optimal parameters of systems with time delay. Based on the randomness and ergodicity of strange nonchaotic dynamics, the strange nonchaotic sequence was adopted to replace the initialized random particles and linear attenuation weight to improve the global search capability. Furthermore, a mutation rule with strange nonchaotic characteristics was employed to improve global search capability further. To illustrate the effectiveness of SNPSO, it was compared with the other three algorithms (classical particle swarm optimization (PSO), PSO with time-varying acceleration coefficients (PSO-TVAC), and modified particle swarm optimization (MPSO)). Simulation results demonstrated that SNPSO is more suitable for parameter identification of systems with time delay. When the first-order-plus-dead-time (FOPDT) or second-order-plus-dead-time (SOPDT) models were employed to model the FOPDT or SOPDT systems, SNPSO achieved the highest accuracy of accurate matching. To identify high-order systems, SNPSO had the best average and best minimum convergence performances. !CKNOWLEDGMENTS The authors are grateful for the financial support received from the National Natural Science Foundation of China (Grant No. 62073206) and Technical Innovation Guidance Project of Shaanxi Province (Grant No. 2020CGHJ-007). 2EFERENCES [1] Ammar M E, Dumont G. Identification of paper machines cross-directional models in closed-loop. In the 2013 5th International Conference on Modelling, Identification and Control (ICMIC) ICMIC, Cairo, Egypt: 2013, 3-9. [2] Sun G, Nie H, Su Y, Nie W. Research on two-degree-of- freedom IMC-PID control for time-delay systems. Application Research of Computers , 2014, 31(8), 2357-2360. [3] Shan W, Tang W, Wang M, Liu B. Two-degree-of-freedom smith predictor based on fractional order PID controller in paper basis weight. Packaging Engineering , 2017, 38(11), 143-147. [4] Xu Y, Chen D. Partially-linear least-squares regularized regression for system identification. IEEE Transactions on Automatic Control , 2009, 54(11), 2637-2641.
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