Li Junhong, Zong Tiancheng, Lu Guoping
School of Electrical Engineering, Nantong University, Nantong 226019, PR China.
ISA Trans. 2022 Jan;120:89-98. doi: 10.1016/j.isatra.2021.03.021. Epub 2021 Mar 25.
This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems.
本文研究具有未知时滞的 Hammerstein-Wiener（H-W）非线性系统的参数估计问题。针对此类时滞系统，提出了线性变权粒子群算法。该算法将非线性系统辨识问题转化为参数空间中的函数优化问题，然后利用粒子群优化的并行搜索能力和迭代辨识技术，实现所有参数及未知时滞的同时估计。最后，从最优参数中分离出线性子模块、非线性子模块及时间延迟中的参数。此外，给出了两个示例以评估所提方法的有效性。仿真结果表明，该方法在估计具有未知时滞的 H-W 系统时具有收敛速度快、估计精度高的特点，并将其应用于床层温度系统的辨识。
