Nonlinear Iterative Optimization Process for Multichannel Remote Parameter Control

In this article, compared with traditional Remote Parameter Control (RPC), the iterative process is improved based on linear transfer function (TF) estimation of the nonlinear dynamic system. In the improved RPC, the iteration coefficient is designed according to the convergence condition of the nonlinear iterative process, so that the convergence level, convergence speed, and iteration stability could be improved. The difference between the traditional and the improved RPC iterative process is discussed, the RPC iterative process of the nonlinear system is analyzed, and channel decoupling for Multi-Input Multi-Output (MIMO) system based on eigen-decomposition of the system TF and linear TF estimation is introduced. It assumes that the eigenvector matrix of the system TF remains the same, and the linear TF in the iterative process is estimated and updated, which is used for iterative calculation. The method for iteration coefficient is designed according to the nonlinear system convergence condition of the iterative process. The whole theory is verified on a two-channel electrohydraulic servo system and a lightweight motorcycle. The optimization strategy can be used not only for motorcycles but also for general dynamic systems with the same number of inputs and outputs. The experiment results show that the improved RPC is superior to the traditional RPC in the convergence level, convergence speed, and iteration stability. The improved algorithm makes the iterative process more effective, faster, and more stable. In the practical application of RPC, the results can be reproduced better, as well as the time and manpower can be saved.