NARMA model is a simple and effective way to represent nonlinear systems, based on the NARMA model, NARMA-L2 controller is designed and has been successfully applied in the literature. Success of NARMA-L2 controller is directly related to the precision with which controlled systems’ dynamics can be estimated. In this paper, online SVR is utilized to obtain controlled plant’s subdynamics and consequently this information is used in the construction of NARMA-L2 controller. Hence functionality of NARMA-L2 controllers and high generalization capability of SVR are combined. Also, SVR formulates a convex optimization problem and therefore guarantees global optimum solution. The proposed method is assessed by performing simulations on a nonlinear CSTR system, the robustness of the designed controller is also tested under noisy and uncertainty conditions.

NARMA模型是表示非线性系统的一种简单有效的方法，在NARMA模型的基础上，设计了NARMA-L2控制器，并已在文献中成功应用。NARMA-L2控制器的成功与可估计受控系统动力学的精度直接相关。在本文中，在线SVR用于获得受控工厂的子动力学，因此该信息可用于NARMA-L2控制器的构造。因此，将NARMA-L2控制器的功能和SVR的高泛化能力结合在一起。此外，SVR提出了凸优化问题，因此可以保证全局最优解。通过在非线性CSTR系统上进行仿真来评估所提出的方法，还在噪声和不确定性条件下测试了所设计控制器的鲁棒性。