An Improved Adaptive PID Controller Based on Online LSSVR with Multi RBF Kernel Tuning

摘要

In this paper, the effects of using multi RBF kernel for an online LSSVR on modeling and control performance are investigated. The Jacobian information of the system is estimated via online LSSVR model. Kernel parameter determines how the measured input is mapped to the feature space and a better plant model can be achieved by discarding redundant features. Therefore, introducing flexibility in kernel function helps to determine the optimal kernel. In order to interfuse more flexibility to the kernel, linear combinations of RBF kernels have been utilized. The purpose of this paper is to improve the modeling performance of the LSSVR and also control performance obtained by adaptive PID by tuning bandwidths of the RBF kernels. The proposed method has been evaluated by simulations carried out on a continuously stirred tank reactor (CSTR), and the results show that there is an improvement both in modeling and control performances.