A new radial basis function neural network based multi-variable adaptive pole-zero placement controller

Citation

Abdullah R, Hussain A & Zayed AS (2006) A new radial basis function neural network based multi-variable adaptive pole-zero placement controller. In: 2006 IEEE International Conference on Engineering of Intelligent Systems. 2006 IEEE International Conference on Engineering of Intelligent Systems, Islamabad, Pakistan, 22.04.2006-23.04.2006. Piscataway, NJ: IEEE. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1703158&abstractAccess=no&userType=inst; https://doi.org/10.1109/ICEIS.2006.1703158

Abstract
In this paper a new multi-variable adaptive controller algorithm for non-linear dynamical systems has been derived which employs the radial basis function (RBF) neural network. In the proposed controller, the unknown plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a non-linear `learning' sub-model. The parameters of the linear sub-model are identified by a recursive least squares (RLS) algorithm with a directional forgetting factor, whereas the unknown non-linear sub-model is modeled using the RBF neural network resulting in a new multi-variable non-linear controller with a generalized minimum variance performance index. In addition, the new controller overcomes the shortcomings of other linear control designs and provides an adaptive mechanism which ensures that both the closed-loop poles and zeros are placed at their pre-specified positions. Simulation results using a non-linear multi-input multi-output (MIMO) plant model demonstrate the effectiveness of the proposed controller