Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control

Abstract

In this work a practical study evaluates two parametric modelling approaches -- linear and non-linear (neural) -- for automatic adaptive control. The neural adaptive control is based on a developed hybrid learning technique using an adaptive (on-line) learning rate for a Gaussian radial basis function neural network. The linear approach is used for a self-tuning pole-placement controller. A selective forgetting factor method is applied to both control schemes: in the neural case to estimate on-line the second-layer weights and in the linear case to estimate the parameters of the linear process model. These two techniques are applied to a laboratory-scaled bench plant with the possibility of dynamic changes and different types of disturbances. Experimental results show the superior performance of the neural approach particularly when there are dynamic changes in the process.