Adaptive Predictive Control Utilizing Both State-Space and Input-Output Models

This paper introduces a controller which integrates a predictive control synthesis based on a multivariable state – space model of the controlled system and an on – line identification of an ARX model corresponding to the state – space model. The used approach then combines both state – space and input – output models. The model parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method. The optimization was realized by minimization of a quadratic objective function. The controller was applied for real-time control of a three – tank – system laboratory model. The objective laboratory model is a two input – two output (TITO) nonlinear system. It is based on experience with authentic industrial control applications. Results of real-time experiments are also included. Key-Words: Predictive control, adaptive control, state-space model, input-output model, difference equations, optimization, recursive identification, multivariable systems