INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL Int. J. Robust Nonlinear Control 2010; 20:1817–1835 Published online 9 December 2009 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/rnc.1549 Nonlinear predictive control of smooth nonlinear systems based on Volterra models. Application to a pilot plant Jorn K. Gruber 1, ∗, † , Carlos Bordons 1 , Ruth Bars 2 and Robert Haber 3 1 Department of Automatic Control and Systems Engineering, Escuela Superior de Ingenieros, University of Seville, Camino de los Descubrimientos, s/n, 41092 Seville, Spain 2 Department of Automation and Applied Informatics, MTA-BME Control Research Group, Budapest University of Technology and Economics, H-1111, Budapest, Goldmann Gy. t´ er 3, Budapest, Hungary 3 Laboratory for Process Automation, Institute of Process Engineering and Plant Design, Cologne University of Applied Sciences, Betzdorfer Str. 2, 50679 K¨ oln, Germany SUMMARY There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non-autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real-time control of a chemical pilot plant. Copyright 2009 John Wiley & Sons, Ltd. Received 18 November 2008; Revised 16 September 2009; Accepted 22 October 2009 KEY WORDS: nonlinear predictive control; Volterra models; process control 1. INTRODUCTION Model predictive control (MPC) is arguably the most popular advanced control technique in industry due to the intuitive control problem formulation and its ability to deal with economic objectives and operating ∗ Correspondence to: Jorn K. Gruber, Departamento de Ingenier´ ıa de Sistemas y Autom´ atica, Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos s/n, 41092 Sevilla, Spain. † E-mail: jgruber@cartuja.us.es Contract/grant sponsor: Spanish Ministry of Education; contract/grant number: DPI2007-66718-C04-01 constraints [1]. MPC algorithms determine the control signal minimizing a cost function using the deviation between the reference signal and a predicted system output over a given horizon. To predict the system output, a wide range of different mathematical models can be used [2]. However, nonlinear formulation of MPC (NMPC) has a lot of open issues, and its scarce influence on Contract/grant sponsor: Hungarian Academy of Science, MTA- TKI, Grant for Control Research Contract/grant sponsor: Hungarian National Research Foundation; contract/grant number: OTKA T68370 Contract/grant sponsor: European Commission - Cordis FP6; contract/grant number: ITMPC - 3092 Copyright 2009 John Wiley & Sons, Ltd.