Levente TAMAS 1 , Ioan NASCU 1 , Robin De Keyser 2 1 Technical University of Cluj-Napoca, Romania, {levente.tamas, ioan.nascu}@aut.utcluj.ro 2 Ghent University, Belgium, rdk@autoctrl.ugent.be Abstract-This paper is intended to give a short overview about the NEPSAC nonlinear predictive control approach. In the first part there are presented the theoretical aspects of the control de- sign procedure, and then in the next section are shown the results for the designed algorithm for a real life experiment. There are also compared the results with other type of controllers like the MPC or PID controller. Keywords- nonlinear predictive control, model based control, parameter estimation, physical modeling. I. INTRODUCTION The main objective of this paper is to present the NEPSAC (Nonlinear Extended Prediction Self-Adaptive Control) control algorithm in a real life application. As the name shows it can deal with nonlinear systems, using predictive control algorithm with a self adaptive ability. The fact that it can be applied to nonlinear systems is only an extension of the linear EPSAC, the fundamental principles of the algorithm are the same in both cases [1]. The predictive part of the algorithm is relying on the fact that it is based on the MBPC principle, i.e. it uses a model for pre- dicting the future response of the system, and based upon this prediction computes the necessary control action for the sys- tem. The self-adaptive capability of the algorithm specifies that even for partial miss modeling or for varying systems it is able to function within a normal range, adapting itself to the changes of the modeled system. In other words this ensures a rather high degree of robustness of the algorithm. A. General description problem The chosen problem may be divided into the following sub- parts: • Choosing the system that will be controlled • Building the necessary IO interface to the system • Making identification experiments for the model • Getting a valid model for the system • Building a controller for the system • Testing and optimizing the controller At the moment of choosing the plant a list of points needs to be analyzed in order to eliminate the possible problems which may emerge at later stages of the design procedure. Such points may be the available measuring devices for the quantities needed to be measured or how many states can be measured and how many are needed to be known; controllability of the plant and the sampling rate of the signals. These are only some of the most fundamental parts of the selection criteria. Based on the mentioned criteria’s there was selected a high perform- ance equipment water tank system which will be described in details in the next section. As the NEPSAC control algorithm is a model based control algorithm it needs a model for the system. The model can be obtained via identification experiments. At this point is already essential to have some basic knowledge about the system, such as the sampling time, the measured variables, and the con- trolled quantities. Based upon this priory knowledge it is needed to be selected the type of the identification experiment: step response, stair case, PRBS or other type of excitation [3]. The ideas upon which were selected the input types is presented in the section describing the identification. After selecting the input type it is needed to be performed the proper identification experiments. Finally, based on the collected data from the identification experiment it can be built a model for the system, which later can be used in the control design procedure. This model build- ing is not a trivial task; it may need also physical modeling knowledge about the system. The model building is treated in details in the part presenting the modeling. The controller design in the case of EPSAC is generally done based on input/output from a state space model. The cur- rently presented solution has extended this principle. B. The novelty of this approach The main novelty of the current solution is that it does not use a state space or transfer function model for the system, in- stead of it, it can be used any ‘black box’ model being able to generate a valid output for the model for a certain excitation at a certain operation point. In other words, it uses only the re- sponse of a model, and does not care about the description form of the model. II. THEORETICAL PRESENTATION A. Theoretical Aspects of the Model Based Predictive Control Basically there is an interest to reduce some cost in the plants. In other word these economical problems can be trans- lated to some cost indices used in the control procedure. The model based predictive control (MBPC) approach’s main ad- vantage is that it can handle easily these costs and constraints. However the MBPC control does not want to eliminate, nor replace the ‘old good working’ PID controllers, rather it offers a chance to reformulate some problems at higher hierarchical The NEPSAC Nonlinear Predictive Controller in a Real Life Experiment 229 1-4244-1148-3/07/$25.00 ©2007 IEEE.