Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2013, Article ID 913458, 14 pages http://dx.doi.org/10.1155/2013/913458 Research Article Effective and Robust Generalized Predictive Speed Control of Induction Motor Patxi Alkorta, 1 Oscar Barambones, 2 Asier Zubizarreta, 3 and José Antonio Cortajarena 1,4 1 Department of Automatic Control and Systems Engineering, EUITI de Eibar, University of the Basque Country (UPV/EHU), Otaola Hiribidea 29, 20600 Eibar, Spain 2 Department of Automatic Control and Systems Engineering, EUI de Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain 3 Department of Automatic Control and Systems Engineering, ETSI de Bilbao, University of the Basque Country (UPV/EHU), Alameda Urquijo s/n, 48013 Bilbao, Spain 4 Department of Electronics Technology, EUITI de Eibar, University of Te Basque Country (UPV/EHU), Otaola Hiribidea 29, 20600 Eibar, Spain Correspondence should be addressed to Jos´ e Antonio Cortajarena; josean.cortajarena@ehu.es Received 20 June 2013; Revised 30 September 2013; Accepted 16 October 2013 Academic Editor: Bo-Chao Zheng Copyright © 2013 Patxi Alkorta et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis paper presents and validates a new proposal for efective speed vector control of induction motors based on linear Generalized Predictive Control (GPC) law. Te presented GPC-PI cascade confguration simplifes the design with regard to GPC-GPC cascade confguration, maintaining the advantages of the predictive control algorithm. Te robust stability of the closed loop system is demonstrated by the poles placement method for several typical cases of uncertainties in induction motors. Te controller has been tested using several simulations and experiments and has been compared with Proportional Integral Derivative (PID) and Sliding Mode (SM) control schemes, obtaining outstanding results in speed tracking even in the presence of parameter uncertainties, unknown load disturbance, and measurement noise in the loop signals, suggesting its use in industrial applications. 1. Introduction Te Model Predictive Control (MPC) groups a set of con- trollers which are based on the model of the system and the known future reference for optimal control signal calculation. Te operational principle of predictive control is to calculate in advance the control signal required by the system, when the future input reference that will be applied is known beforehand [1]. In this sense, the system is able to react to the input reference, anticipating its changes and avoiding the efects of delay in system response [2]. Tere are countless applications in industry where the input reference is known beforehand, such as robotic systems, and machine tools. Terefore, in all these systems predictive control algorithms can be implemented. Since Clarke et al. proposed the design principles of Generalized Predictive Control [3, 4], many authors have used this advanced technique for induction motor control in the last two decades. Tere is extensive research related to the application of predictive controllers in electric drives, and, for this reason, predictive algorithms compete with other advanced control techniques such as fuzzy control [5], sliding mode control [6, 7], and nonlinear  inf control [8]. Predictive algorithms are ofen implemented using two or more GPC blocks to control several loops of the electrical machine, and usually they are connected in cascade form [9–11]. Frequently, only one predictive regulator for the control of the main variable of the machine is implemented, such as speed [12–15] or position [16], while the rest of the variables are controlled with classical algorithms, usually PI/PID and hysteresis comparators. Some authors use more complex formulations in order to control several variables of