Fault tolerance in the framework of support vector machines based model predictive control

Model based predictive control (MBPC) has been extensively investigated and is widely used in industry. Besides this, interest in non-linear systems has motivated the development of MBPC formulations for non-linear systems. Moreover, the importance of security and reliability in industrial processes is in the origin of the fault tolerant strategies developed in the last two decades. In this paper a MBPC based on support vector machines (SVM) able to cope with faults in the plant itself is presented. The fault tolerant capability is achieved by means of the accurate on-line support vector regression (AOSVR) which is capable of training an SVM in an incremental way. Thanks to AOSVR is possible to train a plant model when a fault is detected and to change the nominal model by the new one, that models the faulty plant. Results obtained under simulation are presented.