The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Accurate on-line support vector regression
Neural Computation
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Adaptive neural model-based fault tolerant control for multi-variable processes
Engineering Applications of Artificial Intelligence
Brief paper: Application of nonlinear transformations to automatic flight control
Automatica (Journal of IFAC)
Brief paper: A velocity algorithm for the implementation of gain-scheduled controllers
Automatica (Journal of IFAC)
A versatile software tool making best use of sparse data for closed loop process control
Advances in Engineering Software
Expert Systems with Applications: An International Journal
Fuzzy SVM learning control system considering time properties of biped walking samples
Engineering Applications of Artificial Intelligence
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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.