A bioreactor benchmark for adaptive network-based process control
Neural networks for control
A challenging set of control problems
Neural networks for control
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Optimal control by least squares support vector machines
Neural Networks
Chattering-Free LS-SVM Sliding Mode Control
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
A fuzzy clustering method of construction of ontology-based user profiles
Advances in Engineering Software
Support vector machine based aerodynamic analysis of cable stayed bridges
Advances in Engineering Software
Output feedback sliding mode control with support vector machine based observer gain adaptation
CSECS'09 Proceedings of the 8th WSEAS International Conference on Circuits, systems, electronics, control & signal processing
Advances in Engineering Software
Fault tolerance in the framework of support vector machines based model predictive control
Engineering Applications of Artificial Intelligence
Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks
IEEE Transactions on Neural Networks
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This paper presents the design of a software supported sliding mode controller for a biochemical process. The state of the process is characterized by cell mass and nutrient amount. The controller is designed for tracking of a desired profile in cell mass and it is shown that the nutrient amount in the controlled bioreactor evolves bounded. A smart software tool named Support Vector Machine (SVM), which minimizes the upper bound of an empirical risk function, is proposed to approximate the nonlinear function seen in the control law by using very limited number of numerical data. This removes the necessity of knowing the functional form of the nominal nonlinearity in the control law. It is shown that the controller is robust against noisy measurements, considerable amount of parameter variations, discontinuities in the command signal and large initial errors. The contribution of the present work is the achievement of robustness and tracking performance on a benchmarking process, under the presence of limited prior knowledge.