Neural modelling and control of a Diesel engine with pollution constraints
Journal of Intelligent and Robotic Systems
Controlling a complex electromechanical process on the basis of a neurofuzzy approach
Future Generation Computer Systems
Constrained Control of a Class of Uncertain Nonlinear MIMO Systems Using Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Controlling a complex electromechanical process on the basis of a neurofuzzy approach
Future Generation Computer Systems
Nonlinear internal model control based on transformed fuzzy hyperbolic model
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
International Journal of Applied Mathematics and Computer Science
ICCS'03 Proceedings of the 1st international conference on Computational science: PartI
A transductive neuro-fuzzy controller: application to a drilling process
IEEE Transactions on Neural Networks
SVM based internal model control for nonlinear systems
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Dynamics of vertebral column observed by stereovision and recurrent neural network model
ISBMDA'05 Proceedings of the 6th International conference on Biological and Medical Data Analysis
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We propose a design procedure of neural internal model control systems for stable processes with delay. We show that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order of the inverse. The controller is then obtained by cascading this inverse with a rallying model which imposes the regulation dynamic behavior and ensures the robustness of the stability. A change in the desired regulation dynamic behavior, or an improvement of the stability, can be obtained by simply tuning the rallying model, without retraining the whole model reference controller. The robustness properties of internal model control systems being obtained when the inverse is perfect, we detail the precautions which must be taken for the training of the inverse so that it is accurate in the whole space visited during operation with the process. In the same spirit, we make an emphasis on neural models affine in the control input, whose perfect inverse is derived without training. The control of simulated processes illustrates the proposed design procedure and the properties of the neural internal model control system for processes without and with delay