A function estimation approach to sequential learning with neural networks
Neural Computation
Variable neural networks for adaptive control of nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Brief paper: A velocity algorithm for the implementation of gain-scheduled controllers
Automatica (Journal of IFAC)
Dynamic structure neural networks for stable adaptive control of nonlinear systems
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Iterative inversion of neural networks and its application to adaptive control
IEEE Transactions on Neural Networks
Deliveries optimization by exploiting production traceability information
Engineering Applications of Artificial Intelligence
Fault tolerant control of nonlinear systems based on fault diagnosis and switching
CI '07 Proceedings of the Third IASTED International Conference on Computational Intelligence
Fault tolerance in the framework of support vector machines based model predictive control
Engineering Applications of Artificial Intelligence
Adaptive neural model based fault tolerant control for multi-variable process
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Fault diagnosis and accommodation based on online multi-model for nonlinear process
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
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An adaptive neural network model-based fault tolerant control approach for unknown non-linear multi-variable dynamic systems is proposed. A multi-layer Perceptron network is used as the process model and is adapted on-line using the extended Kalman filter to learn changes in process dynamics. In this way, the adaptive model will learn the post-fault dynamics caused by actuator or component faults. Then, the inversion of the neural model is used as a controller to maintain the system stability and control performance after fault occurrence. The convergence of the model inversion control is proved using Lyapunov method. The proposed method is applied to the simulation of a two-input two-output continuous-stirred tank reactor to demonstrate the effectiveness of the approach. Several actuator and component faults are simulated on the continuously stirred tank reactor process when the system is under the proposed fault tolerant control. The results have shown a fast recovery of tracking performance and the maintained stability.