Nonlinear model-based control using second-order Volterra models
Automatica (Journal of IFAC)
Optimization of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants
Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point Optimizers
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
International Journal of Applied Mathematics and Computer Science
Efficient model predictive control algorithm with fuzzy approximations of nonlinear models
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Fuzzy Modeling and Control
Supervisory predictive control and on-line set-point optimization
International Journal of Applied Mathematics and Computer Science
Hi-index | 0.00 |
The idea proposed in the paper consists in significant simplification of the control structure with a predictive control algorithm and a steady-state target optimization. It is done by application of only one fuzzy (nonlinear) dynamic control plant model for both: predictive control and set-point calculation. The approach exploits possibilities offered by a fuzzy model used by the predictive control algorithm. The fuzzy model is of Takagi-Sugeno type with step responses used as the local models. Such a model can be obtained relatively easy and well tuned using neural networks. The proposed approach, despite simplification of the control system, offers very good control performance. It is demonstrated using an example of a control system of a nonlinear chemical reactor with inverse response.