Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Nonlinear predictive control based on multivariable neural wiener models
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
| Hi-index | 0.00 |
This paper describes a nonlinear Model Predictive Control (MPC) algorithm based on a neural Wiener model. The model is linearised on-line along the predicted trajectory. Thanks to linearisation, the algorithm is computationally efficient since the control policy is calculated on-line from a series of quadratic programming problems. For a nonlinear system for which the linear MPC approach is inefficient and the MPC algorithm with approximate linearisation is inaccurate, it is demonstrated that the described algorithm gives control quality practically the same as the MPC approach with on-line nonlinear optimisation.