In this paper, adaptive neural-network predictive control strategies for general nonlinear systems are presented. The system is described by an unknown NARMAX model and neuro model is used to on-line learn the system. Despite state/parameter estimation, the neural predictive control scheme associated with the constrained optimization framework is implemented in a straightforward manner. Through the Lyapunov stability analysis, the network weight adaptation rule is derived, and guarantees the minimum error between the neuro output and plant output. An unstable reactor system is given to demonstrate the effectiveness of the proposed control schemes.
|Number of pages||6|
|Journal||IFAC Proceedings Volumes (IFAC-PapersOnline)|
|Publication status||Published - 2004 Jan 1|
|Event||7th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2004 - Cambridge, United States|
Duration: 2004 Jul 5 → 2004 Jul 7
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering