in this paper, we present new RHNHC (Receding Horizon Neural H ∞ Control) for nonlinear unknown systems. First, we propose LMI (Linear Matrix Inequality) condition on the terminal weighting matrix for stabilizing RHNHC. Under this condition, noninceasing monotonicity of the saddle point value of the finite horizon dynamic game is shown to be guaranteed. Then, we propose RHNHC for nonlinear unknown systems which guarantees the infinite horizon H∞ norm bound and the internal stability of the closed-loop systems. Since RHNHC can deal with input and state constraints in optimization problem effectively, it does not cause an instability problem or give a poor performance in contrast to the existing neural H∞ control schemes.
|Title of host publication
|Proceedings of the 16th IFAC World Congress, IFAC 2005
|Number of pages
|Published - 2005
|IFAC Proceedings Volumes (IFAC-PapersOnline)
Bibliographical noteFunding Information:
1 This work was supported by the SNU BK21-IT Program.
- Neural networks
- Nonlinear systems
- Receding horizon control
- Unknown systems
ASJC Scopus subject areas
- Control and Systems Engineering