A receding-horizon regulator for nonlinear systems and a neural approximation
Automatica (Journal of IFAC)
Polynomial Fitting for Edge Detection in Irregularly Sampled Signals and Images
SIAM Journal on Numerical Analysis
Set Membership approximation theory for fast implementation of Model Predictive Control laws
Automatica (Journal of IFAC)
Applied Numerical Mathematics
Automatica (Journal of IFAC)
A shearlet approach to edge analysis and detection
IEEE Transactions on Image Processing
Constrained Control and Estimation: An Optimisation Approach
Constrained Control and Estimation: An Optimisation Approach
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Examples when nonlinear model predictive control is nonrobust
Automatica (Journal of IFAC)
Technical communique: Stabilizing decentralized model predictive control of nonlinear systems
Automatica (Journal of IFAC)
Approximate explicit receding horizon control of constrained nonlinear systems
Automatica (Journal of IFAC)
Hi-index | 22.14 |
In this paper, the use of Set Membership (SM) methods is investigated, in order to derive off-line an approximation of a discontinuous nonlinear model predictive control (NMPC) law. The approximating function can then be evaluated on-line, instead of solving the nonlinear program embedded in the NMPC scheme. This way, a significant decrease of computational times may be obtained, thus allowing the application of NMPC also to systems with ''fast'' dynamics. It is shown that the knowledge of the discontinuities is needed to achieve an approximated controller with arbitrarily small approximation error. By exploiting such a knowledge, SM techniques already developed for the case of continuous NMPC laws are generalized in order to approximate discontinuous ones. The stability of the origin of the closed loop system with the approximated control law is analyzed, and a numerical example is employed to illustrate the features of the proposed approach.