Convergence properties of a self-tuning regulator incorporating an input mean-square constraint are studied. An algorithm, derived from the long-range controller MUSMAR, is considered. For this algorithm, using the ODE method for analysing stochastic recursive algorithms and singularly perturbed ODE theory, a local convergence result is proved. This result characterizes possible convergence points of the algorithm as the constrained minima of the underlying steady-state quadratic cost. The actual convergence of the algorithm to the possible equilibrium points predicted by theory is verified by means of simulation examples including unmodelled plant dynamics.
Lemos, J.M., Mendonca, T.F., Mosca, E., & Nistri, P. (1992). Adaptive predictive control with mean-square input constraint. AUTOMATICA, 28(3), 593-597.
|Titolo:||Adaptive predictive control with mean-square input constraint|
|Citazione:||Lemos, J.M., Mendonca, T.F., Mosca, E., & Nistri, P. (1992). Adaptive predictive control with mean-square input constraint. AUTOMATICA, 28(3), 593-597.|
|Appare nelle tipologie:||1.1 Articolo in rivista|