Adaptive control for manufacturing systems using infinitesimal perturbation analysis

Combining infinitesimal perturbation analysis (IPA) with stochastic approxi mation gives identification algorithms to estimate the optimal threshold va lue for failure-prone manufacturing systems consisting of one machine produ cing one part type. Two adaptive control schemes are proposed. The adaptive control schemes do not require the knowledge of the distribution functions of the up and down times. Under some appropriate conditions, the strong co nsistency, as well as the convergence rates, of the identification algorith ms and the cost function is established for the adaptive control schemes. I n particular, it is shown that central limit theorems hold for the identifi cation algorithms.