An integrated system optimization and parameter estimation technique for hierarchical control of steady-state systems

This paper presents computer simulation results of an optimal adaptive algorithm (Brdy? and Roberts 1984) and develops a double iterative alternative. Both algorithms are optimal in the sense that Kuhn-Tucker necessary optimality conditions are satisfied. The aim of the latter is to reduce the number of times that information is required from the real system. Simulation results also show that it does not increase the total number of information exchanges during the iteration procedure and may even reduce it.