ERROR MODELS WITH PARAMETER CONSTRAINTS

This work treats the analysis of two adaptive systems described by err or models. The desired but unknown parameters of each adaptive system are, however, not independent. In general, only linear constraints upo n these parameters are considered, although a constant but unknown sca lar that introduces some nonlinearities is acceptable within the given constraint. The necessity of this analysis frequently arises, in the areas of both adaptive control and parameter estimation. It is shown t hat, if the relationship between ideal parameters is linear, it is the n possible to find coupled adaptive laws such that the overall adaptiv e system is globally stable for each type of known error model. Simula tions show that the parameter estimation is generally much closer usin g coupled adaptive laws than those not incorporating the information c ontained within the constraint.