  Concurrent learning is a recently developed adaptive update scheme that can
be used to guarantee parameter convergence without requiring persistent
excitation. However, this technique requires knowledge of state derivatives,
which are usually not directly sensed and therefore must be estimated. A novel
integral concurrent learning method is developed in this paper that removes the
need to estimate state derivatives while maintaining parameter convergence
properties. A Monte Carlo simulation illustrates improved robustness to noise
compared to the traditional derivative formulation.
