  Extremum seeking (ES) optimization approach has been very popular due to its
non-model based analysis and implementation. This approach has been mostly used
with gradient based search algorithms. Since least squares (LS) algorithms are
typically observed to be superior, in terms of convergence speed and robustness
to measurement noises, over gradient algorithms, it is expected that LS based
ES schemes will also provide faster convergence and robustness to sensor
noises. In this paper, with this motivation, a recursive least squares (RLS)
estimation based ES scheme is designed and analysed for application to scalar
parameter and vector parameter static map and dynamic systems. Asymptotic
convergence to the extremum is established for all the cases. Simulation
studies are provided to validate the performance of proposed scheme.
