Recursive least squares for real-time implementation [lecture notes]
SAU Islam, DS Bernstein - IEEE Control Systems Magazine, 2019 - ieeexplore.ieee.org
IEEE Control Systems Magazine, 2019•ieeexplore.ieee.org
Recursive least squares (RLS) is a technique used for minimizing a quadratic cost function,
where the minimizer is updated at each step as new data become available. RLS is more
computationally efficient than batch least squares, and it is extensively used for system
identification and adaptive control. This article derives RLS and emphasizes its real-time
implementation in terms of the availability of the data as well as the time needed for the
computation.
where the minimizer is updated at each step as new data become available. RLS is more
computationally efficient than batch least squares, and it is extensively used for system
identification and adaptive control. This article derives RLS and emphasizes its real-time
implementation in terms of the availability of the data as well as the time needed for the
computation.
Recursive least squares (RLS) is a technique used for minimizing a quadratic cost function, where the minimizer is updated at each step as new data become available. RLS is more computationally efficient than batch least squares, and it is extensively used for system identification and adaptive control. This article derives RLS and emphasizes its real-time implementation in terms of the availability of the data as well as the time needed for the computation.
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