Parallel numerical optimization for fast adaptive nonlinear moving horizon estimation

This paper proposes a novel strategy using parallel optimization computations for nonlinear moving horizon state estimation, and parameter identification problems of dynamic systems. The parallelization is based on the multi-point derivative-based Gauss-Newton search, as one of the most efficient algorithms for the nonlinear least-square problems. A numerical experiment is performed to demonstrate the parallel computations with the comparison to sequential computations.