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Adaptive arrival cost update for improving Moving Horizon Estimation performance.

G Sánchez1, M Murillo1, L Giovanini1

  • 1Research Institute for Signals, Systems and Computational Intelligence, sinc(i), FICH-UNL/CONICET, Ciudad Universitaria UNL, 4° piso FICH, S3000 Santa Fe, Argentina.

ISA Transactions
|March 6, 2017
PubMed
Summary
This summary is machine-generated.

This study enhances moving horizon estimation by using adaptive methods to improve the arrival cost approximation. This leads to reduced optimization problem size while ensuring estimate stability and convergence.

Keywords:
Adaptive estimationArrival costConstrained state estimationMoving Horizon EstimationVariable forgetting

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Area of Science:

  • Control Systems Engineering
  • Optimization Theory
  • Adaptive Estimation

Background:

  • Moving horizon estimation (MHE) is vital for state and parameter estimation in constrained dynamical systems.
  • MHE utilizes finite horizon optimization, naturally handling system constraints.
  • Accurate approximation and updating of the arrival cost remain key research challenges.

Purpose of the Study:

  • To develop an improved method for approximating and updating the arrival cost in MHE.
  • To leverage adaptive estimation techniques for enhanced MHE performance.
  • To reduce computational complexity while maintaining estimation accuracy and stability.

Main Methods:

  • Adaptive estimation techniques were employed to update the arrival cost parameters.
  • The proposed method focuses on refining the arrival cost's role in incorporating prior information.
  • Simulation studies were conducted to validate the approach.

Main Results:

  • A better approximation of the arrival cost was achieved.
  • The size of the optimization problem within MHE was significantly reduced.
  • Stability and convergence of the estimates were mathematically guaranteed.

Conclusions:

  • Adaptive updating of the arrival cost parameters improves MHE efficiency.
  • Enhanced arrival cost approximation is crucial for reducing computational load.
  • The method ensures robust and stable state and parameter estimation for constrained systems.