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Updated: May 13, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Probabilistic DHP adaptive critic for nonlinear stochastic control systems.

Randa Herzallah1

  • 1Faculty of Engineering Technology, Al-Balqa Applied University, Jordan. herzallah.r@gmail.com

Neural Networks : the Official Journal of the International Neural Network Society
|March 19, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic control design for nonlinear stochastic systems, extending prior work. The randomized control algorithm aims to match system behavior to an ideal probability distribution, showing promising simulation results.

Related Experiment Videos

Last Updated: May 13, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

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Published on: October 28, 2022

Area of Science:

  • Control Theory
  • Stochastic Systems
  • Nonlinear Dynamics

Background:

  • Builds upon existing algorithms for fully probabilistic control design in general dynamic stochastic systems.
  • Addresses limitations in previous work by extending to more general nonlinear stochastic discrete-time systems.

Purpose of the Study:

  • To present solutions for probabilistic dual heuristic programming (DHP) adaptive critic methods and randomized control algorithms.
  • To design randomized control inputs that align the closed-loop system's probability density function with a desired ideal.
  • To formulate and solve the fully probabilistic control design problem for nonlinear stochastic discrete-time systems.

Main Methods:

  • Utilizes recently developed algorithms for fully probabilistic control design.
  • Applies probabilistic dual heuristic programming (DHP) adaptive critic methods.
  • Employs a randomized control algorithm for stochastic nonlinear dynamical systems.

Main Results:

  • Successfully formulates and solves the fully probabilistic control design problem for nonlinear stochastic discrete-time systems.
  • Demonstrates the algorithm's application through a simulated example.
  • Obtained encouraging results validating the proposed approach.

Conclusions:

  • The developed probabilistic control design is effective for nonlinear stochastic discrete-time systems.
  • The randomized control algorithm successfully approximates the ideal probability density function.
  • This work advances the field of probabilistic control design for complex dynamical systems.