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¹Faculty of Engineering Technology, Mechatronics Engineering Department, Al-Balqa Applied University, Amman 11185, Jordan. randa_herzal@hotmail.com

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Summary

This summary is machine-generated.

This study introduces a novel probabilistic control framework for nonlinear systems, overcoming limitations of traditional methods by directly handling uncertainty and input-dependent noise without needing system dynamics knowledge.

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Area of Science:

Control Theory
Stochastic Systems
Nonlinear Dynamics

Background:

Traditional control design relies on expected values and certainty equivalence, limiting uncertainty handling.
Existing methods often require detailed system dynamics knowledge, which is not always available.
Input-dependent noise presents a significant challenge in stochastic uncertain systems.

Purpose of the Study:

To develop an improved probabilistic framework for control design in stochastic uncertain nonlinear systems.
To provide a method for optimal control law strategy development using a fully probabilistic approach.
To address the challenge of input-dependent noise in control design.

Main Methods:

A fully probabilistic approach for information extraction from process data.
Development of a control framework not constrained by traditional assumptions like certainty equivalence.
Theoretical demonstration on the affine class and validation through a nonlinear simulation example.

Main Results:

An optimal control law strategy is derived using a probabilistic approach.
The framework effectively incorporates and handles system uncertainty.
The proposed method successfully addresses input-dependent noise in discrete-time nonlinear systems.

Conclusions:

The developed probabilistic framework offers a more natural and robust approach to control design for stochastic uncertain nonlinear systems.
This method bypasses the need for detailed system dynamics and simplifies computation.
The framework is applicable to general nonlinear discrete-time systems and validated by simulation.