Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form
IEEE Transactions on Neural Networks and Learning Systems
|March 31, 2015
Summary
This study introduces a new adaptive neural network (NN) control method for nonlinear systems. The approach ensures system stability using radial basis function (RBF) NNs and output feedback.
Area of Science:
- Control Theory
- Nonlinear Systems
- Artificial Intelligence
Background:
- Adaptive neural network (NN) control is crucial for complex nonlinear systems.
- Nonstrict-feedback systems present unique challenges in control design.
- Output feedback control is often preferred due to limited sensor availability.
Purpose of the Study:
- To develop a novel adaptive NN backstepping output-feedback control strategy.
- To address control problems in nonlinear nonstrict-feedback systems.
- To ensure semiglobal boundedness of closed-loop systems.
Main Methods:
- A novel adaptive NN backstepping output-feedback control approach is proposed.
- Radial basis function (RBF) neural networks are utilized for their approximation capabilities.
- A state observer is constructed to estimate unmeasurable states.
Main Results:
- The proposed controller effectively handles nonlinear nonstrict-feedback systems.
- The controller guarantees semiglobal boundedness of all signals in the closed-loop system.
- Two illustrative examples demonstrate the approach's efficacy.
Conclusions:
- The developed adaptive NN output-feedback controller is effective for nonlinear nonstrict-feedback systems.
- The integration of RBF NNs and backstepping overcomes system structure limitations.
- The controller ensures robust performance and stability.
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