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A perceptron network for functional identification and control of nonlinear systems
1George W. Woodruff Sch. of Mech. Eng., Georgia Inst. of Technol., Atlanta, GA.
IEEE Transactions on Neural Networks
|January 1, 1993
Summary
This study introduces a perceptron neural network (PNN) for tracking control in nonlinear systems. The novel approach enables direct online estimation of control inputs without assuming feedback linearizability, ensuring stability.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Nonlinear Dynamics
Background:
- Traditional control methods struggle with complex nonlinear systems.
- Existing adaptive control schemes often require plants to be feedback linearizable.
- Online estimation of control inputs presents significant challenges.
Purpose of the Study:
- To develop a novel perceptron neural network (PNN) based tracking control strategy for general nonlinear systems.
- To enable direct online estimation of feedforward control inputs.
- To avoid assumptions of feedback linearizability for the controlled plant.
Main Methods:
- Derivation of the basic structure and training law for the PNN.
- Introduction of a discrete-time control strategy utilizing the PNN for direct feedforward control estimation.
- Rigorous stability analysis under ideal and inexact modeling conditions.
Main Results:
- A PNN-based controller capable of tracking control for a general class of nonlinear systems.
- The controller effectively performs direct online estimation of the feedforward control input.
- Demonstrated stability of the neural controller, including robust stability against modeling inaccuracies.
Conclusions:
- The proposed PNN control strategy offers a viable approach for nonlinear system tracking.
- The method's independence from feedback linearizability broadens its applicability.
- The controller exhibits robust stability, making it suitable for real-world applications with imperfect models.
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