A neural approach for control of nonlinear systems with feedback linearization
1Lehrstuhl für Allgemeine und Theoretische Elektrotechnik, Universität Erlangen-Nürnberg, 91058 Erlangen, Germany.
IEEE Transactions on Neural Networks
|February 8, 2008
Summary
This study explores feedback linearization using neural networks, introducing a neurocontroller design. It covers full, partial, and approximate linearization methods with practical examples for advanced control systems.
Area of Science:
- Control Systems Engineering
- Artificial Intelligence
- Nonlinear Dynamics
Background:
- Feedback linearization is a powerful technique for controlling nonlinear systems.
- Neural networks offer advanced capabilities for complex system modeling and control.
- Existing methods may face limitations with systems not satisfying specific conditions.
Purpose of the Study:
- To investigate and compare various feedback linearization schemes employing neural networks.
- To introduce a novel approach for designing a neurocontroller based on feedback linearization principles.
- To address different system complexities, including full, partial, and approximate linearization scenarios.
Main Methods:
- Comparative analysis of different neural network-based feedback linearization strategies.
- Development of a neurocontroller design methodology.
- Implementation of algorithms for full, partial, and approximate linearization.
- Utilizing case studies and programming examples for validation.
Main Results:
- Demonstration of effective feedback linearization using neural networks across various system types.
- Successful design and illustration of a neurocontroller.
- Validation of the proposed methodologies through practical examples and programs.
- Highlighting the adaptability of neural networks in achieving linearization.
Conclusions:
- Neural network-based feedback linearization provides a versatile framework for nonlinear control.
- The proposed neurocontroller design is effective for systems with varying relative degrees and involutivity conditions.
- The presented methodology offers practical solutions for complex control problems in engineering.
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01:26Linear Approximation in Frequency Domain
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01:21Linear Approximation in Time Domain
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01:24Effects of feedback
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
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01:26Classification of Systems-I
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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01:26Linearization and Approximation
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