**Introduction to Neural Network Control System**
Neural network control is a cutting-edge discipline in the field of automatic control that emerged in the late 1980s. It represents a new branch of intelligent control, offering innovative solutions for managing complex nonlinear, uncertain, and dynamic systems. By integrating artificial intelligence with traditional control theory, it has become a powerful tool for addressing challenges that conventional control methods struggle to handle.
This interdisciplinary field draws from various domains such as mathematics, biology, neuroscience, computer science, and artificial intelligence. It combines theoretical foundations with practical techniques, making it a rapidly evolving area of research and application. The goal of neural network control is to enable systems to learn, adapt, and make decisions autonomously, which is essential for modern automation and intelligent systems.
In the context of control systems, a system that can learn from experience is referred to as a learning control system, and neural control falls under this category. As a subset of intelligent control, neural control leverages the adaptive capabilities of artificial neural networks to improve performance and robustness in real-world applications.
Over the past few decades, numerous neural network-based control structures have been developed. Examples include neural predictive control, neural inverse control, and hybrid neural-fuzzy control systems. These approaches are designed to handle uncertainty, nonlinearity, and time-varying dynamics, making them suitable for a wide range of industrial and scientific applications.
The structure of a neural network control system typically involves multiple components, including the controller, the plant, the neural network model, and feedback mechanisms. Below are some common structural diagrams that illustrate different configurations of neural network control systems:
These diagrams provide a visual representation of how neural networks can be integrated into control systems to enhance their performance, adaptability, and efficiency. Whether used in robotics, process control, or autonomous vehicles, neural network control continues to shape the future of intelligent automation.
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