**Introduction to Neural Network Control System**
Neural network control is a cutting-edge field within the broader domain of automatic control, which emerged in the late 1980s. It represents a new branch of intelligent control that has opened up innovative approaches for managing complex nonlinear and uncertain systems. By integrating artificial neural networks with traditional control theory, this discipline has become a powerful tool for addressing challenges in dynamic and unpredictable environments.
This interdisciplinary field draws from various domains, including mathematics, biology, neuroscience, computer science, and artificial intelligence. It combines theoretical foundations, practical techniques, and research outcomes from these areas to develop advanced control strategies. The goal is to create systems that can adapt, learn, and make decisions in real-time, much like the human brain.
In the context of control systems, any system that possesses learning capabilities is classified as a learning control system, which falls under the umbrella of intelligent control. Neural network control, by its very nature, incorporates learning abilities and is therefore considered a subset of learning control. This makes it an essential component of modern intelligent control systems.
Over the past few decades, neural control has evolved significantly, leading to the development of multiple control architectures. Some common structures include neural predictive control, neural inverse system control, and adaptive neural control. These methods leverage the power of neural networks to model complex behaviors, predict system responses, and optimize control actions.
The structural diagram of a neural network control system typically includes several key components: the neural network itself, the plant (the system being controlled), the controller, and the feedback loop. These elements work together to ensure accurate and efficient control performance.
Below are some examples of typical neural network control system structures:
These diagrams illustrate different configurations and implementations of neural network-based control systems, showcasing their versatility and adaptability in various applications. Whether used in industrial automation, robotics, or autonomous vehicles, neural network control continues to evolve and expand its impact across multiple fields.
Shanghai Really Technology Co.,Ltd , https://www.really-led.com