Research on multimodel intelligent control technology and its application

Multimodel intelligent control technology provides an effective solution for managing complex systems. Traditional single-model control methods often struggle to achieve satisfactory performance when dealing with systems charactered by multimodal behaviors, strong nonlinearity, and uncertainty. In contrast, multimodel intelligent control describes different system states or behaviors by constructing a model set composed of multiple models, each representing a specific operating condition or mode. A dedicated controller is designed for each model, forming a corresponding controller set. A switching criterion based on the identification error between each model and the actual plant is designed. When the parameters of the controlled system change, the system switches to the model that best matches the current conditions and activates the corresponding controller. This approach significantly enhances adaptability and robustness, making it well-suited for complex, uncertain, stochastic, and nonlinear systems. Furthermore, with the continuous advancement of artificial intelligence, neural networks, and other emerging technologies, the application scope of multimodel intelligent control is expanding, which promotes the development of multimodel interaction fusion and multichannel applications.

In the area of model set optimization, various optimization strategies, such as genetic algorithms, particle swarm optimization, and other intelligent algorithms, have been proposed to enhance the accuracy and generalization capability of the model. This paper explores key challenges related to switch timing, target model selection, switching accuracy, and switching speed. Several indicator function designs are introduced to optimize the switch performance between models. With the integration of neural networks, fuzzy logic and other advanced technologies, the strategies underlying multimodel intelligent control have evolved considerably. The basic principle, design method, and stability of multimodel intelligent control have been extensively studied, and a variety of control strategies have emerged, such as neural network–based multimodel adaptive control and fuzzy logic–based multimodel control. From a practical standpoint, multimodel intelligent control has found wide application in industrial automation, intelligent manufacturing, and intelligent healthcare. These applications have demonstrated significant improvements in automation levels, production efficiency, and intelligent system optimization. In healthcare, for example, it has supported the automatic control of medical devices and the intelligent analysis of medical data.

Despite its advantages, multimodel intelligent control technology still faces several challenges. These include improving the accuracy and generalization ability of the model, optimizing the algorithm of the switching mechanism for greater efficiency, and integrating the approach with other advanced technologies to broaden its application range. Addressing these challenges requires further in-depth research and development. In the future, the continued advancement of artificial intelligence and machine learning is expected to drive multimodel intelligent control technology toward more intelligent, efficient, and precise control outcomes. Further research is likely to explore new application domains—such as speech, text, image, and video processing—within multimodal and cross-modal contexts.