With the rapid development of Internet of Things technology, intelligent perception and control have been widely used in many fields. However, the existing intelligent perception and control technology still has problems such as high energy consumption, slow response and poor adaptability when dealing with dynamic changes and complex environments. In order to solve these problems, this paper introduces the fusion technology of artificial intelligence and Internet of Things, and proposes an adaptive intelligent control algorithm based on edge computing. The algorithm automatically adjusts the control strategy by real-time sensing the load changes, bandwidth conditions and device collaboration requirements of IoT devices to optimize the energy efficiency and performance of the system. Experimental results show that compared with traditional static power management and model predictive control (MPC)-based methods, this method shows significant advantages in stable load, burst load, bandwidth limitation and multi-device collaboration scenarios. Specifically, this method has achieved significant improvements in energy consumption reduction rate, service performance retention rate, adaptation speed and response time. In the multi-device collaboration scenario, the service performance retention rate of the proposed algorithm is 89.60%, which is 11.10% and 8.40% higher than static power management (78.50%) and MPC (Model Predictive Control) (81.20%), respectively. These results demonstrate the great potential of the integration of artificial intelligence and the Internet of Things in improving the detailed control and overall efficiency of intelligent control systems.