Abstract: Abstract: In order to facilitate DO (dissolved oxygen) monitoring for a scaled aquaculture pond, a DO intelligent monitoring system was developed based on a wireless sensor network, which could realize distribution measurement, intelligent control, and centralized management. The system consists of a three-layer structure including data acquisition and control, water quality monitoring, and water management. The data acquisition and control layer was composed of data acquisition and control terminals, routing nodes, and a coordinator node based on ZigBee technology. They were deployed in the sensing area for an aquaculture pond's waters, and they constituted a wireless monitoring network for water quality environmental parameters by self organization to collect water quality parameters and adjust control devices. The water quality monitoring layer included a water quality monitoring terminal and a communication computer, which realized water quality supervision and aquaculture equipment intelligent control. The water quality management layer contained mainly a water quality management terminal, a system database, and a Web server end. The water quality management end was responsible for analysis and processing for the water quality data. The monitoring system concentrated wireless data acquisition, intelligent control, and centralized management for water quality parameters to improve scale aquaculture benefit and information management level. Aiming at low adaptive ability for conventional fuzzy PID controller, a variable universe fuzzy PID controller was proposed, which comprises an adjustment unit for expansion factors, variable universe fuzzy control unit and PID controller, the extension factor α1, α2 and β for input and output domain of fuzzy control unit were adjusted constantly by an expansion factor regulating unit according to DO error and DO error change rate. PID controller parameters were tuned online by a variable universe fuzzy control unit to realize the purpose for DO adaptive control. According to the DO change characteristics with nonlinear, large delay and large inertia in a pond, the cascade control system was constituted by a variable universe fuzzy PID controller and an aerator speed PID controller. DO was the main controlled variable, and the aerator speed was the secondary controlled variable. If the DO concentration deviates from the setting value, a DO control loop will operate. Its output is the control loop input of the aerator speed, and the output of the aerator regulator changes the aerator speed to make DO concentration fast track the setting value of system target. The cascade control system can timely and accurately adjust the DO concentration to meet aquacultural needs. According to the changing trend of the DO data sequence for multiple pond monitoring sites, a combination grey DO forecasting model was constructed based on grey theory and weights to predict DO concentration and the feedback value for a variable universe fuzzy PID controller. This achieved DO prediction control and beforehand adjustment, and DO overshooting was well restrained. The test pond and the contrast pond respectively used a variable universe fuzzy PID controller and a fuzzy PID controller to regulate DO in the ponds, and the DO target value was 7.10 mg/L. In the dynamic response phase, DO response time for the contrast pond extended about 15 min more than the test pond, and the overshooting expanded 2.96 times. After the system entered steady process, the standard deviation, the mean variance, and the maximum error and minimum error enlarged by 3-4 times. The testing results showed that the adjustment process for the test pond had characteristics with fast response, small overshooting, high control precision, and good stability compared with the contrast pond. The adjusting factors for the variable universe fuzzy PID controller better solved contradictions between the quantity of fuzzy control rules and DO control precision, and realized self tuning for PID parameters, and improved dynamic performance, and raised steady accuracy and quality of the fuzzy PID controller. It can effectively restrain many uncertain factors of interference affecting DO stability to meet aquaculture requirements for DO and provide a new control method to solve control problem for complex objects with a nonlinear and large time delay.