针对暖通空调(HVAC)系统难以控制的问题,提出一种基于m ax-product推理的M am dan i模糊模型预测控制策略.首先利用一步模糊预测模型的结构分析得到其解析表达式,获得系统在k+1时刻的线性化预测模型;然后基于模糊线性化模型进行模型预...针对暖通空调(HVAC)系统难以控制的问题,提出一种基于m ax-product推理的M am dan i模糊模型预测控制策略.首先利用一步模糊预测模型的结构分析得到其解析表达式,获得系统在k+1时刻的线性化预测模型;然后基于模糊线性化模型进行模型预测控制器设计.对HVAC系统的仿真和实验结果表明,该算法是一种跟踪性能好且鲁棒性强的有效控制算法.展开更多
Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environ...Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.展开更多
文摘针对暖通空调(HVAC)系统难以控制的问题,提出一种基于m ax-product推理的M am dan i模糊模型预测控制策略.首先利用一步模糊预测模型的结构分析得到其解析表达式,获得系统在k+1时刻的线性化预测模型;然后基于模糊线性化模型进行模型预测控制器设计.对HVAC系统的仿真和实验结果表明,该算法是一种跟踪性能好且鲁棒性强的有效控制算法.
基金by the National Key R&D Plan Project(Grant No.2016YFD070030201)。
文摘Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.
