基于变论域模糊控制的Taylor逼近型内模PID算法

Taylor Approximation Type Internal Model PID Algorithm based on Variable Universe Fuzzy Control

针对传统的PID算法和Smith预估器控制温度对象时,存在控制精度低、自适应能力差、对模型误差极为敏感、严重影响控制品质等问题。利用泰勒逼近,将内模控制与PID算法相结合,在减少调节参数的同时,有效降低模型失配对系统的影响,并引入变论域模糊控制,不仅能实现参数在线自整定,还能提高系统自适应能力和控制精度。Matlab仿真表明,当模型出现误差时,与传统算法相比,文中提出的算法超调量最低、稳定性最好、综合性能指标最高。

When the traditional PID algorithm and Smith predictor control the temperature object,there are problems such as low control accuracy,poor adaptive ability,and extreme sensitivity to model errors,which seriously affect the control quality.By using the Taylor approximation method,the internal model control is combined with the PID algorithm,which not only reducing the adjustment parameters but also effectively reduces the influence of the model mismatching system.The variable universe fuzzy control is introduced,which can not only realize the parameter online self-tuning but also improve the adaptability and control accuracy of the system.Matlab simulation shows that when the model has errors,compared with the traditional algorithm,the algorithm in this paper has the lowest overshoot,the best stability,and the highest comprehensive performance index.