基于参数自学习的柴油机转速主动抗扰控制  

Active Disturbance Rejection Control of Diesel Engine Speed Based on Parameter Self-Learning

作　　者：邵灿 宋康[1] 陈韬[1] 谢辉[1] Shao Can;Song Kang;Chen Tao;Xie Hui(State Key Laboratory of Engines,Tianjin University,Tianjin 300350,China)

机构地区：[1]天津大学内燃机燃烧学国家重点实验室,天津300350

出　　处：《内燃机学报》2022年第2期144-152,共9页Transactions of Csice

基　　金：国家重点研发计划资助项目(2017YFE0102800,SQ2018YFA070035);国家自然科学基金资助项目(51906174).

摘　　要：转速的控制效果对柴油机运行品质有重要影响,但转速易受突变负荷和随机负荷的干扰而发生波动.笔者提出一种基于参数自学习和负荷转矩主动观测的转速抗扰控制算法.首先,构建了由指示转矩、摩擦转矩及负荷转矩构成的控制用曲轴转速动态模型.其次,基于该模型设计了降阶扩张状态观测器,用于快速补偿负荷以提升转速抗扰能力.再次,设计了模型参数的自学习算法,不断改善模型准确性以提升控制品质.硬件在环(HIL)仿真测试结果表明:相比于遗传算法整定的比例-积分-微分(PID)算法,主动抗扰控制算法转速跌幅降至28 r/min,改善60.0%,转速恢复时间约为1.6 s,缩短23.8%.在加入模型参数自学习算法后,转速控制品质提升24.3%.台架试验结果表明:在负荷阶跃测试试验中,相比于遗传算法整定的PID算法,笔者提出的算法转速跌幅减小至18 r/min,相对改善68.9%.转速恢复时间减少至1.4 s,相对改善60.0%.The speed control effect has an important influence on the running quality of diesel engines,but the speed is susceptible to fluctuations due to the interference of sudden load and random load.In this paper,an active disturbance rejection control algorithm based on parameter self-learning and active observation of load torque was proposed.Firstly,a dynamic model of crankshaft speed for control was constructed,which is composed of indicated torque,friction torque,and load torque.Secondly,a reduced-order extended state observer was designed based on the dynamic model,which can quickly compensate the load and improve the anti-disturbance ability of speed.Finally,a self-learning algorithm for model parameters was designed to improve model accuracy,so to enhance control quality.The hardware-in-the-loop(HIL)simulation test results show that,compared with the proportional integral differential(PID)algorithm tuned by genetic algorithm,the active disturbance rejection control algorithm has a speed drop of 28 r/min,an improvement of 60.0%,and a speed recovery time of about 1.6 s,a reduction of 23.8%.After adding the model parameter self-learning algorithm,the quality of speed control has been improved by 24.3%.The bench test results show that,in the load step test,compared with the PID algorithm tuned by genetic algorithm,the speed drop of the algorithm proposed is reduced to 18 r/min,a relative improvement of 68.9%.The speed recovery time is reduced to 1.4s,a relative optimization of 60.0%.

关 键 词：柴油机 负荷转矩观测 主动抗扰 参数自学习 转速控制 

分 类 号：TK421[动力工程及工程热物理—动力机械及工程]