基于DCQGA的PID参数自整定
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摘要
量子遗传算法相对于普通遗传算法具有更好的多目标寻优能力,在PID参数优化中具有一定优势,但是标准量子遗传算法容易陷入局部极值,并且在进化后期容易出现早熟收敛的现象,提出了一种新的方法——双链量子遗传算法(DCQGA)来整定PID控制器的参数。该方法直接用实数来编码基因位,用量子旋转门和量子非门进行染色体的更新和变异,用最优解和当前解量子位概率幅组成的行列式来确定量子门旋转方向,用结合目标函数梯度信息的自适应策略调整旋转角大小。同时采用误差绝对值时间积分性能指标作为PID参数选择的最小目标函数进行Matlab仿真,通过与普通遗传算法和标准量子遗传算法整定效果的对比,结果验证了DCQGA具有更快的响应速度和更好的多目标寻优能力。
Quantum genetic algorithm has better multi-objective optimization ability than general genetic algorithm.It has some advantages in PID parameters optimization.However,standard quantum genetic algorithm is easy to fall into local extremum and prone to premature convergence in late evolutionary stage.A new method of double chain quantum genetic algorithm(DCQGA)is proposed to tune parameters of the PID controller.Real number is directly used to encode genes position in the method.The updating and aberrance of chromosomes are realized with quantum rotary gates and negater.The rotation direction of the quantum gates is determined by the determinant of the probability of the optimal solution and the current solution qubit.The size of the rotation angle is adjusted by the adaptive strategy combined with the gradient information of the objective function.At the same time,performance index of the error absolute value time integral is used as the minimum objective function of PID parameter selection to carry on the Matlab simulation.Through comparison with the general genetic algorithm and standard quantum genetic algorithm,the result verifies DCQGA has faster response speed and better multi-objective optimization ability.
Quantum genetic algorithm has better multi-objective optimization ability than general genetic algorithm.It has some advantages in PID parameters optimization.However,standard quantum genetic algorithm is easy to fall into local extremum and prone to premature convergence in late evolutionary stage.A new method of double chain quantum genetic algorithm(DCQGA)is proposed to tune parameters of the PID controller.Real number is directly used to encode genes position in the method.The updating and aberrance of chromosomes are realized with quantum rotary gates and negater.The rotation direction of the quantum gates is determined by the determinant of the probability of the optimal solution and the current solution qubit.The size of the rotation angle is adjusted by the adaptive strategy combined with the gradient information of the objective function.At the same time,performance index of the error absolute value time integral is used as the minimum objective function of PID parameter selection to carry on the Matlab simulation.Through comparison with the general genetic algorithm and standard quantum genetic algorithm,the result verifies DCQGA has faster response speed and better multi-objective optimization ability.
引文
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[14]郁磊,史峰.Matlab智能算法30个案例分析[M].北京:北京航空航天大学出版社,2015.
[15]曾成,赵锡均.改进量子遗传算法在PID参数整定中的应用[J].电力自动化设备,2009(10):125-127,139.
[16]李士勇,李盼池.量子计算与量子优化算法[M].哈尔滨:哈尔滨工业大学出版社,2009.
[17]Zhang G X,Li N,Jin W D.A Novel Quantum Genetic Algorithm and Its Application[J].ACTA Electronic Sinica,2004,32(03):476-479.
[18]刘金琨.先进PID控制Matlab仿真[M].北京:电子工业出版社,2011:25-26.
[2]刘屿,田联房,毛宗源.一种新型人工免疫算法的PID自整定研究[J].计算机应用研究,2007,24(04):84-87.
[3]陈书谦,张丽虹.蚁群算法在PID控制器参数优化中的应用研究[J].计算机应用研究,2011,28(01):177-181.
[4]余丽莹,焦嵩鸣.基于鱼群算法的PID优化[J].计算机仿真,2014,31(03):155-158.
[5]彭勇,施宁,林浒.佳点遗传算法集及其在PID控制中的应用[J].计算机应用研究,2009,26(02):524-526.
[6]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999
[7]Narayanan A,Moore M.Quantum-inspired Genetic Algorithms[C]//Proceedings of IEEE International Conference on Evolutionary Computation.Nagoya,Japan,1996:61-66.
[8]Han Kuk-Hyun,Kim Jong-Hwan.Quantum-inspired Evolutionary Algorithm for a Class of Combinatorial Optimization[J].IEEE Trans Evolutionary Computation,2002,6(06):580-593.
[9]Zhang G X,Gu Y J,Hu L,et al.A Novel Genetic Algorithm and Its Application to Digital Filter Design[C]//Proc on IEEE Intelligent Transportation System.New Jersey:IEEE Press,2003,2:1600-1605.
[10]商允伟,裘聿皇.适应值共享对遗传算法选择概率的影响分析[J].控制与决策,2003,18(06):708-711.
[11]Li P C,Li S Y.Quantum-inspired Evolutionary Algorithm for Continuous Spaces Optimization[J].Chinese Journal of Electronics,2008,17(01):100-104.
[12]李士勇,李盼池.基于实数编码和目标函数梯度的量子遗传算法[J].哈尔滨工业大学学报,2006(08):1216-1218.
[13]张兴华,朱筱蓉,林锦国.基于量子遗传算法的PID控制器参数自整定[J].计算机工程与应用,2007(21):218-220.
[14]郁磊,史峰.Matlab智能算法30个案例分析[M].北京:北京航空航天大学出版社,2015.
[15]曾成,赵锡均.改进量子遗传算法在PID参数整定中的应用[J].电力自动化设备,2009(10):125-127,139.
[16]李士勇,李盼池.量子计算与量子优化算法[M].哈尔滨:哈尔滨工业大学出版社,2009.
[17]Zhang G X,Li N,Jin W D.A Novel Quantum Genetic Algorithm and Its Application[J].ACTA Electronic Sinica,2004,32(03):476-479.
[18]刘金琨.先进PID控制Matlab仿真[M].北京:电子工业出版社,2011:25-26.