The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is...The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.展开更多
大容积环境测试舱内温湿度控制系统具有非线性、时变性和耦合性的特点,传统的比例积分微分(Proportion integral differential,PID)控制器参数整定方法不能满足环境测试舱温湿度控制的要求。只有获得PID控制器的最优参数才能实现环境测...大容积环境测试舱内温湿度控制系统具有非线性、时变性和耦合性的特点,传统的比例积分微分(Proportion integral differential,PID)控制器参数整定方法不能满足环境测试舱温湿度控制的要求。只有获得PID控制器的最优参数才能实现环境测试舱温湿度的优化控制。该文提出一种遗传算法(Genetic algorithm,GA)优化PID控制器参数的控制算法—GA-PID。首先通过预估解耦方法对温湿度解耦,然后将目标函数作为控制器的评估值,通过遗传算法的选择、交叉、变异、迭代功能获得PID控制器参数的最优解,以弥补常规PID算法在环境测试舱温湿度控制系统中的不足。通过MATLAB进行了仿真实验,实验结果表明预估解耦可有效地对温湿度进行解耦,提出的GA-PID控制算法可实现快速、准确以及稳定的环境测试舱温湿度控制,具有更好的控制性能。展开更多
针对压电柔性机械臂运行过程中的弹性振动问题,提出了基于粒子群优化算法(particle swarm optimization,简称PSO)自整定比例积分微分(proportional integral differential,简称PID)控制器参数的柔性臂振动抑制方法。采用标准粒子群优化...针对压电柔性机械臂运行过程中的弹性振动问题,提出了基于粒子群优化算法(particle swarm optimization,简称PSO)自整定比例积分微分(proportional integral differential,简称PID)控制器参数的柔性臂振动抑制方法。采用标准粒子群优化算法,以时间乘绝对误差积(integrated time and absolute error,简称ITAE)准则为适应度函数,整定PID控制器的3个控制参数Kp,Ki和Kd,并采用Matlab Simulink平台建立双连杆压电柔性机械臂振动控制仿真模型,研制基于虚拟仪器技术的柔性臂振动控制试验系统。仿真与试验结果表明,采用常规PID控制算法和基于PSO自整定的PID控制算法均能有效地抑制柔性机械臂的弹性振动,但后者的振动抑制效果、鲁棒性与稳定性优于前者。展开更多
基金supported by the National Natural Science Foundation of China(6137415361473138)+2 种基金Natural Science Foundation of Jiangsu Province(BK20151130)Six Talent Peaks Project in Jiangsu Province(2015-DZXX-011)China Scholarship Council Fund(201606845005)
文摘The quantum bacterial foraging optimization(QBFO)algorithm has the characteristics of strong robustness and global searching ability. In the classical QBFO algorithm, the rotation angle updated by the rotation gate is discrete and constant,which cannot affect the situation of the solution space and limit the diversity of bacterial population. In this paper, an improved QBFO(IQBFO) algorithm is proposed, which can adaptively make the quantum rotation angle continuously updated and enhance the global search ability. In the initialization process, the modified probability of the optimal rotation angle is introduced to avoid the existence of invariant solutions. The modified operator of probability amplitude is adopted to further increase the population diversity.The tests based on benchmark functions verify the effectiveness of the proposed algorithm. Moreover, compared with the integerorder PID controller, the fractional-order proportion integration differentiation(PID) controller increases the complexity of the system with better flexibility and robustness. Thus the fractional-order PID controller is applied to the servo system. The tuning results of PID parameters of the fractional-order servo system show that the proposed algorithm has a good performance in tuning the PID parameters of the fractional-order servo system.
文摘大容积环境测试舱内温湿度控制系统具有非线性、时变性和耦合性的特点,传统的比例积分微分(Proportion integral differential,PID)控制器参数整定方法不能满足环境测试舱温湿度控制的要求。只有获得PID控制器的最优参数才能实现环境测试舱温湿度的优化控制。该文提出一种遗传算法(Genetic algorithm,GA)优化PID控制器参数的控制算法—GA-PID。首先通过预估解耦方法对温湿度解耦,然后将目标函数作为控制器的评估值,通过遗传算法的选择、交叉、变异、迭代功能获得PID控制器参数的最优解,以弥补常规PID算法在环境测试舱温湿度控制系统中的不足。通过MATLAB进行了仿真实验,实验结果表明预估解耦可有效地对温湿度进行解耦,提出的GA-PID控制算法可实现快速、准确以及稳定的环境测试舱温湿度控制,具有更好的控制性能。
文摘针对压电柔性机械臂运行过程中的弹性振动问题,提出了基于粒子群优化算法(particle swarm optimization,简称PSO)自整定比例积分微分(proportional integral differential,简称PID)控制器参数的柔性臂振动抑制方法。采用标准粒子群优化算法,以时间乘绝对误差积(integrated time and absolute error,简称ITAE)准则为适应度函数,整定PID控制器的3个控制参数Kp,Ki和Kd,并采用Matlab Simulink平台建立双连杆压电柔性机械臂振动控制仿真模型,研制基于虚拟仪器技术的柔性臂振动控制试验系统。仿真与试验结果表明,采用常规PID控制算法和基于PSO自整定的PID控制算法均能有效地抑制柔性机械臂的弹性振动,但后者的振动抑制效果、鲁棒性与稳定性优于前者。
