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
An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the...An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral-derivative （PID） parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor （PMSM） under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.展开更多
大量新能源并入电网,对水电的运行提出了更高的要求。为了提高水电的调节特性,提出一种考虑综合调节特性的水轮机调速器比例积分微分(proportional integral derivative,PID)控制参数整定方法。首先,基于特征线法和水轮机特性曲线建立...大量新能源并入电网,对水电的运行提出了更高的要求。为了提高水电的调节特性,提出一种考虑综合调节特性的水轮机调速器比例积分微分(proportional integral derivative,PID)控制参数整定方法。首先,基于特征线法和水轮机特性曲线建立了水轮机调节系统模型。其次,基于第三代非支配排序遗传算法(non-dominated sorting genetic algorithmⅢ,NSGA-Ⅲ),以转速的时间乘绝对误差积分准则(integrated time and absolute error,ITAE)和超调量为目标,以比例调节系数,积分调节系数和微分调节系数为决策变量,得到帕雷托(Pareto)解集。最后,基于优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS),以增减负荷工况下的功率反调,超调量,稳定时间,机组水头极值,转速ITAE和超调量为指标,最终得到综合调节特性最好的PID参数。结果表明:该PID参数整定方法可以有效提升水轮机的综合调节特性,为实际工程中PID参数的选取提供理论依据。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.
基金supported by the National Natural Science Foundation of China(61301011)the Fundamental Research Funds for the Central Universities(HIT.NSRIF.2012010)+1 种基金the China Postdoctoral Science Foundation(2013M540279)the Heilongjiang Postdoctoral Financial Assistance(LBH-Z11157)
文摘An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed algorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportionalintegral-derivative （PID） parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor （PMSM） under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.
文摘大量新能源并入电网,对水电的运行提出了更高的要求。为了提高水电的调节特性,提出一种考虑综合调节特性的水轮机调速器比例积分微分(proportional integral derivative,PID)控制参数整定方法。首先,基于特征线法和水轮机特性曲线建立了水轮机调节系统模型。其次,基于第三代非支配排序遗传算法(non-dominated sorting genetic algorithmⅢ,NSGA-Ⅲ),以转速的时间乘绝对误差积分准则(integrated time and absolute error,ITAE)和超调量为目标,以比例调节系数,积分调节系数和微分调节系数为决策变量,得到帕雷托(Pareto)解集。最后,基于优劣解距离法(technique for order preference by similarity to ideal solution,TOPSIS),以增减负荷工况下的功率反调,超调量,稳定时间,机组水头极值,转速ITAE和超调量为指标,最终得到综合调节特性最好的PID参数。结果表明:该PID参数整定方法可以有效提升水轮机的综合调节特性,为实际工程中PID参数的选取提供理论依据。
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。
