Improved quantum bacterial foraging algorithm for tuning parameters of fractional-order PID controller

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.

基于ESO和分数阶PID的改进P&O控制策略

为了提高光伏电池转换效率、降低能量损失,有必要研究最大功率点跟踪(maximum power point tracking,MPPT)方法。针对传统扰动观察法(perturbation observation method,P&O)存在无法兼顾跟踪速度与稳态精度、在光照度发生较大变化时会产生误判现象的问题,文中提出一种能适应环境变化的变步长P&O控制策略。首先,利用光伏电池刚启动时类似恒流源的特性获取当前光照度下的短路电流,通过固定电流法推导出最大功率点(maximum power point,MPP)的参考电压;其次,当光照度突变时,提出功率修正方法,并给出突变时的变步长调整策略;最后,设计基于线性扩张状态观测器(linear extended state observer,LESO)的分数阶比例积分微分(fractional order proportion integration differentiation,FOPID)控制器,可以对算法输出的参考电压进一步进行跟踪补偿。仿真结果表明,所提控制策略可以提高稳态精度和跟踪速度,有效提高光伏电池的输出功率。

Research on Stick-Slip Vibration Suppression Method of Drill String Based on Machine Learning Optimization

During the drilling process,stick-slip vibration of the drill string is mainly caused by the nonlinear friction gen-erated by the contact between the drill bit and the rock.To eliminate the fatigue wear of downhole drilling tools caused by stick-slip vibrations,the Fractional-Order Proportional-Integral-Derivative(FOPID)controller is used to suppress stick-slip vibrations in the drill string.Although the FOPID controller can effectively suppress the drill string stick-slip vibration,its structure isflexible and parameter setting is complicated,so it needs to use the cor-responding machine learning algorithm for parameter optimization.Based on the principle of torsional vibration,a simplified model of multi-degree-of-freedom drill string is established and its block diagram is designed.The continuous nonlinear friction generated by cutting rock is described by the LuGre friction model.The adaptive learning strategy of genetic algorithm(GA),particle swarm optimization(PSO)and particle swarm optimization improved(IPSO)by arithmetic optimization(AOA)is used to optimize and adjust the controller parameters,and the drill string stick-slip vibration is suppressed to the greatest extent.The results show that:When slight drill string stick-slip vibration occurs,the FOPID controller optimized by machine learning algorithm has a good effect on suppressing drill string stick-slip vibration.However,the FOPID controller cannot get the drill string system which has fallen into serious stick-slip vibration(stuck pipe)out of trouble,and the machine learning algorithm is required to mark a large amount of data on adjacent Wells to train the model.Set a reasonable range of drilling parameters(weight on bit/drive torque)in advance to avoid severe stick-slip vibration(stuck pipe)in the drill string system.

分数阶PID控制器参数的自适应设计

分数阶比例-积分-微分(proportional-integral-derivative,PID)控制器是整数阶PID控制器在复数域的推广,通过引入两个可调参数λ和μ使得控制器参数整定范围变大,但使得参数设计变得更加复杂。针对分数阶PID控制器的特点,提出了一种参数自适应的改进型差分进化算法整定方法,在目标函数的选取上,针对传统时间与绝对误差乘积准则对超调量控制不足,引入误差与控制信号加权,分别对整数阶和分数阶被控模型进行理论分析和数值仿真,仿真结果证明了该控制器参数设计方法的有效性和准确性。

制导弹箭分数阶控制系统

针对制导弹箭控制系统中系统参数出现较大的非线性和时变性等问题,整数阶PID控制难以获得满意的控制效果。根据分数阶微积分的定义和性质,构造了分数阶控制器的结构,分析了其控制器参数对系统性能的影响,将分数阶控制器应用到制导弹箭的控制系统设计中,并对制导弹箭的分数阶控制系统进行了研究。结果表明,分数阶控制器较PID控制器有更强的鲁棒性和抗干扰能力,可以应用于制导弹箭的控制系统中,设计的分数阶控制器的频域和时域响应特性较PID控制器要好。研究结果为分数阶控制器在制导弹箭控制系统中的应用提供了理论基础。

抽水蓄能机组空载工况分数阶PID调节控制

抽水蓄能机组运行在低水头空载工况易进入"S"特性区域,进而引起机组频率大范围波动和运行不稳定。针对传统比例—积分—微分(PID)控制在低水头空载工况下不能较好地调节抽水蓄能机组频率波动的不足,基于全特性曲线对数曲线投影变换的水泵水轮机数学模型,提出了适用于抽水蓄能机组低水头空载工况运行的分数阶PID(FOPID)调节系统模型,并运用粒子群优化算法优化FOPID控制参数,重点讨论了FOPID控制在低水头空载开机和空载频率扰动时的应用。仿真分析表明:相较于传统PID控制,FOPID控制使得低水头空载运行时机组频率和导叶开度的过渡过程有了明显的改进,提高了抽水蓄能机组调节系统的速动性与稳定性。

基于差分进化分数阶PID的注塑机液压缸控制

注塑机、液压电梯、调速作动器等变转速泵控缸属强非线性液压系统,其速度跟踪控制存在响应滞后、稳态精度差等问题,为此,提出基于分数阶PID的控制策略来实现由伺服电机驱动定量泵的注塑机液压缸速度跟踪控制。首先,结合自适应差分进化算法对控制器参数进行在线整定;然后,利用伺服电机驱动的齿轮泵控液压缸注塑机工况模拟加载试验系统,建立了变转速泵控液压缸的差分进化分数阶PID控制器(Self-adaptive differential evolution fractional order PID,SADE-FOPID)模型。仿真结果表明,运用自适应差分进化算法优化控制参数的分数阶PID控制比经典PID控制具备更好的稳态精度和快速响应性能,反复实验结果证明了该控制策略的有效性和可行性,并具备良好的抗负载扰动性能。

基于遗传算法的应急救援车辆线控转向系统的分数阶PID控制方法（英文）

针对传统应急救援车辆很难顺利通过狭小地域的救援现场,本文设计电液线控转向系统,实现应急救援车辆的多模式转向。首先,详细描述了电液线控转向系统的总体架构和数学模型。其次,设计基于分数阶微积分理论的分数阶PID控制器,控制线控转向系统中转向油缸的运动。进一步,在分数阶PID控制器设计中考虑抗饱和问题。然后,通过遗传算法最大化适应度函数,优化分数阶PID控制器的5个参数。该适应度函数包括时间乘绝对值误差积分,最大超调和调节时间。最后,通过时域仿真来判定分数阶PID控制器的性能。仿真结果表明,本文所提出的分数阶PID控制器在瞬态响应,跟踪能力和鲁棒性方面比传统PID控制器具有更好的控制特性。

基于分数阶PID理论的汽车线控转向的主动控制

为实现更好的车辆稳定性,提出一种基于分数阶比例积分微分(PID)理论的线控转向(SBW)主动控制方法。建立了SBW系统Simulink动力学模型,设计了定横摆角速度增益的理想变传动比曲线;基于横摆角速度反馈的状态跟踪校正控制策略,提出该算法;利用Oustaloup滤波器和粒子群优化算法,进行了有理化和参数整定处理。在高速双移线和鱼钩工况下,进行了离线Carsim/Simulink联合仿真分析。结果表明:相比于传统PID控制和模糊控制,该控制方法的横摆角速度降低了4.7%和9%,侧向加速度降低了6.3%和7.7%,质心侧偏角降低了9.8%和19.5%;因而,该控制器具有较好的SBW主动控制性能。

A Hybrid Firefly Algorithm for Optimizing Fractional Proportional-Integral-Derivative Controller in Ship Steering

In this paper, a new algorithm which integrates the powerful firefly algorithm(FA) and the ant colony optimization(ACO) has been used in tracking control of ship steering for optimization of fractional-order proportional-integral-derivative(FOPID) controller gains. Particle swarm optimization(PSO) algorithm is also used to optimize FOPID controllers, and their performances are compared. It is found that FA optimized FOPID controller gives better performance than others. Sensitivity analysis has been carried out to see the robustness of optimum FOPID gains obtained at nominal conditions to wide changes in system parameters, and the optimum FOPID gains need not be reset for wide changes in system parameters.