实时遗传算法在位置伺服系统中的应用

利用前馈控制与传统的反馈控制结合来控制位置伺服系统,由实时遗传算法在线调整前馈控制器参数,使其为对象的逆模型。用实际输出和设定值之间的跟踪误差计算个体的适应度,突变操作的范围也由跟踪误差确定,以保证参数的收敛。实验结果证明实时遗传算法的嵌入使得系统在跟踪、抗扰性能等方面有明显改善。     

遗传算法优化的RBF神经网络控制器

为了消除神经网络参数初值对控制器性能的影响,提出了一种改进遗传算法优化的RBF神经网络控制器.该方法设计了基于性能指标的适应度函数,自适应的交叉概率、变异概率,引入移民的遗传算法,保证了得到的控制器为最优参数控制器.该方法可用于非线性对象的控制器设计,仿真结果说明了该方法的有效性.     

改进LuGre模型的挖掘机器人摩擦补偿控制

非线性摩擦会降低挖掘机器人电液伺服系统的动静态性能,引起轨迹爬行、平峰和稳态误差等现象。经典LuGre摩擦模型仅与速度有关,内部鬃毛状态变量无法准确测量,无法全面描述复杂的挖掘机器人电液伺服系统摩擦特性。本文综合考虑电液伺服系统位置、速度和方向等信息,设计了一种改进的LuGre摩擦模型,同时引入速度阈值解决了弹性鬃毛平均变形状态观测器不稳定问题。其次,为了解决传统优化算法陷入局部最优解、收敛速度慢等问题,通过引入惯性权重、异步变化和精英突变操作改进基本粒子群优化算法,以精准快速辨识出改进LuGre摩擦模型中的6个未知参数。最后,结合辨识出的摩擦模型,基于结构不变性原理设计前馈摩擦补偿控制器,并在23吨挖掘机器人进行了正弦和三角波不同工况下的轨迹跟踪实验。实验结果表明,传统的比例积分微分控制器跟踪误差最大,三角轨迹最大跟踪误差达到了29.68 mm,基于改进LuGre模型设计的前馈摩擦补偿控制器仅为9.70 mm,误差减小了67.31%,基于改进LuGre模型设计的前馈摩擦补偿控制器可以有效提升挖掘机器人的轨迹跟踪精度。     

基于数字前置滤波器优化的ZPETC在伺服跟踪控制中的应用

在高精度伺服跟踪控制系统中,为使输出响应能完整地跟踪输入指令,不仅要求输出与输入之间的相位差为零,而且要求幅值一致,通常采用零相位误差跟踪控制器(ZPETC)作为前馈控制器,补偿相位误差,但同时也产生一定的增益误差.为改善ZPETC的跟踪性能,提出一种基于L2-范数优化的前馈控制器设计方案,通过选取适当的目标函数,设计出最优的数字前置滤波器(DPF),将此滤波器与ZPETC串联构造成新的前馈控制器.仿真结果表明,采用本文提出的优化设计方案,既补偿了相位误差,又改善了系统的增益性能,从而提高了系统的跟踪精度.     

Robust feedforward control system considering sudden disturbance for optical disk recording system

This paper proposes a new robust feedforward tracking servo system for optical disk recording systems in the case of a sudden disturbance for the optical disk recording system. In optical recording systems, the tracking servo system must suppress tracking error below its tolerance. This paper designs the robust feedback control system by using the coprime factorization and disturbance observer. The proposed robust feedback control system suppresses the sudden disturbance caused by walking and running. The detecting signal of optical disk recording systems is only a tracking error. Hence, the feedforward controller of the proposed tracking control system is constructed based on both Zero Phase Error Tracking (ZPET) control theory and prediction of tracking error. The experimental results point out that the proposed tracking servo system has a quick and precise tracking response and keeps the residual tracking error below its tolerance. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 156(4): 60– 68, 2006; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20257     

自动离合器位置跟踪神经元自适应PID控制

针对自动离合器位置难以准确快速跟踪控制的问题,在采用蜗卷弹簧作为补偿装置改善驱动机构性能基础上,设计了一种具有前馈作用的增益可调神经元自适应PID控制器,利用前馈控制加快控制器的响应速度,利用神经元的自学习功能实现权值的自动调整,采用PSD算法实现增益的自动调整.实验结果表明,与常规控制器相比,该控制器用于离合器位置跟踪控制动、静态误差小,能够适应离合器负荷和目标接合速度的非线性变化,具有较好的鲁棒性,能够满足离合器位置跟踪控制要求.     

基于优化设计数字滤波器在伺服控制中的应用

在高精确度伺服控制系统中,通常采用零相位误差跟踪控制器ZPETC作为前馈控制器, 作用是消除相位误差,但是,ZPETC将导致很小的增益误差。为改善ZPETC对系统增益性能的影响,提出一种基于L2范数优化的前馈控制器设计方案,通过选取适当的目标函数,设计出最优的数字滤波器,将此滤波器与ZPETC串联组成新的前馈控制器,此滤波器在保持系统零相位误差的同时,改善了系统的增益性能,提高了跟踪精确度。仿真实验表明,采用本文提出的优化设计方案,能够改善系统的运动跟踪性能。     

最优ZPETC在伺服跟踪控制中的应用

在高精度伺服跟踪控制系统中,为使输出响应能完整地跟踪输入指令,不仅要求输出与输入之间的相位差为零,而且要求幅值一致,通常采用零相位误差跟踪控制器(ZPETC)作为前馈控制器,补偿相位误差,但同时也产生一定的增益误差.为改善ZPETC的跟踪性能,提出一种基于L2-范数优化的前馈控制器设计方案,通过选取适当的目标函数,设计出最优的数字前置滤波器(DPF),将此滤波器与ZPETC串联构造成最优ZPETC.仿真结果表明,该优化设计方案,既补偿了相位误差,又改善了系统的增益性能,从而提高了系统的跟踪精度.     

A modified MPPT control loop for PV/battery‐charging system using PI controller optimally tuned with GA

In this study, a stand‐alone photovoltaic (PV)/battery‐charging system is proposed to efficiently charge a lead–acid battery with the available maximum power from the PV array. The relative sizing of the battery characteristics with respect to the PV array characteristics is indicated. The maximum‐power‐point (MPP)‐tracking operation is achieved through developing a new control loop, which is based on using the incremental conductance algorithm and the PI controller. The parameters of the PI controller are optimally tuned using the genetic algorithm (GA) by minimizing the integral of squared error and settling time. The proposed system was simulated twice by using MATLAB‐SIMULINK and by writing the appropriate MATLAB code in an M‐file. The SIMULINK model was designed to investigate the performance of the proposed system, whereas the M‐file model was designed to be used with the GA tool under MATLAB software, to optimally tune the PI controller. The simulation results indicated a rapid tracking capability for the PV array MPPs and a good matching between the PV array and the battery under various charging conditions. Copyright © 2010 John Wiley & Sons, Ltd.     

遗传算法在模糊控制器参数寻优中的应用研究

介绍遗传算法的基本原理及实现步骤，并运用模糊控制器的参数寻优中，对复杂被控对象实现了最优控制，模糊控制规则采用基于解析表达式的模糊数模型，用遗传算法对模糊控制器中的量化因子和比例因子及偏差加权因子进行编码，确定适应度函数，在复制，交叉，变异的进化过程中获得全局最优点，改善模糊控制性能。在Matlab环境中实现遗传算法各算子的编程，并以二阶系统为例，对常规模糊控制器与经过遗传处寻优后的模糊控制器进行了仿真比较，其结果表明采用遗传算法寻优能获得较好的控制效果，该方法具有良好的动静态特性。     

同步发电机励磁系统的Tabu搜索PID控制

发电机励磁控制关系到系统电压稳定和无功分配。复杂突变工况下,采用工程整定方法得到的PID参数很难满足系统稳定的高品质要求,寻找参数的智能优化方法成为研究热点。禁忌搜索(Tabu)是一种高效搜索算法,模拟人类记忆来跳出一般优化方法的早熟局限。本文通过动态调整邻域范围、终止准则,改进禁忌条件来提高算法效率。建立励磁调节系统及PSS模型,以综合电压性能指标为适应度,应用Tabu优化PID参数,并在RTDS实验装置上进行验证。优化后的PID控制,电压跟踪和抗干扰性能都有明显提升,证明了该方法的有效性。     

Adaptive asymptotical tracking controller design for uncertain nonaffine nonlinear system with high‐order mismatched disturbances

In this paper, the problem of anti‐disturbance asymptotical tracking control is studied for nonaffine systems with high‐order mismatched disturbances. The disturbances can be described as polynomial functions, which are first estimated by constructing generalized extended state filter. The nonaffine system is changed into an augmented affine system via introducing an auxiliary integrator. A novel adaptive anti‐disturbance tracking controller is recursively designed, where the disturbance estimation is used for feedforward compensation at each step. A sliding mode differentiator is applied to reduce the computational burden taken by the backstepping method. The boundedness of the closed‐loop system is proved based on Lyapunov stability theory and zero error tracking performance is ensured. Finally, a numerical example is provided to show the effectiveness of the proposed scheme.     

Dynamic behavior of cycloconverter system

The dynamic behavior of a multiprocessor-based 12-pulse circulating current cycloconverter is discussed. This type of cycloconverter can provide a high output to input frequency ratio for fixed and variable-frequency applications. Waveform level simulation of the cycloconverter indicates that both feedback and feedforward techniques can be used to minimize the tracking error of the circulating current controller. The lab prototype is used to test the response of the 12-pulse, single-phase cycloconverter system. The control is implemented by a total of six 16-b microprocessor cards and additional input and output cards resident in a single card cage     

Joint angle control by FES using a feedback error learning controller.

The feedback error learning (FEL) scheme was studied for a functional electrical stimulation (FES) controller. This FEL controller was a hybrid regulator with a feedforward and a feedback controller. The feedforward controller learned the inverse dynamics of a controlled object from feedback controller outputs while control. A four-layered neural network and the proportional-integral-derivative (PID) controller were used for each controller. The palmar/dorsi-flexion angle of the wrist was controlled in both computer simulation and FES experiments. Some controller parameters, such as the learning speed coefficient and the number of neurons, were determined in simulation using an artificial forward model of the wrist. The forward model was prepared by using a neural network that can imitate responses of subject's wrist to electrical stimulation. Then, six able-bodied subjects' wrist was controlled with the FEL controller by delivering stimuli to one antagonistic muscle pair. Results showed that the FEL controller functioned as expected and performed better than the conventional PID controller adjusted by the Chien, Hrones and Reswick method for a fast movement with the cycle period of 2 s, resulting in decrease of the average tracking error and shortened delay in the response. Furthermore, learning iteration was shortened if the feedforward controller had been trained in advance with the artificial forward model.     

平面电机自适应加速度前馈运动控制

为解决在较大加速度运动条件下,固定不变的加速度前馈系数难以提高平面电机加、减速阶段的伺服性能的问题,提出一种基于比例微分控制器输出与目标加速度的自适应前馈系数求解方法.采用最小二乘法,根据运动过程中比例微分控制器输出与目标加速度数据集,对前馈系数进行在线修正.通过引入遗忘因子,使得前馈系数与基于当前位置的动态特性更加匹配.分别采用沿y、x方向的不同加速度轨迹,在最大加速度为20 m/s2时,加、减速段的最大轨迹跟踪误差为0.56 μm.该方法完全基于在线运动控制实验,实现了无需电机模型参数的前馈系数求解.     

双三相永磁同步电机位置伺服前馈反馈复合控制

传统伺服系统的位置控制器只采用纯比例控制,不能兼顾系统的响应速度和稳定裕度,且对斜坡等输入信号不能实现无差跟踪。通过分析位置伺服系统的传递函数,设计了一种新型前馈反馈复合控制器。该复合控制器重构了系统的误差传递函数,使系统能够准确跟踪给定信号,提高了伺服系统的跟踪性能和稳定性。为了进一步提升电流环动态性能,在电流环添加反电动势前馈势补偿,用于减小反电动势对电流响应的影响。仿真和试验表明,该控制方法提高了位置伺服系统的动态性能和跟踪精度,验证了前馈反馈复合控制的有效性。     

基于BAS-PSO优化自抗扰的高速列车速度跟踪控制

针对高速列车自动驾驶系统,采用基于天牛须粒子群（BAS-PSO）优化自抗扰控制（ADRC）的算法,设计速度跟踪控制器。基于列车动力学模型设计自抗扰控制器,并以ITAE作为目标函数,利用BAS PSO实现参数整定。选用CRH380A型动车组参数,通过MATLAB进行仿真验证,对比BAS-PSO、PSO以及改进鲨鱼优化ADRC算法对列车目标速度曲线的追踪效果,其中基于BAS-PSO优化ADRC算法的列车目标速度曲线跟踪误差保持在±0.4 km/h的范围内,相比另外两种算法更加紧密地贴近目标速度曲线。结果表明,基于BAS-PSO优化ADRC具有跟踪误差小、抗干扰能力强的优点。     

基于小波神经网络PID的永磁同步电机转速控制

提出了基于小波神经网络PID的永磁同步电机(PMSM)转速控制策略。根据系统运行参数的变化,采用三层前馈式人工神经网络,基于梯度下降纠正误差法在线训练实时更新PID参数值。采用小波神经网络和增量式PID共同构成转速环控制器。建立PMSM数学模型,设计PMSM速度环控制器,构建S函数,对控制算法进行仿真试验,验证了该控制算法的先进性。试验结果表明,所提控制策略比传统PID转速控制具有更好的动态性能和抗干扰能力。     

基于RFNN补偿的直线伺服系统反推控制

针对永磁直线同步电机(PMLSM)伺服系统,在分析影响直线伺服跟踪精度因素的基础上,采用智能反推控制策略对该伺服系统进行有效的补偿控制。考虑参数变化、外部负载扰动和摩擦力等不确定因素对系统伺服性能的影响,设计基于递归模糊神经网络(RFNN)的反推控制器,利用了递归神经网络具有捕获系统动态信息的优点,可实时补偿不确定因素对跟踪性能的影响。仿真结果表明,控制策略明显降低了不确定因素对系统性能的影响,从而显著提高了直线伺服系统的位置跟踪精度。     

基于PI迭代学习的有源滤波器电流跟踪控制

在采用有源电力滤波器消除谐波过程中,由于滤波器的检测精度有限,指令电流计算延时和输出滤波器的相移等因素影响,传统PI控制滤波效果不够理想。本文针对稳定性负载电网谐波具有重复周期性特点,提出了一种基于PI型学习律的迭代学习控制算法,通过实施遗忘因子学习律增强系统鲁棒性的同时,引入了在最优目标选择下模糊参考电流误差的D型学习律前馈环节,提高了系统的跟踪精度。仿真与现场运行结果都证明,该控制方法具有谐波电流跟踪效果好,计算量小,工程易于实现等特点。