Periodic speed ripples minimization in PM synchronous motors using repetitive learning variable structure control

In this paper, we propose a simple repetitive learning variable structure control (RLVSC) scheme to reduce periodic speed ripples in a permanent magnet synchronous motor (PMSM). These speed ripples are induced by parasitic torque pulsations that vary periodically with rotor position. The conventional PI speed controller is able to reduce speed ripples to a certain extent but not sufficient enough for many high performance applications. During steady state, the RLVSC generates a reference compensation current that together with the outer loop PI speed controller is used to minimize the speed ripples. Being a plug-in module, the proposed RLVSC controller can be easily integrated to any of the existing PMSM drive systems. Experimental evaluation of the proposed scheme is carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed control scheme in reducing speed ripples by a factor of approximately 3 under various operating conditions.     

Adaptive sliding mode current control with sliding mode disturbance observer for PMSM drives

This paper focuses on the current control of a permanent magnet synchronous motor (PMSM) for electric drives with model uncertainties and external disturbances. To improve the performance of the PMSM current loop in terms of the speed of response, tracking accuracy, and robustness, a hybrid control strategy is proposed by combining the adaptive sliding mode control and sliding mode disturbance observer (SMDO). An adaptive law is introduced in the sliding mode current controller to improve the dynamic response speed of the current loop and robustness of the PMSM drive system to the existing parameter variations. The SMDO is used as a compensator to restrain the external disturbances and reduce the sliding mode control gains. Experiments results demonstrate that the proposed control strategy can guarantee strong anti-disturbance capability of the PMSM drive system with improved current and speed-tracking performance.     

Cogging effect minimization in PMSM position servo system using dual high-order periodic adaptive learning compensation

Cogging effect which can be treated as a type of position-dependent periodic disturbance, is a serious disadvantage of the permanent magnetic synchronous motor (PMSM). In this paper, based on a simulation system model of PMSM position servo control, the cogging force, viscous friction, and applied load in the real PMSM control system are considered and presented. A dual high-order periodic adaptive learning compensation (DHO-PALC) method is proposed to minimize the cogging effect on the PMSM position and velocity servo system. In this DHO-PALC scheme, more than one previous periods stored information of both the composite tracking error and the estimate of the cogging force is used for the control law updating. Asymptotical stability proof with the proposed DHO-PALC scheme is presented. Simulation is implemented on the PMSM servo system model to illustrate the proposed method. When the constant speed reference is applied, the DHO-PALC can achieve a faster learning convergence speed than the first-order periodic adaptive learning compensation (FO-PALC). Moreover, when the designed reference signal changes periodically, the proposed DHO-PALC can obtain not only faster convergence speed, but also much smaller final error bound than the FO-PALC.     

基于扰动观测器的永磁同步电机电流环自适应滑模控制

针对扰动对永磁同步电机转速伺服系统性能的影响,提出了基于扰动观测器的电流环自适应滑模控制方法。设计了自适应律在线估计系统的内部参数摄动以补偿模型不确定性扰动。同时,设计了滑模扰动观测器实时估计系统外部负载扰动,并将观测值前馈补偿到电流环自适应滑模控制器,在提高系统鲁棒性的同时降低滑模控制系统的抖振。实验结果显示,采用基于扰动观测器的电流环自适应滑模控制方法,系统可快速、准确、无超调地跟踪900r/min的速度指令,调节时间为0.08s,稳态误差为±5r/min。加入0.6N·m的负载扰动,该控制方法的最大转速波动为21r/min,比PI控制方法的转速波动减小了3.4%。仿真和实验结果表明,基于扰动观测器的电流环自适应控制方法提高了永磁同步电机转速伺服系统的鲁棒性和动态响应性能,同时可有效抑制滑模控制系统的抖振。     

基于机械臂位姿变换的柔性负载伺服驱动系统控制策略

伺服驱动系统的柔性负载端转动惯量等参数随柔性机械臂位姿变化而变化,进而影响伺服驱动系统电动机输出端转速。为降低电动机输出端速度波动,采用极点配置的方法设计伺服驱动系统转速环PI控制器参数,该参数的选择随柔性机械臂位姿变化而变化,从而使伺服驱动系统在不同位姿下获得良好的动态响应特性,避免机械谐振发生。首先根据连续体振动理论和拉格朗日原理建立了柔性负载伺服驱动系统动力学模型,并通过状态方程求得电动机转速到柔性负载驱动转矩的传递函数。将变参数PI控制策略应用于伺服驱动系统转速环控制中,分析了柔性负载对转速环的控制特性的影响,采用极点配置的方法设计控制器参数。接下来分别采用相同幅值、相同阻尼系数、相同实部3种极点配置策略设计控制器参数。讨论了这 3 种极点配置策略不同参数对系统谐振峰值、谐振频率和带宽的影响。最后通过数值仿真分析表明:伺服驱动系统等效柔性负载参数与机械臂的位姿有关;柔性负载回转半径、转动惯量较大的情况,不适宜使用相同实部的极点配置方法,但其余两种方法可通过适当选择参数使电动机输出转速波动程度减小。     

永磁直线伺服系统模糊PI速度控制器研究

针对永磁直线同步电机伺服系统常规PI速度调节器动态响应慢、输出超调大等问题,提出了模糊自适应PI速度控制器,对比常规PI速度控制器进行了仿真和实验。基于永磁直线同步电机矢量速度闭环控制,分析了模糊PI速度控制器和基于模糊PI控制器的伺服矢量控制系统的结构,设计了模糊PI速度控制器,在Matlab/Simulink仿真环境下,建立了基于模糊PI速度控制器的永磁直线同步电机伺服系统仿真模型,并通过实际永磁同步直线电机伺服系统实验对仿真结果进行了实验验证。研究结果表明,模糊PI速度控制器,相对于常规PI控制器,可以明显降低超调量和调节时间。将仿真结果和试验结果对比,两者基本吻合,说明模糊PI速度控制确实可以较好地改善永磁直线同步电机伺服系统的动态性能。     

一种双轴直驱平台新型同步渐近跟踪控制设计

针对双轴直驱平台伺服系统中存在同步进给的问题,提出一种交叉耦合迭代学习控制器与自适应加加速度控制器相结合的新型同步控制方法。首先,构建同步误差,利用交叉耦合控制器解决双轴的耦合问题;设计自适应PD型学习律减小同步误差,实现双轴协调同步。采用模型前馈控制补偿系统的参数不确定性,提高系统的响应速度。自适应加加速度控制器抑制系统中外部扰动、摩擦力等不确定性因素,实现系统的渐近跟踪控制。加加速度积分后形成反馈控制律,保证了控制信号的稳定性和连续性。设计自适应更新律,使鲁棒增益实现指数收敛并削弱测量噪声对系统的影响,增强系统的鲁棒性。系统实验结果表明,该方法能够明显地提高系统的同步性能,改善双轴直驱平台伺服系统的控制精度。     

内模控制方法在永磁直驱潮流发电系统中的应用研究

针对永磁直驱潮流发电控制系统设计的问题,对潮流流速模型、最大功率追踪策略、永磁电机矢量控制、并网逆变器控制等方面进行了研究,提出了一种基于内模控制的转速控制算法以及一种新型的并网逆变器模型。使用内模控制器代替了机侧转速外环PI控制器,同时使用并网逆变器小信号模型设计了较准确的并网逆变器的控制参数。最后,基于上述算法和模型,在Matlab软件平台中对潮流发电系统进行了仿真,并对比了传统PI控制和内模控制下潮流发电系统的输出波形。仿真结果显示,所提出的优化控制策略能够准确控制发电机输出的有功功率、并网有功功率并且能够实现无功功率独立调节以及高功率因数并网运行。     

基于等价输入干扰估计器的永磁同步电机无速度传感器控制

提出了一种新型的采用等价输入干扰估计器的永磁同步电机控制系统结构,其控制器采用PI控制方法,估计器采用状态观测器与低通滤波器的组合,在此结构下控制器与估计器的设计满足分离原理.为实时估计电机转速,根据定子侧测量的电压、电流值,基于扩展卡尔曼滤波方法(EKF)设计了电机的转速估计器.仿真结果表明在较宽的速度给定范围内,估计器有较高的估算精度,控制器能很好地抑制未知干扰,提高了永磁同步电机的性能.最后通过dSPACE实验验证了该方法的有效性.     

交流异步电机矢量控制滑模速度控制器设计

为了提高异步电动机调速系统的动态品质,在传统指数趋近率的基础上提出了一种新型趋近律,该方法能够根据系统状态距离平衡点的远近而自适应调整指数趋近律的收敛速度。为了提高系统的动态性能,将该趋近律算法引入到异步电机的速度控制器的设计中,提出了一种适用异步电机矢量控制系统的滑模速度控制器。与传统PI调节器进行了比较,仿真结果表明,该速度控制器能够有效地提高系统的静态、动态特性与鲁棒性。     

Model predictive control for a class of systems with isolated nonlinearity

The paper is concerned with an overall convergent nonlinear model predictive control design for a kind of nonlinear mechatronic drive systems. The proposed nonlinear model predictive control results in the improvement of regulatory capacity for reference tracking and load disturbance rejection. The design of the nonlinear model predictive controller consists of two steps: the first step is to design a linear model predictive controller based on the linear part of the system at each sample instant, then an overall convergent nonlinear part is added to the linear model predictive controller to combine a nonlinear controller using error driven. The structure of the proposed controller is similar to that of classical PI optimal regulator but it also bears a set-point feed forward control loop, thus tracking ability and disturbance rejection are improved. The proposed method is compared with the results from recent literature, where control performance under both model match and mismatch cases are enlightened.     

基于粒子群算法的参数自整定伺服控制器的设计

提出了一种可以不考虑系统的数学模型以及外部的工作状况,直接通过在可行域内搜索最优解来找到合适的控制器参数的方法。以伺服系统的速度环控制器为研究对象,将粒子群优化算法应用于控制器的参数自整定中,设计了参数自整定PI控制器。在Matlab／Simulink环境下建立永磁同步电机伺服系统的仿真模型,对比了通过人工参数整定与基于粒子群优化算法的参数整定两种方法。仿真实验结果表明,该方法整定出的控制器参数可以使伺服系统的性能得到较大提高,具有较大的应用价值。     

MTPA control of mechanical sensorless IPMSM based on adaptive nonlinear control

In this paper, an adaptive nonlinear control scheme has been proposed for implementing maximum torque per ampere (MTPA) control strategy corresponding to interior permanent magnet synchronous motor (IPMSM) drive. This control scheme is developed in the rotor d–q axis reference frame using adaptive input–output state feedback linearization (AIOFL) method. The drive system control stability is supported by Lyapunov theory. The motor inductances are online estimated by an estimation law obtained by AIOFL. The estimation errors of these parameters are proved to be asymptotically converged to zero. Based on minimizing the motor current amplitude, the MTPA control strategy is performed by using the nonlinear optimization technique while considering the online reference torque. The motor reference torque is generated by a conventional rotor speed PI controller. By performing MTPA control strategy, the generated online motor d–q reference currents were used in AIOFL controller to obtain the SV-PWM reference voltages and the online estimation of the motor d–q inductances. In addition, the stator resistance is online estimated using a conventional PI controller. Moreover, the rotor position is detected using the online estimation of the stator flux and online estimation of the motor q-axis inductance. Simulation and experimental results obtained prove the effectiveness and the capability of the proposed control method.     

基于PMAC运动控制卡的风洞风速控制系统

介绍了一种基于PMAC多轴运动控制卡的风洞风速控制系统。采用Pc作为上位机,PMAC运动控制卡为控制核心,风速传感器为反馈,控制两套交流旋转伺服系统驱动风扇运转。建立了伺服系统的控制模型并在Simulink仿真环境下进行了仿真;利用Visualc#语言开发设计了风洞风速控制软件。在所搭建风速控制平台上进行风速控制实验,结果表明该风洞控制系统能够快速、准确实现风速的调控。     

Design and experimental realization of a robust decentralized PI controller for a coupled tank system

This paper presents design and realization of a robust decentralized PI controller for regulating the level of a coupled tank system. The proposed controller is designed based on a predefined reference transfer function model in which we adopt a frequency matching of actual and reference models. Realization of control algorithms for a multivariable system is often complicated owing to uncertainties in the process dynamics. In this paper, initially a frequency response fitting model reduction technique is adopted to obtain a First Order Plus Dead Time (FOPDT) model of each higher order decoupled subsystem. Further, using the obtained reduced order model, the proposed robust decentralized PI controller is designed. The stability and performance of the proposed controller are verified by considering multiplicative input and output uncertainties. The performance of the proposed robust decentralized controller has been compared with that of a decentralized PI controller. To validate the performance of the proposed control approach, real-time experimentation is pursed on a Feedback Instrument manufactured coupled tank system.     

交流电机位置伺服系统的扰动补偿控制

针对交流电机伺服系统在未知负载条件下进行准确位置控制的需求,提出了一种参数化复合控制方案。该方案建立在交流电机磁场定向矢量控制构架的基础上,以转矩电流作为控制信号(电流内环的给定值),以电机转角位置信号作为可量测的系统输出量,设计了基于极点配置的状态反馈与扰动前馈补偿组成的参数化控制律,并利用一个降阶线性扩展状态观测器对电机转速(未量测)和未知负载扰动加以估计。该控制律通过TMS320F28335DSP编程,在一台永磁同步电机伺服系统上进行了实验测试,实验结果验证了伺服系统能在未知负载情况下对各目标位置进行平稳且准确地跟踪。研究结果表明,这种基于扩展状态观测器的复合控制方案可以有效地实现交流伺服系统的高性能位置调节,且对负载幅值和模型参数差异具有较好的鲁棒性。     

模糊单神经元PI复合调节的永磁同步电动机矢量控制系统

在分析永磁同步电动机（PMSM）数学模型的基础上,提出了一种模糊单神经元复合控制的方法,它兼有模糊控制和单神经元控制的优点,对被控对象的数学模型要求不高,能够克服传统控制方法中由于电机参数时变和负载扰动而带来的系统性能变差,具有较好的鲁棒性和实时性.该算法已应用于永磁同步电动机双闭环矢量控制系统中的转速控制器中.仿真结果表明,采用该方法的系统具有良好的静态和动态性能.     

直联压缩机两种不同控制方法的比较研究

建立了空压机的负载数学模型,对其负载进行了分析。建立了永磁同步电机的数学模型,给出了一种用于交流永磁同步电机速度控制的滑模控制器。根据永磁同步电机的控制特点和空压机负载特点进行Simulink仿真,仿真结果表明,加滑模控制器时,电机转速波动较小。最后,对用永磁同步电机驱动直联便携式空压机做实验。实验结果表明,与常规的PI控制策略相比,滑模变结构控制改善了系统的动态性能,有较强的鲁棒性。永磁同步电机驱动空压机效率高,转速波动小。     

基于RCP开发的PMSM驱动系统实验平台设计

针对永磁同步电动机(PMSM)驱动技术研究中控制单元资源配置繁琐、算法编程复杂、研究开发周期长等问题,设计了基于快速控制原型(RCP)的实验平台。根据PMSM工作与控制原理,搭建了与dSPACE半实物实时仿真系统对接的软硬件模块,包括逆变器、传感器、保护隔离电路等硬件部分和基于Simulink搭建算法仿真模型、在线调试ControlDesk界面等软件部分。实验证明了驱动系统实验平台的有效性,为PMSM动态性能优化、控制策略研究以及电动机伺服系统预先研发提供了良好的开发环境。     

基于改进PSO算法的电液位置伺服系统MRAC跟踪控制

针对电液位置伺服系统控制性能不佳的问题,提出一种基于改进PSO算法优化的模型参考自适应(Model Reference Adaptive Control,MRAC)跟踪控制方法。首先,建立电液位置伺服系统数学模型,设计出模型参考自适应控制器;其次,分析PSO算法、APSO算法在参数寻优过程中的不足,提出一种改进的PSO算法;最后,将改进的PSO算法用于模型参考自适应控制器以改善其控制性能。结果表明,改进PSO算法优化的模型参考自适应控制具有响应速度快、跟踪精度高的优点。