Fuzzy bang–bang relay control of a single-axis active magnetic bearing system

Active magnetic bearings (AMB) are presently being utilized in various classes of rotating machinery. Although the rotor-AMB systems are open loop unstable, they are easily stabilized using feedback control schemes of which the PID controller is the most commonly used. The PID controller is however only effective at the vicinity of the rotor’s equilibrium position where the dynamics of the rotor-AMB system is linearized. Significant deviation of the rotor’s motion from this equilibrium position may occur due to large imbalance forces. In this situation, the nonlinearity in AMBs, which arises from the relationship between the electromagnetic force, coil current and air gap, may render the PID controller ineffective. For the control of nonlinear systems, artificial intelligence techniques such as fuzzy and hybrid techniques are effective. In this paper, a new fuzzy controller is proposed for the control of a single-axis AMB system. This controller is based on the bang–bang scheme, which is an old but effective technique to control nonlinear systems in optimal time. The performance of the proposed integrated fuzzy bang–bang relay controller (FBBRC) was found to be superior to that of the optimized PD controller and the conventional fuzzy logic controller. Comparison of the FBBRC with the fuzzy logic controller cascaded with a hard limiter (FBBC) relay revealed almost equal performance. High frequency chattering was however observed in the steady-state response of the FBBC. Such chattering is known to cause instability and distortion in the amplifiers that are used to supply current to the magnetic bearing actuators.     

Characteristic model based all-coefficient adaptive control of an AMB suspended energy storage flywheel test rig

Feedback control of active magnetic bearing (AMB) suspended energy storage flywheel systems is critical in the operation of the systems and has been well studied. Both the classical proportional-integral-derivative (PID) control design method and modern control theory, such as H_∞ control and μ-synthesis, have been explored. PID control is easy to implement but is not effective in handling complex rotordynamics. Modern control design methods usually require a plant model and an accurate characterization of the uncertainties. In each case, few experimental validation results on the closed-loop performance are available because of the costs and the technical challenges associated with the construction of experimental test rigs. In this paper, we apply the characteristic model based all-coefficient adaptive control (ACAC) design method for the stabilization of an AMB suspended flywheel test rig we recently constructed. Both simulation and experimental results demonstrate strong closed-loop performance in spite of the simplicity of the control design and implementation.     

Decentralized PID neural network control for five degree-of-freedom active magneticbearing

A decentralized proportional–integral–derivative neural network (PIDNN) control scheme is proposed to regulate and stabilize a fully suspended five degree-of-freedom (DOF) active magnetic bearing (AMB) system which is composed of two radial AMBs (RAMBs) and one thrust AMB (TAMB). First, the structure and operating principles of the five-DOF AMB system are introduced. Then, the adopted differential driving mode (DDM) for the drive system of the AMB is analyzed. Moreover, due to the exact dynamic model of the five-DOF AMB system is vague, a decentralized PIDNN controller is proposed to control the five-axes of the rotor simultaneously in order to confront the uncertainties including inherent nonlinearities and external disturbances effectively. Furthermore, the connective weights of the PIDNN are trained on-line and the convergence analysis of the regulating error is provided using a discrete-type Lyapunov function. Based on the decentralized concepts, the computational burden is reduced and the controller design is simplified. Finally, the experimental results show that the proposed control scheme provides good control performances and robustness for controlling the fully suspended five-DOF AMB system in different operating conditions.     

一类欧-拉系统的全系数自适应控制研究

对于动力学方程未知的全驱动多变量欧-拉系统,进行了基于特征模型的全系数自适应控制方法研究.根据全系数自适应控制思想,针对全驱动欧-拉系统提出了差分方程形式的特征模型,并得到该特征模型各系数的一些性质.基于建立的特征模型,提出多变量黄金分割控制律和相应的自适应控制策略,并对闭环系统的稳定性进行了分析.最后对两连杆的空间机器人进行了仿真,仿真结果表明了该自适应控制方法的优越性.     

基于特征模型自适应方法的一类最小相位非线性系统稳定性分析

在特征建模理论中,由全系数自适应控制器组成的闭环系统是一个非常复杂的混合系统,采用传统自适应框架难以进行分析,因此,稳定性分析一直是该领域的一个难点.本文以一类最小相位、相对阶为2的单输入单输出(SISO)高阶非线性系统为例,通过一种新的特征建模方法,把高阶混合系统变换为一个含有稳定未建模误差的、参数有界慢时变的采样间接自适应控制问题,并利用基于欧拉近似离散化模型的采样系统稳定性分析方法进行了系统分析.该方法可进一步推广到任意相对阶的SISO或多输入多输出(MIMO)系统甚至无限维最小相位系统中去.     

一种自适应PID控制及其在发电机性能测试装置中的应用

针对经典PID控制参数整定困难且受时变、非线性等因素影响不能达到预期控制效果的实际情况,设计了一种具有自学习能力的自适应PID控制器,利用Widrow-Hoff学习算法实现系统在线自动调整最佳控制参数,从而提高系统的自适应能力、改善系统的动态性能,并成功地将其应用到发电机性能自动化测试装置中。     

大滞后系统控制中专家-模糊PID方法的应用

为解决大滞后系统控制难度大的问题,设计了专家-模糊自适应PID控制器,这种控制器既具有模糊PID控制器高精度、稳定性、鲁棒性高的优点,又具有专家控制器进入稳定状态快的特点。对双容大滞后系统控制的仿真实验结果表明,该方法在动态性能上均优于单独采用模糊自适应PID方法,并且能够较快地进入稳定状态。     

转子圆度误差对四自由度AMB的稳定性影响

主动磁悬浮轴承(Active Magnetic Bearing, AMB)对转子的加工精度要求很高,圆度误差是转子加工中常见的误差,但是前人的工作中关于圆度误差影响的研究很少。建立了四自由度AMB的数学模型,分析了圆度误差对力-电流系数和力-位移系数的影响,推导了四自由度AMB的闭环特征方程。通过闭环特征方程的性质,分析了圆度误差对四自由度AMB稳定性的影响,通过仿真和实验验证了上述分析的正确性。研究结果表明,过大的圆度误差会使四自由度AMB系统不稳定,但通过选取合适的PID参数能够使系统恢复稳定。     

轴向主动磁轴承的无模型控制

轴向主动磁轴承系统是一个典型的非线性开环不稳定系统,且其准确模型也难以得到.它的控制一般是采用PID,但存在PID参数调整费时和扰动抑制效果差的问题.新的无模型控制方法不需要知道被控对象的精确模型和阶次.依据此方法所设计的控制器具有结构简单、控制参数调整方便等优点.因此,尝试将该新型控制方法应用于轴向主动磁轴承的控制.仿真和实验结果表明:与PID控制方法相比,应用该方法所设计的控制系统具有无超调、稳态精度高、对扰动抑制作用强等优点.     

Design of model-based unbalance compensator with fuzzy gain tuning mechanism for an active magnetic bearing system

This paper deals with the unbalanced vibration problem of a stabilized active magnetic bearing (AMB) system. First, a model-based integral-type observer is proposed to evaluate the shaft displacement and the unbalancing forces of the rotor of an AMB system controlled by a proportional-integral-derivative (PID) controller. A fuzzy gain tuning mechanism is added to adjust the output of the PID controller in order to overcome the disturbances and suppress the unbalancing vibration. We have shown via simulation that the addition of the integral-type force observer improves the rotating accuracy in the device operating range. The experimental results demonstrate an efficient reduction of the shaft displacement of the rotor. Thus, the proposed scheme allows for a remarkable improvement in the unbalanced vibration of an AMB system over conventional PID controls without fuzzy gain tuning mechanism.     

时滞系统的模糊自适应PID控制研究

将模糊控制和常规PID控制相结合,设计了一种模糊自适应PID控制器,该控制器根据偏差和偏差变化率的要求实时调整PID参数。通过仿真表明：该控制器既具有常规PID控制器高精度的优点,又具有模糊控制器快速、适应性强的特点,并可以迅速消除系统误差,保证了系统具有良好的动、静态特性。     

模糊免疫PID算法在温度控制系统中的应用研究

该文针对温度控制系统非线性、大滞后、参数时变的特征,设计了模糊免疫PID控制器。该控制器结合了模糊逻辑、免疫机理以及PID调节的各种优点,既具有模糊控制的非线性作用,又具有免疫控制的自适应能力,同时还具有PID控制的广泛适用性。文章介绍了模糊免疫PID控制器的控制原理和设计方法,在Matlab中编写函数仿真,结果表明该控制器能够实现持续干扰情况下的闭环鲁棒稳定,并使系统呈现良好的动态和静态性能。     

基于FUZZY-PID直流调速系统的仿真与分析

针对常规PID在直流调速系统中控制参数难以整定等问题,在对直流调速系统建模的基础上,以偏差e和偏差变化率ec作为输入,找出常规PID的三个参数与e和ec之间的模糊关系,在运行中通过检测e和ec,根据模糊推理原理来对三个参数在线修改,从而满足不同时刻的e和ec对PID参数自整定的要求,以此设计了一种基于模糊控制原理的PID控制器,并对采用FUZZY-PID控制的直流调速系统的动态过程进行仿真分析.仿真结果表明:采用FUZZY-PID控制策略的直流调速系统响应快、超调较小、鲁棒性强,较传统PID控制具有更好的动、静态特性,这说明了这种控制策略的正确性和有效性.     

Characteristic model-based all-coefficient adaptive control for automatic train control systems

Safe and reliable automatic train control is a primary consideration for any advanced rail transit system. This paper introduces the characteristic model-based modeling method into ATC system and develops an all-coefficient adaptive control. Two characteristic models,namely speed characteristic model and position characteristic model,are established for analyzing both train traction and cruising dynamical characteristics.Control strategies are proposed using single speed feedback and speed/position bi-feedback. Numerical simulations are carried out to verify the effectiveness of the proposed strategies,showing that control objective and required dynamic performance are well satisfied. Moreover,the system shows robustness against time-varying model uncertainties and unknown operational environment dynamics.     

基于自适应模糊PID控制的太阳光跟踪伺服系统

针对太阳光跟踪伺服系统中传统PID控制过程中的一些问题,通过对自适应模糊PID控制系统的分析,设计了一种双轴跟踪伺服系统自适应模糊PID控制器,同时在Simulink环境中建立了方位角跟踪传动机构仿真模型并完成了仿真。仿真结果表明,太阳光跟踪伺服系统自适应模糊PID控制器较传统PID控制器具有较强的稳定性、适应性与鲁棒性,在太阳光跟踪伺服系统控制领域具有重要的实用价值与应用空间。     

基于粒子群优化算法的PID控制器参数整定

PID控制器的性能完全依赖于其参数的整定和优化，但参数的整定及在线自适应调整对常规的PID控制器是难以解决的问题。根据粒子群算法具有对整个参数空间进行高效并行搜索的特点，提出了一种基于粒子群优化算法整定PID控制器参数的设计方法，并定义了一种新的性能指标函数来评价PID控制器的性能。现以二阶的船舶控制装置为研究对象，运用粒子群优化方法对PID控制器参数进行了寻优研究。仿真结果表明，该方法比一般PID参数整定方法具有更好的控制性能指标，有着一定的工程应用价值。     

基于Fuzzy模型的交流伺服系统自适应PID调节器的设计

为了提高交流伺服系统在实际应用场合中的控制精度和性能特性,提出了模糊自适应PID控制器。模糊自适应PID控制,将传统的PID控制和模糊控制结合起来,可以发挥两者的优势,得到更优良的控制性能。基于Matlab/Simu-link平台搭建了系统的仿真模型,内环采用PI控制,外环采用模糊自适应PID控制,并对模糊自适应控制中的模糊隶属函数进行了优化。在仿真平台上进行了突加负载实验,仿真结果验证了这种方法的可行性,同时,也可以获得较好的性能。     

自适应模糊控制技术在铝塑板剪切生产线中的实现

飞剪控制系统是一个非线性、强耦合、时变的复杂系统,用普通的PID难以达到理想的控制效果.设计出一种基于模糊控制原理的自适应PID控制系统,根据偏差和偏差变化率来实时调整kp,kl,kd的值.仿真和应用结果表明,这种模糊自适应PID比常规PID控制器在飞剪控制中具有更好的控制性能.     

自适应神经模糊推理结合PID控制的并联机器人控制方法

针对6自由度液压驱动并联机器人的精确控制问题,提出一种结合自适应神经模糊推理系统(ANFIS)和比例积分微分(PID)控制的机器人控制方法。首先,利用浮动坐标系描述法(FFRF)来模拟机器人柔性组件,并构建并联机器人的拉格朗日动力学模型。然后,根据模糊推理中的模糊规则来自适应调整PID控制器参数。最后,利用神经自适应学习算法使模糊逻辑能计算隶属度函数参数,从而使模糊推理系统能追踪给定的输入和输出数据。将该控制器与传统PID控制器、模糊PID控制器进行比较,结果表明,ANFIS自整定PID控制器大大减小了末端器位移误差,能很好的控制并联机器人末端机械手的运动。     

PID控制在火箭炮伺服系统中的仿真实现——改进的专家自适应PID控制

针对传统PID（Proportional-Integral-Derivative）控制无法兼顾部分系统的静态性能和动态性能,结合专家PID控制原理,提出了一种改进的专家自适应PID控制器的设计方案,对某火箭炮伺服系统进行仿真跟踪。给出了伺服系统的分析设计过程,利用MATLAB/Simulink完成了改进的专家自适应PID控制器在某伺服系统中的仿真应用,得到了良好的跟踪特性图,说明了该方法的有效性。