On-line gain-tuning IP controller using RFNN

In this study an integral-proportional (IP) controller with on-line gain-tuning using a recurrent fuzzy neural network (RFNN) is proposed to control the mover position of a permanent magnet linear synchronous motor (PMLSM) servo drive system. The structure and operating principle of the PMLSM are first described in detail. A field-oriented control PMLSM servo drive is then introduced. After that, an IP controller with on-line gain tuning using an RFNN is proposed to control the mover of the PMLSM for achieving high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN on line. Moreover to guarantee the convergence of tracking error for the periodic step-command tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Furthermore, the proposed control system is implemented in a PC-based computer control system, Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line learning capability of the RFNN. In addition, the proposed on-line gain-tuning servo drive system is robust with regard to parameter variations and external disturbances     

Novel sliding mode controller for synchronous motor drive

A novel sliding mode controller with an integral-operation switching surface is proposed. Furthermore, an adaptive sliding mode controller is investigated, in which a simple adaptive algorithm is utilized to estimate the bound of uncertainties. The position control for a permanent magnet (PM) synchronous servo motor drive using the proposed control strategies is illustrated. The theoretical analysis and the theorems for the proposed sliding mode controllers are described in detail. Simulation and experimental results show that the proposed controllers provide high-performance dynamic characteristics and are robust with regard to plant parameter variations and external load disturbance     

Induction motor servo drive with adaptive rotor time-constantestimation

An indirect filed-oriented induction motor (IM) position servo drive with adaptive rotor time-constant estimation and an on-line trained neural network controller is presented. First, the rotor time-constant is estimated real-time on the basis of the model reference adaptive system (MRAS) theory. Next, a linear model-following controller (LMFC) is designed according to the estimated plant model to allow the state responses of the plant to follow the reference model. Then an augmented signal generated from the proposed neural network controller, whose connective weights are trained on-line according to the model-following error of the states, is added to the LMFC system to preserve a favorable model-following characteristic under various operating conditions     

Adaptive Nonlinear Optimal Compensation Control for Electro-hydraulic Load Simulator

 Directing to the strong position coupling problem of electro-hydraulic load simulator (EHLS), this article presents an adap-tive nonlinear optimal compensation control strategy based on two estimated nonlinear parameters, viz. the flow gain coeffi-cient of servo valve and total factors of flow-pressure coefficient. Taking trace error of torque control system to zero as control object, this article designs the adaptive nonlinear optimal compensation control strategy, which regards torque control output of closed-loop controller converging to zero as the control target, to optimize torque tracking performance. Electro-hydraulic load simulator is a typical case of the torque system which is strongly coupled with a hydraulic positioning system. This article firstly builds and analyzes the mathematical models of hydraulic torque and positioning system, then designs an adaptive nonlinear optimal compensation controller, proves the validity of parameters estimation, and shows the comparison data among three control structures with various typical operating conditions, including proportion-integral-derivative (PID) controller only, the velocity synchronizing controller plus PID controller and the proposed adaptive nonlinear optimal compensation controller plus PID controller. Experimental results show that systems’ nonlinear parameters are estimated exactly using the proposed method, and the trace accuracy of the torque system is greatly enhanced by adaptive nonlinear optimal compensation control, and the torque servo system capability against sudden disturbance can be greatly improved.     

Decoupling and tracking control of induction motor drive

This work introduces an adaptive observation system and a robust control system for achieving the favorable decoupling control and high-precision speed tracking property of an induction motor (IM) drive system. First, an adaptive observation system with an inverse rotor time-constant observer is derived on the basis of model reference adaptive system (MRAS) theory to preserve the decoupling control characteristic of an indirect field-oriented IM drive. The adaptive observation system is implemented using a digital signal processor (DSP) with a high sampling rate to make it possible to achieve good dynamics. Moreover, a robust control system is developed based on the principle of computed torque control. In the robust control system, a grey uncertainty predictor is utilized to adapt the lumped uncertainty on line to relax the requirement of the lumped uncertainty in the design of a computed torque speed controller. In addition, the effectiveness of the proposed observation and control systems is verified by simulated and experimental results.     

电液力伺服系统的神经网络并行自适应预测 PI 控制

考虑电液伺服系统的复杂非线性和不确定性特性,提出一类基于神经网络的并行自适应预测PI控制结构,该结构使控制参数的调整和系统的实时控制操作可并行进行,不仅做到了神经模型和控制器的在线辨识和设计,而且避免了神经网络方法通常存在的实时控制的困难,使复杂系统的在线学习控制成为可能。仿真结果表明该控制器具有良好的适应性和鲁棒性。      

Incremental motion control of linear synchronous motor

In this study a particular incremental motion control problem, which is specified by the trapezoidal velocity profile using multisegment sliding mode control (MSSMC), is proposed to control a permanent magnet linear synchronous motor (PMLSM) servo drive system. First, the structure and operating principle of the PMLSM are described in detail. Second, a field-oriented control PMLSM servo drive is introduced. Then, each segment of the multisegment switching surfaces is designed to match the corresponding part of the trapezoidal velocity profile, thus the motor dynamics on the specified-segment switching surface have the desired velocity or acceleration corresponding part of the trapezoidal velocity profile. In addition, the proposed control system is implemented in a PC-based computer control system. Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results.     

直升机结构响应自适应控制研究

研究了基于自适应滤波技术、采用伺服惯性力发生器（SIFG）的直升机结构响应主动控制;比较了被动动力吸振器和SIFG的减振效果。研究表明,SIFG在直升机前飞速度和旋翼转速及前飞速度改变的情况下均能保持良好的减振效果。     

陀螺稳定平台视轴稳定系统自适应模糊PID控制

 在运动载体上的光电跟踪系统中，需要采用建立在陀螺稳定平台上的视轴稳定控制。分析了平台结构和惯性稳定隔离原理。针对系统机械谐振、力矩耦合及电气参数波动等非线性不确定因素的影响，设计了复合自适应模糊PID控制器。引入自适应调整因子进行控制规则和参数的在线修正，采用复合控制克服模糊控制固有的盲区，实现无差调节。在光电跟踪转台上的实验结果显示该方法能够有效地隔离载体扰动，减小扰动造成的误差，保证视轴对目标的准确瞄准，具有快速的动态响应和较强的抗干扰性。     

RFNN control for PMLSM drive via backstepping technique

A robust fuzzy neural network (RFNN) control system is proposed in this study to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories. First, an ideal feedback linearization control law is designed based on the backstepping technique. Then, a fuzzy neural network (FNN) controller is designed to be the main tracking controller of the proposed RFNN control system to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. Moreover, to relax the requirement for the bound of uncertainty term, which comprises a minimum approximation error, optimal parameter vectors and higher order terms in Taylor series, an adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the simulated and experimental results due to periodic reference trajectories demonstrate that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.     

导弹发射装置随动系统模糊神经PID控制器

在有些系统性能指标要求很高的导弹发射装置的随动系统中,传统的PID控制往往难以满足要求。文章以某型导弹发射装置的随动系统为模型,结合了模糊控制、神经网络及PID等控制算法,设计了模糊神经PID控制器。通过与传统的PID控制器仿真实验对比,可以看出应用模糊神经PID控制器能够有效地提高该随动系统的动态性能和鲁棒稳定性。     

高速开关阀控电液位置伺服系统自适应鲁棒控制

为研究高精度的液压缸位置跟踪控制问题,设计了高速开关阀和换向阀组合控制液压缸的结构方案,并通过实验分析了高速开关阀的静态流量特性。建立了液压缸的连续可微摩擦模型,利用粒子群优化算法对其参数进行辨识。建立了系统的非线性数学模型,基于非连续参数映射和反步法设计了直接自适应鲁棒控制器,通过参数在线自适应调节来更新估计值和鲁棒反馈项支配参数不确定性,实验结果表明:在跟踪幅值为5mm,频率为0.4Hz的正弦信号时,最后一个周期的最大跟踪误差、平均跟踪误差及其标准差分别为0.638、0.25mm和0.405mm,与传统PID控制器相比,控制精度显著提升,旨在为实现高精度的数字阀控位置伺服技术提供有价值的参考。      

角伺服系统模糊自适应PID控制研究

将模糊控制和PID控制相结合,提出了一种智能复合控制策略,并将其应用于某型导弹系统角伺服系统的控制。利用模糊控制在线自适应调整PID控制器的参数,从而使系统的静态和动态性能指标较为理想。在Simulink中的仿真结果表明,这种模糊PID控制器的控制效果优于单纯的PID控制,超调量小,抗干扰性能强,对变参数系统的鲁棒性强,满足在线实时自适应控制的要求。     

应用保护映射理论的高超声速飞行器自适应控制律设计

针对高超声速飞行器包线范围广、参数变化大的控制需求,应用保护映射理论提出一种高超声速飞行器的自适应控制律设计方法。首先建立整个飞行包线内的线性变参数(LPV)模型,在参数变化边界点设计一个初始的控制结构和参数,然后基于保护映射理论分析初始控制结构使闭环系统稳定的参数范围,通过迭代自动获取整个包线内满足性能指标的控制参数,进而通过多项式拟合设计出高超声速飞行器自适应控制律。所提出的方法能够根据初始控制结构自动寻找一系列满足性能要求的控制器参数,并确定这些控制参数满足闭环系统稳定的设计范围。仿真结果表明,所设计的自适应控制律能够确保高超声速飞行器大包线的设计要求,实现闭环系统的鲁棒稳定。     

Adaptive Fuzzy Torque Control of Passive Torque Servo Systems Based on Small Gain Theorem and Input-to-state Stability

Passive torque servo system (PTSS) simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control (AFTC) algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno (T-S) fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator (ELS), and the comparative experimental results indicate that AFTC controller is effective for PTSS.     

基于扩张状态观测器的DGMSCMG框架伺服系统振动抑制方法

针对带有谐波减速器的双框架磁悬浮控制力矩陀螺(DGMSCMG)框架系统存在较低频率谐振点的问题,提出了一种基于扩张状态观测器(ESO)振动抑制的控制方法。在谐波减速器的输入端和输出端分别设计三阶扩张状态观测器,通过电机及负载端的角位置信号不但能够估计电机和负载的角速率,而且可以比较准确地估计到扭转刚度的非线性变化及外部扰动力矩引起的综合扰动,通过反馈及前馈补偿对综合扰动进行抑制。设计的基于扩张状态观测器的控制器抑制了框架伺服系统的振动、提高了框架的速率输出精度。仿真及实验结果表明了该控制算法是有效的并具有较强的鲁棒性。     

大粘滞摩擦因数的永磁伺服系统自适应反步控制

对于粘滞摩擦系数大的永磁伺服系统,传统的自适应反步控制会导致参数辨识结果波动较大、不易稳定、转速跟踪性能较差。设计了一种实时辨识粘滞摩擦因数的自适应反步控制器,同时对转动惯量、负载转矩和粘滞摩擦因数进行辨识,提高了对粘滞摩擦因数较大系统的转速跟踪性能和转动惯量及负载转矩辨识精度。所提方法结构简单、易于实现,在dSPACE公司的DS1103系统试验平台上对其进行了试验验证,试验结果表明了所提方法的正确性和有效性。     

无刷直流电机网络伺服控制技术研究

针对直流电机网络控制的问题,考虑反馈通道和前向通道均存在延时的状况,设计了直流电机网络伺服控制器。该控制器采用基于模糊自适应调节的广义预测控制算法。模糊自适应调节方法对广义预测控制算法的输出进行调节,从而克服系统参数的不确定性对控制性能的影响。仿真实验结果表明该控制方案能够减小网络延时对系统控制特性的影响,改善系统的动态特性。     

Modified robust optimal adaptive control for flight environment simulation system with heat transfer uncertainty

To solve the rapid transient control problem of Flight Environment Simulation System (FESS) of Altitude Ground Test Facilities (AGTF) with large heat transfer uncertainty and disturbance, a new adaptive control structure of modified robust optimal adaptive control is presented. The mathematic modeling of FESS is given and the influence of heat transfer is analyzed through energy view. To consider the influence of heat transfer in controller design, we introduce a matched uncertainty that represents heat transfer influence in the linearized system of FESS. Based on this linear system, we deduce the design of modified robust optimal adaptive control law in a general way. Meanwhile, the robust stability of the modified robust optimal adaptive control law is proved through using Lyapunov stability theory. Then, a typical aero-engine test condition with Mach Dash and Zoom-Climb is used to verify the effectiveness of the devised adaptive controller. The simulation results show that the designed controller has servo tracking and disturbance rejection performance under heat transfer uncertainty and disturbance; the relative steady-state and dynamic errors of pressure and temperature are both smaller than 1% and 0.2% respectively. Furthermore, the influence of the modification parameter γ is analyzed through simulation. Finally, comparing with the standard ideal model reference adaptive controller, the modified robust optimal adaptive controller obviously provides better control performance than the ideal model reference adaptive controller does.     

机械手的神经网络自适应滑动模控制器设计

 针对多关节机械手的鲁棒跟随控制器设计问题，提出了一种新的机械手神经网络自适应滑动模控制器设计方法，机械手的动力学非线性假设是完全未知的。在提出的控制结构中，高斯径向基函数神经网络用于在线补偿机械手的动力学非线性，参数学习律由稳定性理论得到。给出了系统稳定性和参数收敛性的证明。最后提出方法的可行性通过仿真得到验证。