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.     

Adaptive recurrent-neural-network control for linear inductionmotor

In this study an adaptive recurrent-neural-network controller (ARNNC) is proposed to control a linear induction motor (LIM) servo drive. First, the secondary flux of the LIM is estimated with an adaptive flux observer on the stationary reference frame and the feedback linearization theory is used to decouple the thrust force and the flux amplitude of the LIM. Then, an ARNNC is proposed to control the mover of the LIM for periodic motion. In the proposed controller, the LIM servo drive system is identified by a recurrent-neural-network identifier (RNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the RNNI on line. Moreover, to guarantee the convergence of identification and tracking errors, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RNNI and the optimal learning rate of the adaptive controller. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results. Furthermore, the advantages of the proposed control system are indicated in comparison with the sliding mode control system     

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.     

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 controller design for a linear motor control system

Three different adaptive controllers for a permanent magnet linear synchronous motor (PMLSM) position-control system are proposed. The proposed controllers include: a backstepping adaptive controller, a self-tuning adaptive controller, and a model reference adaptive controller. The detailed systematic controller design procedures are discussed. A PC-based position control system is implemented. Several experimental results including transient responses, load disturbance responses, and tracking responses of square-wave, sinusoidal-wave, and triangular-wave commands are discussed and compared. The proposed system has a good robustness performance even though the inertia of the system is increased to 10 times. The experimental results validate the theoretical analysis.     

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。      

永磁直线同步电机无传感驱动系统全速范围位置估计新方法

为了适应直线电机速度变化范围大的特点,针对永磁直线同步电机(PMLSM)无传感驱动系统中难以在全速范围内精确提取动子位置信息这一问题,提出了一种基于扩展卡尔曼滤波法(EKF)和位置闭环观测器的复合新型位置估计算法。在电机起动与低速时采用EKF,在中高速时采用位置闭环观测器,在速度承接区域采用EKF和闭环观测器算法的加权复合,以实现PMLSM从起动到高速全速范围内高精度的位置估计。仿真试验结果表明,提出的方法在全速范围内能较准确地估计出电机的位置信息。     

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。      

直线电机位置伺服系统的增强复合非线性控制

针对高性能机电系统中常用的直线伺服电机,设计了一个能实现快速与准确的定点运动的位置控制器。控制器采用线性控制律与平滑非线性控制律相结合的方案,并利用一个降阶线性状态观测器对电机运动速度(未量测)加以估计。为了消除未知扰动带来的稳态误差,控制律中嵌入了积分控制作用。整个控制律采用全参数化设计,可实现动态增益控制,方便了在线参数整定与性能优化。控制方案应用于一个实际的永磁直线电机位置伺服系统,基于TMS320F28335 DSC进行了试验测试,结果表明系统能对各目标位置进行准确的跟踪,且具有理想的瞬态性能。     

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.     

基于名义模型的飞行模拟转台反演滑模控制

针对飞行模拟转台这一实际的不确定伺服系统,提出一种新型控制策略,该控制策略是建立在名义模型基础上的一种新型全鲁棒滑模控制器.控制系统由两种控制器构成,一种是针对实际对象的全鲁棒滑模控制器,另一种是针对名义模型的积分反演滑模控制器.采用名义模型与实际对象之间的建模误差设计全鲁棒滑模控制器,采用积分反演滑模控制器来保证控制精度,全鲁棒性能由全局滑模控制器来保证.采用Lyapunov方法实现了两种控制器的稳定性分析.以飞行模拟转台伺服系统为被控对象,针对正弦和阶跃响应的仿真结果表明,采用所提出的控制方法,可实现全局鲁棒性并保证较高的位置跟踪精度.     

Robust wavelet neuro control for linear brushless motors

Design, simulation and experimental implementation of a wavelet basis function network learning controller for linear brushless dc motors (LBDCM) are considered. Stability robustness with position tracking is the primary concern. The proposed controller deals mainly with external disturbances, e.g. nonlinear friction force and payload variation in motion control of linear motors. It consists of two parts, one is a state feedback component, and the other one is a learning feedback component. The state feedback controller is designed on the basis of a simple linear model, and the learning feedback component is a wavelet neural controller. The attenuation effect of wavelet neural networks on friction force is first verified by the numerical method. The learning effect of wavelet neural networks on friction force is also shown in the numerical results. Then, a wavelet neural network is applied on a real LBDCM to on-line suppress the friction force, which may be variable due to the different lubrication. The effectiveness of the proposed control schemes is demonstrated by simulated and experimental results.     

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     

Design and implementation for a digital synchronous reluctancedrive

A systematic controller design for a synchronous reluctance drive system is presented. This controller consists of two parts: a forward-loop H^∞ controller to improve the transient response, and a load compensator to reduce the load disturbance. Based on a simplified model of the drive system, a control algorithm has been derived. Detailed analysis of the characteristics of the closed-loop system is presented. The effects of the parameter variations are also studied. A digital signal processor, TMS-320-C30, is used to implement the control algorithm. Both the speed control and the position control of the drive system can be implemented by using the proposed control method. Furthermore, all the control loops are executed by the digital signal processor. The system, as a result, is very flexible. The whole drive system performs well although its hardware is very simple. For speed control, the system can be operated at a speed as low as 1 r/min. For position control, the system can accurately control a one-axis table. In addition, the system also has good position tracking ability. Several experimental waveforms validate the simulated results     

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.     

火箭弹电动舵机模糊单神经元PID控制算法

针对制导火箭弹电动式舵机,为提高其响应速度和精度,文章在模糊PID控制基础上,提出了模糊单神经元PID控制方法。首先,建立了火箭弹舵机系统模型;然后,将模糊推理、单神经元自学习算法和PID控制相结合,建立智能控制系统,以实现对舵机输入指令的精确、快速响应。经仿真实验表明:在此智能控制下,舵机位置在阶跃响应的调节时间、超调量以及正弦跟踪上相对于传统模糊PID控制均得到有效改进,具有良好的动静态性能、自适应性和稳定性。     

飞行模拟转台高精度数字重复控制器的设计

从工程的角度讨论了离散重复控制系统的设计,所提出的重复控制方法可保证速度跟踪误差快速收敛为零。在重复控制器中采用了高阶低通滤波器和动态补偿器,改善了跟踪精度,保证了系统的稳定性,减少了周期性扰动误差。将所提出的方法应用于飞行模拟转台伺服系统的速度控制中,仿真结果表明,针对周期指令信号和周期干扰信号可保证较高的跟踪精度和较强的稳定性和鲁棒性。     

永磁直线同步电机超短反馈滑模控制研究

永磁直线同步电机矢量控制的速度环常采用PI控制。传统PI控制器在速度追踪时存在起动过程超调大、受到负载扰动时调节时间长等不足之处。为提高伺服性能,滑模控制被应用到伺服系统中,但其缺点是存在抖振问题。因此,提出一种基于内分泌激素调节的滑模控制方法,可以有效降低速度的超调量,缩短受到外部扰动时的调节时间。在MATLAB中搭建了系统的仿真模型,仿真结果表明其对速度和推力的控制效果显著,优于传统的PI控制和常规的滑模控制。     

基于CMAC的伺服系统控制研究

针对高精度伺服系统中存在的非线性和各种不确定性因素,提出了基于小脑模型神经网络的复合控制方法,控制器由前馈控制器、比例微分控制器(PD)和小脑模型神经网络控制器(CMAC)构成,该方法在传统的PD+前馈控制方法上加入了CMAC神经网络算法的快速学习,精确逼近的优点,既保证了快速实时跟踪,又进一步提高了跟踪精度。实验结果证明,用CMAC控制方法后系统的跟踪精度比PD＋前馈控制方法提高近一个数量级,同时该方法对摩擦引起的波形畸变有很好的抑制作用,仿真和实验研究表明了该方法的可行性和有效性,并能满足实时性要求,对提高伺服系统的高精度动态跟踪性能有很好的工程参考价值。     

Quantitative speed control for SRM drive using fuzzy adapted inverse model

Quantitative and robust speed control for a switched reluctance motor (SRM) drive is considered to be rather difficult and challenging owing to its highly nonlinear dynamic behavior. A speed control scheme having two-degree-of-freedom (2DOF) structure is developed here to improve the speed dynamic response of an SRM drive. In the proposed control scheme, the feedback controller is quantitatively designed to meet the desired regulation control requirements first. Then a reference model and a command feedforward controller based on an inverse plant model are employed to yield the desired tracking response at nominal case. As the variations of system parameters and operating conditions occur, the prescribed control specifications may not be satisfied any more. To improve this, the inverse model is adaptively tuned by a fuzzy control scheme so that the model-following tracking error is significantly reduced. In addition, a simple disturbance cancellation robust controller is added to improve the tracking and regulation control performances further.