Intelligent Adaptive Backstepping Control System for Magnetic Levitation Apparatus

We propose an intelligent adaptive backstepping control system using a recurrent neural network (RNN) to control the mover position of a magnetic levitation apparatus to compensate for uncertainties, including friction force. First, we derive a dynamic model of the magnetic levitation apparatus. Then, we suggest an adaptive backstepping approach to compensate disturbances, including the friction force, occurring in the motion control system. To further increase the robustness of the magnetic levitation apparatus, we propose an RNN estimator for the required lumped uncertainty in the adaptive backstepping control system. We further propose an online parameter training methodology, derived by the gradient descent method, to increase the learning capability of the RNN. The effectiveness of the proposed control scheme has been verified by experiment. With the proposed adaptive backstepping control system using RNN, the mover position of the magnetic levitation apparatus possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic trajectories     

Recurrent Radial Basis Function Network-Based Fuzzy Neural Network Control for Permanent-Magnet Linear Synchronous Motor Servo Drive

We propose a recurrent radial basis function network-based (RBFN-based) fuzzy neural network (FNN) to control the position of the mover of a field-oriented control permanent-magnet linear synchronous motor (PMLSM) to track periodic reference trajectories. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, it performs the structureand parameter-learning phases concurrently. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient descent method, using a delta adaptation law. Furthermore, all the control algorithms are implemented in a TMS320C32 DSP-based control computer. The simulated and experimental results due to periodic reference trajectories show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties     

径向网络裹包机凸轮定位差动器交流控制的非线性观察器

根据裹包机的交流控制系统控制精度较差的问题,提出采用直接转矩控制方法,但速度控制受到系统固有不确定的影响,采用RBFN不确定观察器的鲁棒机定速度控制器,建立控制输入更新权值和约束常数的自适应规律,仿真和实验结果表明该算法是可行的和有意义的.     

An adaptive recurrent radial basis function network tracking controller for a two-dimensional piezo-positioning stage

An adaptive recurrent radial basis function network (ARRBFN) tracking controller for a two-dimensional piezo-positioning stage is proposed in this study. First, a mathematical model that represents the dynamics of the two-dimensional piezo-positioning stage is proposed. In this model, a hysteresis friction force that describes the hysteresis behavior of one-dimensional motion is used; and a nonconstant stiffness with the cross-coupling dynamic due to the effect of bending of a lever mechanism in x and y axes also is included. Then, according to the proposed mathematical model, an ARRBFN tracking controller is proposed. In the proposed ARRBFN control system, a recurrent radial basis function network (RRBFN) with accurate approximation capability is used to approximate an unknown dynamic function. The adaptive learning algorithms that can learn the parameters of the RRBFN on line are derived using Lyapunov stability theorem. Moreover, a robust compensator is proposed to confront the uncertainties, including approximation error, optimal parameter vectors, higher-order terms in Taylor series. To relax the requirement of the value of the lumped uncertainty in the robust compensator, an adaptive law is investigated to estimate the lumped uncertainty. Using the proposed control scheme, the position tracking performance is substantially improved and the robustness to uncertainties, including hysteresis friction force and cross-coupling stiffness, can be obtained as well. The tracking performance and the robustness to external load of the proposed ARRBFN control system are illustrated by some experimental results.     

电液伺服系统的逆向递推鲁棒自适应控制

针对电液伺服系统存在的非线性特性、参数不确定性,且不确定性不满足匹配条件,引入虚拟控制量的概念,提出了一种逆向递推鲁棒自适应控制方法。该方法把整个系统分成了一个二阶和一个一阶两个子系统,对其进行递推式的分步自适应控制,从而简化了控制器的设计,使计算量大为减少;利用参数自适应和鲁棒控制相结合的方法,使控制器具有较强的抗干扰性。仿真结果显示,该控制方法具有较强的鲁棒性及良好的跟踪性能,与采用 PID 的控制方法相比,系统具有更好的控制性能及更强的抗干扰性。     

Adaptive control with hysteresis estimation and compensation using RFNN for piezo-actuator

Because the control performance of a piezoactuator is always severely deteriorated due to hysteresis effect, an adaptive control with hysteresis estimation and compensation using recurrent fuzzy neural network (RFNN) is proposed in this study to improve the control performance of the piezo-actuator. A new hysteresis model by modifying and parameterizing the hysteresis friction model is proposed. Then, the overall dynamics of the piezo-actuator is completed by integrating the parameterized hysteresis model into a mechanical motion dynamics. Based on this developed dynamics, an adaptive control with hysteresis estimation and compensation is proposed. However, in the designed adaptive controller, the lumped uncertainty E is difficult to obtain in practical application. Therefore, a RFNN is adopted as an uncertainty observer in order to adapt the value of the lumped uncertainty E on line. And, some experimental results show that the proposed controller provides high-performance dynamic characteristics and is robust to the variations of system parameters and external load.     

Hybrid controller with recurrent neural network for magnetic levitation system

We propose a hybrid controller using a recurrent neural network (RNN) to control a levitated object in a magnetic levitation system. We describe a nonlinear dynamic model of the system and propose a computed force controller, based on feedback linearization, to control the position of the levitated object. To relax the requirement of the lumped uncertainty in the design of the computed force controller, an RNN functions as an uncertainty observer to adapt the lumped uncertainty on line. The computed force controller, the RNN uncertainty observer, and a compensated controller are embodied in a hybrid controller, which is based on Lyapunov stability. The computed force controller, with the RNN uncertainty observer, is the main tracking controller, and the compensated controller compensates the minimum approximation error of the RNN uncertainty observer. To ensure the convergence of the RNN, the adaptation law of the RNN is modified by using a projection algorithm. Experimental results illustrate the validity of the proposed control design for the magnetic levitation system.     

不确定电液位置伺服系统的动态面跟踪控制

针对一类含有动态不确定性的双作用液压缸电液伺服系统跟踪控制问题,采用动态面控制方法设计了一个鲁棒自适应跟踪控制器.由于在逆推设计过程中加入了低通滤波器使得该方法不用对模型非线性进行多次微分,因而设计方法简化.所设计的自适应鲁棒控制器不仅能保证闭环系统的半全局渐近稳定,使得输出渐近跟踪期望轨迹;而且,跟踪误差可以通过控制器的设计参数加以调整.数字仿真结果表明,控制系统对给定位置的跟踪具有良好的动态特性,对系统的不确定性,具有较强的鲁棒性.     

高阶不确定系统的自适应跟踪控制

针对一类未知控制增益的不确定非线性系统,提出了一种自适应跟踪控制方法.文中的不确定性包括时变和时不变参数.基于反步设计法,提出了一种新的自适应控制方法.该方法不采用饱和控制,能保证跟踪误差收敛于零.提出了一个仿真例子,仿真结果说明了该方法的有效性.     

Field-programmable gate array-based recurrent wavelet neural network control system for linear ultrasonic motor

A field-programmable gate array (FPGA)-based recurrent wavelet neural network (RWNN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM). First, the structure and operating principles of the LUSM are introduced. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an RWNN controller is designed to improve the control performance for the precision tracking of various reference trajectories. The network structure and its on-line learning algorithm using delta adaptation law of the RWNN are described in detail. Moreover, the connective weights, translations and dilations of the RWNN are trained on-line. Furthermore, to guarantee the convergence of the tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RWNN. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. Finally, the effectiveness of the proposed control system is verified by some experimental results.     

共振式混凝土路面破碎车的自适应反推滑模控制

基于共振式混凝土路面破碎车共振机构的结构,给出了其共振频率和振幅的模型.建立了基于比例泵控马达的频率调节的非线性不确定性控制模型,设计了自适应反推滑模的控制算法,针对系统模型中的不确定性项,给出了各参数项的自适应律,基于Lyapunov函数,证明了频率输出跟踪的渐近收敛.仿真和车载测试结果表明,该方法具有较好的频率跟踪性能,能满足共振式路面破碎车的施工控制要求.     

共振式水泥混凝土路面破碎车共振频率和振幅的控制研究

基于共振式水泥混凝土路面破碎车共振机构的载荷分析,给出了其共振频率和振幅的模型。针对共振机构电液比例控制系统的非线性和不确定性问题,建立了基于比例泵控马达的频率控制数学模型,设计了自适应反推滑模的频率控制算法,针对系统模型中的不确定项,给出了各参数项的自适应律,基于Lyapunov函数,证明了频率输出跟踪的渐近收敛。仿真和车载实验结果表明,该方法具有较好地频率跟踪性能,能满足共振式水泥混凝土路面破碎车的施工作业要求。     

基于干扰观测器的波浪升沉模拟及自适应补偿策略研究

海洋装备的快速升级使得海上吊装应用广泛,但吊装设备易受风浪影响,导致系统控制精度降低。为提高海上吊装设备的控制精度,提出了基于干扰观测器的波浪升沉自适应反步补偿策略。以波浪升沉补偿系统为研究对象,对三级海况下的船舶升沉运动轨迹及其电液提升系统的非线性模型进行推导;利用波浪模拟平台模拟船舶的升沉运动,采用基于干扰观测器的自适应反步补偿策略对电液提升系统的非线性误差进行抑制;通过Lyapnov判据验证自适应反步补偿策略的稳定性,并通过仿真和试验对控制器性能进行验证。试验结果表明,相较于传统的PID（proportion integration differentiation,比例积分微分）控制策略,基于干扰观测器的波浪升沉自适应反步补偿策略具有更好的控制效果。对于海上吊装设备的控制系统,基于干扰观测器的自适应反步补偿策略可有效地抑制外界干扰及系统非线性干扰对控制器的影响,提高对海上吊装设备升沉运动的位置补偿精度。     

永磁同步电动机位置伺服系统的自适应神经网络控制

针对需要考虑参数不确定和负载扰动的永磁同步电动机位置伺服系统,提出了一种新型的自适应神经网络控制方法。首先,利用神经网络建立永磁同步电动机的智能模型。其次,针对模型特点,在反步递推设计框架下,应用神经网络基函数的本质特征,并引入动态面控制技术克服控制设计中存在的“复杂性爆炸”问题,设计基于自适应神经网络动态面控制的位置跟踪算法。最后,仿真结果表明该控制方案是有效可行的,与反步递推控制方案相比,基于神经网络动态面控制的位置伺服系统的跟踪误差具有更快的收敛速度。通过设计新的神经网络自适应律,提出的自适应神经网络控制方法可以避免现有反步递推控制设计中存在的代数环问题。此外,提出的控制算法不仅能够克服不确定性因素对系统性能的影响,而且算法结构简单,易于实现。     

液压柔性机械臂运动及振动的鲁棒控制

针对液压柔性机械臂刚体运动和振动控制，本文提出了一种新的鲁棒控制器设计方法。该设计方法结合了反演控制设计方法、滑模控制理论及极点配置技术，对系统不确定性具有较强的鲁棒性。其中滑模控制主要是通过虚拟控制扭矩的设计，来实现在柔性臂端点负载不确定的情况下，对其刚体转角和柔性振动的控制；反演控制设计主要是完成系统的实际输入——液压伺服阀阀芯位移的设计；而极点配置应用于滑模平面极点的设置，获得期望的系统动态响应。基于Lyapunov稳定性理论的系统稳定性分析，证明系统跟踪误差将收敛于有限区域内。仿真实例表明了本文方法的有效性。     

Precise trajectory tracking of a piezoactuator-driven stage using an adaptive backstepping control scheme

In this paper, an adaptive backstepping control scheme is proposed for precise trajectory tracking of a piezoactuator-driven stage. Differential equations consisting of dynamics of a linear motion system and a hysteresis function are investigated first for describing the dynamics of motion of the piezoactuator-driven stage with hysteresis behavior. Then, to identify the uncertain parameters designed in the differential equations, the Powell method of a numerical optimization technique is used. From the differential equations identified, an equivalent state-space model is developed, then a linear state-space model through a state transformation is established. In the linear state-space model, the hysteresis function is approximated by the first three terms of a Taylor series expansion. Based on the linear state-space model, we developed an adaptive backstepping control for the trajectory tracking. By using the proposed control approach to trajectory tracking of the piezoactuator-driven stage, improvements in the tracking performance, steady-state error, and robustness to disturbance can be obtained. To validate the proposed control scheme, a computer-controlled, single-axis piezoactuator-driven stage with a laser displacement interferometer was set up. Experimental results illustrate the feasibility of the proposed control for practical applications in trajectory tracking.     

Wavelet neural network control for linear ultrasonic motor drive via adaptive sliding-mode technique

A wavelet neural network (WNN) control system is proposed to control the moving table of a linear ultrasonic motor (LUSM) drive system to track periodic reference trajectories in this study. The design of the WNN control system is based on an adaptive sliding-mode control technique. The structure and operating principle of the LUSM are introduced, and the driving circuit of the LUSM, which is a voltage source inverter using two-inductance two capacitance (LLCC) resonant technique, is introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time varying, a WNN control system is designed based on adaptive sliding-mode control technique to achieve precision position control. In the WNN control system, a WNN is used to learn the ideal equivalent control law, and a robust controller is designed to meet the sliding condition. Moreover, the adaptive learning algorithms of the WNN and the bound estimation algorithm of the robust controller are derived from the sense of Lyapunov stability analysis. The effectiveness of the proposed WNN control system is verified by some experimental results in the presence of uncertainties.     

A new adaptive control of dual-motor driving servo system with backlash nonlinearity

In order to weaken the influence of backlash nonlinearity on a dual-motor driving servo system, we first establish the state-space model of the system. We then propose a new adaptive controller combining a projection algorithm with backstepping control for the first time, to the best of our knowledge, and analyze its stability. In the simulation analysis, we respectively choose a triangular wave, sawtooth wave, and random signal as the input signal. Simulation results validate a higher tracking accuracy and stronger adaptability of the proposed control law than that of mere backstepping control. In the experimental tests, we respectively choose a step signal and sine signal and simultaneously apply a white noise signal to the system output after 3 s in each test. The test results validate a stronger adaptability and robustness than that of mere backstepping control.     

基于奇异摄动理论的电液伺服系统Backstepping滑模自适应控制

针对电液伺服位置跟踪系统中存在的非线性特性、系统参数和外部负载的非匹配不确定性,提出了基于奇异摄动理论的电液伺服系统的Backstepping滑模自适应控制。利用奇异摄动中双时间刻度理论将原系统分解为快慢变子系统,分别设计快变和慢变子系统的控制律,再合成得到复合控制器。应用Backstepping的逆向递推方法有效地解决了高阶非线性系统的控制问题,用滑模方法抑制系统的外部扰动,对系统的不确定性参数进行自适应估计。数字仿真的结果验证了所设计控制器的正确性和有效性。     

Adaptive backstepping motion control of induction motor drives

Abstract

In this paper, an adaptive backstepping controller is proposed for position tracking of a mechanical system driven by an induction motor. The mechanical system is a single link fixed on the shaft of the induction motor such as a single‐link robot. The backstepping methodology provides a simpler design procedure for an adaptive control scheme and provides a method to define the sliding surface if the robust slidingmode control is applied. Thus, the backstepping control can be easily extended to work as an adaptive sliding‐mode controller. The presented position control system is shown to be stable and robust to parameter variations and external disturbances. The effectiveness of the proposed controllers is demonstrated in experiments.