Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive

In this study, a recurrent fuzzy neural network (RFNN) controller is proposed to control a piezoelectric ceramic linear ultrasonic motor (LUSM) drive system to track periodic reference trajectories with robust control performance. First, the structure and operating principle of the LUSM are described in detail. Second, because the dynamic characteristics of the LUSM are nonlinear and the precise dynamic model is difficult to obtain, a RFNN is proposed to control the position of the moving table of the LUSM to achieve high precision position control with robustness. The back propagation algorithm is used to train the RFNN on-line. Moreover, to guarantee the convergence of tracking error for periodic commands tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Then, the RFNN is implemented in a PC-based computer control system, and the LUSM is driven by a unipolar switching full bridge voltage source inverter using LC resonant technique. Finally, the effectiveness of the RFNN-controlled LUSM drive system is demonstrated by some experimental results. Accurate tracking response and superior dynamic performance can be obtained because of the powerful on-line learning capability of the RFNN controller. Furthermore, the RFNN control system is robust with regard to parameter variations and external disturbances     

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.     

FPGA-based elman neural network control system for linear ultrasonic motor

A field-programmable gate array (FPGA)-based Elman neural network (ENN) control system is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principle of the LUSM are introduced. Because the dynamic characteristics and motor parameters of the LUSM are nonlinear and time-varying, an ENN control system is designed to achieve precision position control. The network structure and online learning algorithm using delta adaptation law of the ENN are described in detail. Then, a piecewise continuous function is adopted to replace the sigmoid function in the hidden layer of the ENN to facilitate hardware implementation. In addition, an FPGA chip is adopted to implement the developed control algorithm for possible low-cost and high-performance industrial applications. The effectiveness of the proposed control scheme is verified by some experimental results.     

Robust control of linear ceramic motor drive with LLCC resonant technique

This study presents a robust control system for a linear ceramic motor (LCM) that is driven by a high-frequency voltage source inverter using two-inductance two-capacitance (LLCC) resonant technique. The structure and driving principle of the LCM are introduced. Because the dynamic characteristics and motor parameters of the LCM are nonlinear and time varying, a robust control system is designed based on the hypothetical dynamic model to achieve high-precision position control. The presentation of robust control for the LCM drive system is divided into three parts, which comprise state feedback controller, feed-forward controller, and uncertainty controller. The adaptation laws of control gains in the robust control system are derived in the sense of Lyapunov stability theorem such that the stability of the control system can be guaranteed. It not only has the learning ability similar to intelligent control, but also its control framework is more simple than intelligent control. With the proposed robust control system, the controlled LCM drive possesses the advantages of good tracking control performance and robustness to uncertainties. The effectiveness of the proposed robust control system is verified by experimental results in the presence of uncertainties. In addition, the advantages of the proposed control system are indicated in comparison with the traditional integral-proportional (IP) position control system.     

An adaptive fuzzy-neural-network controller for ultrasonic motor drive using the LLCC resonant technique

In this study an adaptive fuzzy-neural-network controller (AFNNC) is proposed to control a rotary traveling wave-type ultrasonic motor (USM) drive system. The USM is derived by a newly designed, high frequency, two-phase voltage source inverter using two inductances and two capacitances (LLCC) resonant technique. Then, because the dynamic characteristics of the USM are complicated and the motor parameters are time varying, an AFNNC is proposed to control the rotor position of the USM. In the proposed controller, the USM drive system is identified by a fuzzy-neural-network identifier (FNNI) to provide the sensitivity information of the drive system to an adaptive controller. The backpropagation algorithm is used to train the FNNI 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 FNNI and the optimal learning rate of the adaptive controller. In addition, the effectiveness of the adaptive fuzzy-neural-network (AFNN) controlled USM drive system is demonstrated by some experimental results.     

基于工况识别的IWM-EV主动悬架MOPSO模糊滑模控制

轮毂电机电动汽车(in-wheel motor electric vehicle,IWM-EV)的电机激励与车辆系统的耦合特性严重的恶化车辆的动力学性能以及电机的工作稳定性,针对这种振动负效应问题,建立了考虑机电耦合的车辆动力学耦合模型,并设计了工况识别的主动悬架多目标粒子群(multi-objective particle swarm optimization,MOPSO)模糊滑模控制器。基于傅里叶级数法建立了轮毂电机的垂向不平衡激励与电机转矩的电机模型;将电机模型与车辆动力学模型结合建立了电机与悬架联合的垂向-驱动非线性动力学耦合模型。基于耦合模型分析了车辆的机电耦合振动负效应特性,针对模型强非线性的特点,设计了耦合模型的非线性控制器。仿真结果表明,控制器能既能有效的减小电机的相对偏心率,抑制电机不平衡电磁力,又能提升车辆动力学性能,有效的抑制了轮毂电机电动汽车的振动负效应。     

基于无速度传感器直接转矩控制自抗扰交流调速研究

在感应电机直接转矩控制(DYC)调速系统中,常规PID速度调节器在电机受到扰动的情况下,需要花费较长时间才能使电机恢复到稳态值.为此,将一种新型的自抗扰控制器(ADRC)引入感应电机直接转矩控制调速系统中,设计速度ADRc调节器代替PID调节器,基于模型参考自适应控制(MRAS)方法设计速度观测器.对比分析了PID与ADRC两种方案下无速度传感器直接转矩控制交流调速系统性能.仿真试验结果表明,采用ADRC后,系统动态响应更快,抗扰动能力更强,在电机参数摄动的情况下,电机运行速度与指令速度偏差更小.     

离心机的自适应鲁棒控制系统

为了改善离心机的控制性能和稳定性，进行了离心机的自适应鲁棒控制系统研究；基于电动机与离心机相连的结构，设计了控制器以期得到满意的控制性能；首先提出了一种基于自适应鲁棒控制器的离心机控制系统，针对离心机模型设计了自适应鲁棒控制算法；试验结果证明了该控制算法的有效性；在环境条件不同的情况下，离心机控制系统仍表现了满意的控制性能。     

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.     

基于T-S模糊模型的主动悬架滑模容错控制器设计

针对主动悬架系统的质量参数不确定性以及作动器出现的随机故障对车辆行驶平顺性和控制稳定性带来的重要影响,该文提出一种基于T-S模糊模型的主动悬架滑模容错控制器设计方法。为了描述悬架参数不确定性,基于T-S模糊模型建立1/4车辆的非线性模型,利用故障调节因子表示作动器故障的大小,进而获得考虑悬架系统质量不确定性和作动器故障的车辆主动悬架控制模型。接着,将滑模控制与自适应理论结合,设计合适的滑模面函数和滑模容错控制律,以达到故障悬架系统的容错控制目的;并基于Lyapunov稳定性理论,对所提出控制器稳定性和悬架系统安全约束性能进行了分析。最后,给出一个仿真算例,验证了所设计控制器的有效性和适用性。     

航天器姿态机动的自适应鲁棒控制及主动振动抑制

针对航天器在进行姿态机动时挠性附件的主动振动控制问题,提出一种基于自适应鲁棒方法和 理论相结合的控制方案。为有效地进行振动抑制,主动振动控制器采用 状态反馈理论,并且设计时充分考虑由于忽略挠性附件模型高阶模态所带来的结构不确定性,保证振动的快速衰减和方法的鲁棒性。同时,采用自适应鲁棒方法设计姿态控制器,有效地降低干扰和转动惯量不确定性对系统性能的影响,并采用Lyapunov方法分析系统的稳定性。最后,数字仿真结果说明,本文提出的方法是合理和有效的。     

Adaptive controller design for a synchronous reluctance motor drive system with direct torque control

The design and implementation of adaptive controllers for a sensorless synchronous reluctance drive system with direct torque control is proposed. Two adaptive control algorithms, which include adaptive backstepping control and model-reference adaptive control, are proposed to improve the performance of a sensorless direct torque control synchronous reluctance motor drive system. A digital signal processor, TMS320-C30, is used to execute the rotor position estimating technique and the adaptive control algorithms. The system shows good transient responses, good load disturbance responses and good tracking responses. Several experimental results validate the theoretical analysis. The advanced controller design for a sensorless synchronous reluctance motor drive with direct torque control is proposed.     

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.     

Design and implementation of an adaptive inverse controller for a micro-permanent magnet synchronous motor control system

An adaptive inverse controller design for a micro-permanent magnet synchronous motor control system is proposed. The adaptive inverse controller is constructed by using an adaptive model and an adaptive controller. The parameters of the adaptive model and adaptive controller are on-line tuned. By using the proposed adaptive inverse controller, the transient responses, load disturbance responses and tracking responses of the control system are improved. To detect the shaft rotor position, a micro-encoder is attached with the micro-permanent magnet synchronous motor. The micro-encoder provides only 100 pulses/revolution because of its space limitation. As a result, the resolution of the position signal and speed signal is not good enough. In order to improve the resolution, a state estimator is proposed here. By using the proposed state estimator, the control system can be operated from 1 to 25 000 r/min. The adaptive inverse control algorithm and the state estimation algorithm are executed by a digital signal processor, TMS320F28335. In addition, the proposed adaptive inverse control algorithm can be applied to the position control for the micro-permanent magnet synchronous motor as well. Several experimental results validate the theoretical analysis. The experimental results show that the proposed system has good performance including transient responses, load disturbance responses, and tracking responses.     

基于模糊逻辑的异步电动机转速控制

本文设计了基于模糊逻辑控制的速度控制器,以提高异步电动机矢量控制系统对参数变化和负载扰动的鲁棒性,并通过MABLAB/SIMULIINK仿真将其与PI控制的系统速度响应进行比较,仿真结果表明模糊控制能使系统取得较好的控制性能并具有较强的鲁棒性.     

Novel algebraic meal disturbance estimation based adaptive robust control design for blood glucose regulation in type 1 diabetes patients

This study designs a robust closed‐loop control algorithm for elevated blood glucose level stabilisation in type 1 diabetic patients. The control algorithm is based on a novel control action resulting from integrating algebraic meal disturbance estimator with back‐stepping integral sliding mode control (BISMC) technique. The estimator shows finite time convergence leading to accurate and fast estimation of meal disturbance. Moreover, compensation of the estimated disturbance in controller provides significant reduction in chattering phenomenon, which is inherent drawback of sliding mode control (SMC). The controller is applied to one of the most reliable models of type 1 diabetic patients, named Bergman''s minimal model. The effectiveness and superiority of the designed controller is shown by comparing it to classical SMC and super‐twisting sliding mode control. The designed controller is subject to three different cases for detailed analysis of the controller''s robustness against meal disturbance. The three cases considered are hyperglycaemia, hyperglycaemia combined with meal disturbance and three meal disturbance. The simulation results confirm superior performance of algebraic disturbance estimator based BISMC controller for all the cases mentioned above.Inspec keywords: closed loop systems, robust control, sugar, medical control systems, variable structure systems, control system synthesis, blood, nonlinear control systems, adaptive control, diseasesOther keywords: adaptive robust control design, blood glucose regulation, type 1 diabetes patients, closed‐loop control algorithm, elevated blood glucose level stabilisation, type 1 diabetic patients, novel control action, algebraic meal disturbance estimator, mode control technique, accurate estimation, estimated disturbance, super‐twisting sliding mode control, algebraic disturbance estimator, BISMC controller, algebraic meal disturbance estimation, back‐stepping integral sliding mode control technique     

直升机结构响应混合鲁棒控制研究

为解决满足控制系统的自适应性与在线计算量小的要求,本文提出了多输入、多输出直升机结构响应混合鲁棒控制决策,首先利用混合灵敏度设计方法设计出鲁棒反馈控制器,在此基础上引入基于FXLMS方法设计的自适应前馈控制器,形成自适应前馈-鲁棒反馈的混合控制,设计控制器的原始参数取自离线计算与识别,将计算工作量放在设计阶段,而只需很小的在线调整前馈控制器的计算量。进行了直升机结构模型的鲁棒控制仿真计算,与自由-自由梁的鲁棒控制与混合鲁棒控制的试验研究。结果表明本文方法的可行性与有效性。     

Recurrent neural network control for LCC-resonant ultrasonic motor drive

A newly designed driving circuit for the traveling wave-type ultrasonic motor (USM), which consists of a push-pull DC-DC power converter and a two-phase voltage source inverter using one inductance and two capacitances (LCC) resonant technique, is presented in this study. Moreover, because the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, a recurrent neural network (RNN) controller is proposed to control the USM drive system. In the proposed controller, the dynamic backpropagation algorithm is adopted to train the RNN on-line using the proposed delta adaptation law. Furthermore, to guarantee the convergence of tracking error, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates for the training of the RNN. Finally, the effectiveness of the RNN-controlled USM drive system is demonstrated by some experimental results.     

车辆主动悬架的非线性路面自适应控制研究

针对不同的路面状况,提出一种车辆主动悬架的非线性路面自适应控制方法。采用增加高低通非线性滤波器的方法,对以车身垂直加速度和悬架动行程为目标的控制函数进行优化处理,并利用多滑模鲁棒控制方法,设计了一种主动悬架的非线性路面自适应控制器。进行了零动力学子系统的稳定性分析及系统频率特性分析,理论分析表明整个系统是渐进稳定的。仿真结果显示,在不同的路面激励信号作用下,都能取得较好的控制效果,与被动悬架相比,大大改善了乘座的舒适性及车辆的操纵性能。     

应用于圆饼工件分拣的码垛机器人自适应积分滑模控制

目的 针对存在系统未建模特性和负载变化下码垛机器人关节空间轨迹跟踪控制的问题,设计一种基于结合时延估计技术与自适应积分滑模面的控制策略。方法 根据圆饼工件分拣需求,设计一款桌面式码垛机器人系统,推导机器人的运动学与动力学模型,给出关节空间轨迹规划算法,并基于无模型思想设计关节空间轨迹跟踪控制器。结果 利用雅克比伪逆法可反解出机器人的关节角;通过所提的轨迹规划算法能有效获得各关节运动轨迹;与PID控制器和积分滑模控制器相比,文中所提控制器具有较好的控制精度、较强抗干扰性和较高的鲁棒性。结论 仿真和实验结果表明,所设计的基于时延估计技术的自适应积分滑模控制器是合理的,能使得码垛机器人完成圆饼工件的分拣任务,具有一定的工程应用价值。