受扰非线性时滞系统近似最优跟踪控制

为了研究一类参考输入和扰动由外系统描述的非线性时滞系统最优输出跟踪问题,利用逐次逼近法将原最优跟踪问题转化为一族不含时滞项和超前项的非齐次线性两点边值问题序列,通过迭代求解该问题序列,提出了一个伴随向量解序列近似求解过程。最优输出跟踪控制律由解析的前馈-反馈项和以伴随向量的极限形式给出的非线性时滞补偿项组成,通过截取伴随向量序列的有限步迭代值,得到了问题的次优控制律。利用构造扰动和参考输入降维观测器,解决了前馈控制律的物理可实现问题;仿真结果表明,该方法计算量较小,容易工程实施,能有效地解决非线性时滞系统的最优跟踪控制问题。     

基于改进快速幂次趋近律的永磁同步电机滑模控制

针对永磁同步电机(PMSM)滑模控制指数趋近律中趋近速度与抖振之间的矛盾问题,基于快速幂次趋近律提出一种幂次项系数自适应调节的新型趋近律。所提趋近律将系统状态引入幂次项系数中,实现了系统状态由较远处到滑模面附近的趋近过程中加入幂次项的作用,在保证幂次项特点的前提下,动态响应过程的收敛速度大大提高。负载转矩是滑模速度控制器中的一个扰动项,设计了带有幂次项的滑模观测器,将观测值作为转矩前馈补偿。仿真结果表明,与快速幂次趋近律相比,所提趋近律具有更快的收敛速度,负载观测器能准确跟踪负载变化,提升了系统抗扰性能。     

基于改进非奇异快速终端滑模反演控制的风力发电系统最大功率跟踪

针对永磁直驱风力发电机系统的最大功率跟踪存在非线性、系统参数不确定及外部扰动问题,提出一种基于有限时间干扰观测器的非奇异快速终端滑模反演控制策略。针对转速跟踪问题,将系统参数不确定与外部扰动定义为集总扰动,用观测器快速精确估计之后补偿。采用反演控制法处理非线性系统,结合动态面控制设计虚拟控制律,同时设计滑模控制律,提高系统收敛速度与跟踪精度。通过李雅普诺夫理论与仿真实验证明该系统的稳定性与有效性。     

基于神经网络推力观测器的永磁直线电机滑模控制

针对永磁直线同步电机(PMLSM)伺服系统易受到负载扰动、参数变化和推力波动的问题,为确保在较宽的速度范围内实现更为精确的速度控制,采用基于神经网络推力观测器的滑模控制取代常规的PI控制,滑模变结构控制律采用等效控制法,加上负载扰动前馈补偿项,而扰动补偿则是通过线性推力观测器并联一个神经网络观测器相加而得.实验和仿真结果表明,此方法较常规PI控制提高了跟踪性能,增强了伺服系统对参数摄动和外在扰动的稳健性.     

基于新型趋近律的永磁同步电机滑模速度控制

系统惯性和切换开关的时间滞后造成了滑模控制系统的抖振问题。为了解决该问题,提高永磁同步电机调速系统的动态品质,设计了新型趋近律。趋近律引入了系统状态变量绝对值的反正切函数,减小接近滑模面时的速度。用变边界层饱和函数代替常规的符号函数,使滑模面附近沿滑模面的运动更平滑。负载转矩是滑模速度控制器中的一个扰动项,采用以转矩和转速为观测对象的扩展滑模观测器对负载转矩进行观测,将观测值引进滑模速度控制器进行前馈补偿,进一步提高控制系统的抗扰性。仿真结果表明,改进后的趋近律在减小滑模面趋近时间的同时,有效地削弱了系统的抖振。负载转矩观测器在负载阶跃变化时能够进行准确的跟踪。相较于传统的指数趋近律滑模控制,基于新型趋近律的滑模控制响应速度快、无超调、抗扰性强,能够实现永磁同步电机良好的调速动态品质。     

基于改进型扰动观测器的PMSM抗扰动控制研究

负载转矩扰动及转动惯量变化会对永磁同步电机(PMSM)控制造成影响,为提高系统响应速度以及抗干扰能力,减小负载扰动对控制性能的影响,将观测的负载转矩前馈补偿至转矩电流中,改进滑模速度控制器中指数趋近律函数.设计一种改进型扰动观测器,在传统扰动观测器中加入可变增益算法,针对负载扰动引起系统转动惯量变化的问题,结合模型参考...     

基于扰动估计补偿的PMLSM固定时间积分滑模控制

为了提高永磁直线同步电机(PMLSM)控制系统的动态响应速度和鲁棒性,提出了一种基于扰动估计补偿的固定时间积分滑模控制（DFISMC）方法。首先,在积分滑模面的基础上,引入了一种固定时间滑模控制算法,保证系统可以在固定时间内到达平衡点。其次,设计了一种随系统状态动态调整的控制率增益函数f(s),进一步加快了收敛速度。最后,设计自适应超螺旋扰动观测器估计内部和外部不匹配扰动,并采用前馈补偿的方式将观测值引入控制律,增强了系统的抗干扰能力。仿真结果表明,DFISMC方法不仅保证了系统全局鲁棒性,而且有效提高了系统的跟踪精度和响应速度。     

现代控制方法在火电厂控制中的应用

本文探讨了两种现代控制方法.即采用状态观测器的跟踪控制和基于前馈原理的现代频域法,并将其应用于一台200MW单元机组的控制中,还在VAX数字机上进行了两种方案的仿真研究.并做了详细比较.从中得出了一些有参考价值的结论.     

基于趋近律的互补积分终端滑模音圈电机控制

音圈电机（VCM）主要应用于小范围直线定位场景,需保证其拥有高精度及高鲁棒性,并且由于电机参数及摩擦力等影响,VCM是非线性且时变的。本文从VCM工作原理分析并建立对应二阶系统模型。控制策略上采用滑模控制和状态观测器的结合,针对滑模控制的抖振问题分别从滑模面和趋近律方向分别提出对应解决方案。滑模面采用互补积分终端滑模控制（CISMC）,在减小抖振的基础上,采用齐次理论设计的积分终端滑模实现了有限时间的收敛性;趋近律采用幂次趋近律和非线性函数的结合,可以更快的到达滑模面;在滑模控制外采用状态观测器（ESO）对系统扰动等进行前馈补偿,提出了一种基于趋近律的互补积分终端滑模控制（CISMC）。根据推导的数学模型在MATLAB/simulink中建立系统仿真模型,通过仿真实验表明CISMC+ESO的方案,在跟踪正弦信号相较于互补滑模控制（CSMC）+ESO方案,稳定误差最大减少75%,完全跟踪时间最大减小28.5%;在跟踪斜坡信号时,稳态误差明显减少较大,完全跟踪时间最大减小84%。实验表明其在系统控制精度和鲁棒性方面都有明显提高。     

永磁直驱风力发电系统最大功率跟踪非线性抗扰控制

针对永磁同步发电机的非线性、内部参数不确定以及外部扰动等问题,提出了一种直驱式永磁同步风力发电系统最大功率跟踪的非线性抗扰控制方法。该方法使用一种非线性光滑函数来设计非线性扩张状态观测器(NLESO)和非线性抗扰控制律。由NLESO来实现系统扰动及不确定性的估计,前馈到控制输入端对扰动进行补偿,从而有效提高了系统的抗扰能力。分析了NLESO的收敛性。仿真结果表明了该控制方法不仅具有响应速度快、控制精度高的特点,而且无超调无抖振现象,因而在风力发电系统最大功率跟踪控制领域具有较大应用价值。     

Experience replay–based output feedback Q‐learning scheme for optimal output tracking control of discrete‐time linear systems

This paper focuses on solving the adaptive optimal tracking control problem for discrete‐time linear systems with unknown system dynamics using output feedback. A Q‐learning‐based optimal adaptive control scheme is presented to learn the feedback and feedforward control parameters of the optimal tracking control law. The optimal feedback parameters are learned using the proposed output feedback Q‐learning Bellman equation, whereas the estimation of the optimal feedforward control parameters is achieved using an adaptive algorithm that guarantees convergence to zero of the tracking error. The proposed method has the advantage that it is not affected by the exploration noise bias problem and does not require a discounting factor, relieving the two bottlenecks in the past works in achieving stability guarantee and optimal asymptotic tracking. Furthermore, the proposed scheme employs the experience replay technique for data‐driven learning, which is data efficient and relaxes the persistence of excitation requirement in learning the feedback control parameters. It is shown that the learned feedback control parameters converge to the optimal solution of the Riccati equation and the feedforward control parameters converge to the solution of the Sylvester equation. Simulation studies on two practical systems have been carried out to show the effectiveness of the proposed scheme.     

Online optimal and adaptive integral tracking control for varying discrete-time systems using reinforcement learning

Conventional closed-form solution to the optimal control problem using optimal control theory is only available under the assumption that there are known system dynamics/models described as differential equations. Without such models, reinforcement learning (RL) as a candidate technique has been successfully applied to iteratively solve the optimal control problem for unknown or varying systems. For the optimal tracking control problem, existing RL techniques in the literature assume either the use of a predetermined feedforward input for the tracking control, restrictive assumptions on the reference model dynamics, or discounted tracking costs. Furthermore, by using discounted tracking costs, zero steady-state error cannot be guaranteed by the existing RL methods. This article therefore presents an optimal online RL tracking control framework for discrete-time (DT) systems, which does not impose any restrictive assumptions of the existing methods and equally guarantees zero steady-state tracking error. This is achieved by augmenting the original system dynamics with the integral of the error between the reference inputs and the tracked outputs for use in the online RL framework. It is further shown that the resulting value function for the DT linear quadratic tracker using the augmented formulation with integral control is also quadratic. This enables the development of Bellman equations, which use only the system measurements to solve the corresponding DT algebraic Riccati equation and obtain the optimal tracking control inputs online. Two RL strategies are thereafter proposed based on both the value function approximation and the Q-learning along with bounds on excitation for the convergence of the parameter estimates. Simulation case studies show the effectiveness of the proposed approach.     

Adaptive finite-time control for output regulation of nonlinear systems with completely unknown control directions

This article studies the finite-time output regulation problem for nonlinear strict-feedback systems with completely unknown control directions and unknown functions. First, according to the necessary conditions for the solvability of the output regulation problem, the output regulation problem of nonlinear strict-feedback systems and the external system is transformed into a stabilization problem of nonlinear systems. Second, an internal model with external signals is designed. Third, based on finite time, fuzzy control, output feedback control, and Nussbaum gain functions, the control law is designed so that all signals of the closed-loop system are the semi-global practically finite-time stable (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite-time. Finally, the proposed algorithm is applied to the finite-time tracking problem of Chua's oscillator system.     

基于优化设计数字滤波器在伺服控制中的应用

在高精确度伺服控制系统中,通常采用零相位误差跟踪控制器ZPETC作为前馈控制器, 作用是消除相位误差,但是,ZPETC将导致很小的增益误差。为改善ZPETC对系统增益性能的影响,提出一种基于L2范数优化的前馈控制器设计方案,通过选取适当的目标函数,设计出最优的数字滤波器,将此滤波器与ZPETC串联组成新的前馈控制器,此滤波器在保持系统零相位误差的同时,改善了系统的增益性能,提高了跟踪精确度。仿真实验表明,采用本文提出的优化设计方案,能够改善系统的运动跟踪性能。     

基于PI迭代学习的有源滤波器电流跟踪控制

在采用有源电力滤波器消除谐波过程中,由于滤波器的检测精度有限,指令电流计算延时和输出滤波器的相移等因素影响,传统PI控制滤波效果不够理想。本文针对稳定性负载电网谐波具有重复周期性特点,提出了一种基于PI型学习律的迭代学习控制算法,通过实施遗忘因子学习律增强系统鲁棒性的同时,引入了在最优目标选择下模糊参考电流误差的D型学习律前馈环节,提高了系统的跟踪精度。仿真与现场运行结果都证明,该控制方法具有谐波电流跟踪效果好,计算量小,工程易于实现等特点。     

基于粒子滤波前馈补偿的电机伺服系统控制

针对电机轨迹跟踪过程的非线性非高斯噪声，提出采用基于粒子滤波的前馈控制器。同时对电机伺服系统提出一种二自由度控制结构，基于闭环系统的鲁棒最优性能指标，设计给定值状态目标跟踪控制器，根据系统稳态运行时的抗扰动要求，在过程输入和控制对象输入之间设计前馈控制器、在对象输入和输出端之间设计负载干扰抑制闭环，利用粒子滤波的方法消除非高斯噪声对控制系统的干扰。仿真实例验证了该控制系统的可行性和优越性，可以有效提高电机轨迹跟踪精度。     

An optimal control design for bidirectional inductive power transfer system using dynamics identification

Abstract

In this article, we address the tracking control problem for the bidirectional inductive power transfer systems (BIPTS) in the view of optimal performance. To achieve the optimal destination with respect to a quadratic value function, an identified linear model for the BIPTS is first developed, and it is proved to be convergent to the true dynamic model when the persistent excitation condition is satisfied. And then, based on the identified model, the desired system states and control inputs are derived according to the reference transferring power, and be utilized as the feedback signals and feedforward compensation, respectively, for the optimal controller. A hybrid Lyapunov function is developed to guarantee the closed-loop stability and the asymptotically tracking convergence. A numerical simulation is carried out to illustrate the satisfactory control performance.     

Immersion and invariance adaptive control for discrete‐time systems in strict‐feedback form with input delay and disturbances

This work presents a new adaptive control algorithm for a class of discrete‐time systems in strict‐feedback form with input delay and disturbances. The immersion and invariance formulation is used to estimate the disturbances and to compensate the effect of the input delay, resulting in a recursive control law. The stability of the closed‐loop system is studied using Lyapunov functions, and guidelines for tuning the controller parameters are presented. An explicit expression of the control law in the case of multiple simultaneous disturbances is provided for the tracking problem of a pneumatic drive. The effectiveness of the control algorithm is demonstrated with numerical simulations considering disturbances and input‐delay representative of the application.     

Short-Span seeking control of hard disk drives with multirate vibration suppression perfect tracking control

In this paper, a novel vibration suppression perfect tracking control (PTC) method is proposed for short-span seeking mode of hard disk drives (HDDs) based on multirate feedforward control. In the proposed method, it is assumed that plant is modeled as the rigid-body and primary vibration modes. By using this model, a feedforward PTC is designed with a modified controllable canonical realization in order to control the transient response of position, velocity, acceleration, and jerk of the proposed virtual plant. Simulations and experiments are carried out to show that the proposed system can suppress the primary vibration mode in short-span seeking control. Finally, the proposed method is applied to a benchmark problem of HDD control which is made by a technical committee of IEE of Japan. The robustness of the proposed feedforward method is examined against the variation of primary vibration frequency. Copyright © 2009 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.