Buck变换器的改进型互补滑模控制

针对Buck变换器系统中存在匹配和不匹配干扰的问题, 本文提出了一种基于干扰观测器(DOB)的改进型互补滑模控制(CSMC)策略. 首先, 建立存在多重干扰的Buck变换器数学模型, 将模型改写为标准二阶积分型控制对象, 将式中干扰统一为匹配干扰和不匹配干扰. 其次, 设计2个DOB分别估计匹配干扰和不匹配干扰, 实现有限时间内跟踪干扰信号, 以抵消各种不确定性对系统的影响. 然后, 设计互补滑模面, 提出基于等效控制的改进型互补滑模控制律, 保留边界层内鲁棒性的同时, 提升控制器的动态性能, 减小静态误差, 拓宽边界层参数选择范围. 最后, 基于李雅普诺夫理论证明所提出控制器的稳定性. 数字仿真表明, 提出的改进型CSMC控制器结合DOB的总体控制方案能够有效抑制系统匹配和不匹配干扰, 同时获得更快的收敛速度以及更高的跟踪精度.     

具有扰动的非线性系统高阶迭代学习控制

迭代学习控制(ILC)利用系统的重复性不断改进控制性能.本文讨论一类具有扰动的非线性、时变系统高阶迭代学习控制算法及其迭代学习收敛的充分条件,并与D型迭代学习算法相比,讨论典型PD高阶ILC算法的收敛速度.仿真结果证实高阶ILC算法具有更快的收敛速度,并且当系统满足收敛条件、不确定项及输出扰动项有界时迭代学习收敛.     

永磁直线同步电动机智能递归非奇异终端滑模控制

为解决永磁直线同步电动机(PMLSM)在运行过程中易受参数变化、外部扰动、摩擦阻力等不确定性因素 影响的问题, 本文提出一种基于双隐层径向基函数神经网络(DRBFNN)的递归非奇异终端滑模控制(RNTSMC)方法 来提高PMLSM系统的控制性能. 首先, 分别构造非奇异终端滑模面和递归积分终端滑模面, 使得两滑模面依次连续 到达, 可在削弱抖振的同时保证跟踪误差在理论上的有限时间内收敛至零. 但由于系统不确定性的边界难以确定, 因此引入具有更高拟合精度和泛化能力的DRBFNN对不确定性进行逼近和补偿, 并通过在线自适应更新连接权重, 进一步提高神经网络的逼近能力. 最后, 系统实验结果表明, 该方法能够有效抑制不确定性对系统的影响, 提高了系 统的位置跟踪精度, 并使系统具有较强的鲁棒性.     

随机分布控制系统的自适应迭代学习控制

介绍输出概率密度函数(PDF)常规的迭代学习控制(ILC)的收敛条件,并利用此条件设计相应的迭代学习律.主要讨论如何解决输出PDF迭代学习控制(ILC)中的过迭代,收敛速度等问题.以离散输出概率密度函教(PDF)控制模型为基础,介绍了直接迭代学习控制算法收敛的必要条件,提出自适应的迭代学习参数调节方法和避免过迭代的迭代结束条件,这些措施能够保证输出PDF的迭代控制收敛且具有较快的收敛速度.仿真结果表明,输出PDF的自适应迭代学习控制具有较快的收敛速度,而学习终止条件能很好地避免过迭代.     

人机不确定条件下康复步行训练机器人的部分记忆迭代学习限速控制

为了提高康复步行训练机器人的跟踪精度及安全性,提出了一种带有运动速度约束和部分记忆信息的自适应迭代学习控制方法,目的是抑制人机不确定性及速度突变对系统跟踪性能的影响.在考虑人机不确定性的基础上,建立了康复步行训练机器人的动力学模型.提出了基于模型预测的速度约束方法,通过限制每个轮子的运动速度,约束了机器人的实际运动速度.进一步,利用受约束的运动速度建立了动力学跟踪误差系统,提出了具有部分记忆信息的自适应迭代学习控制器设计方法,并验证了跟踪误差系统的稳定性.仿真对比分析和实验研究结果表明,文中提出的控制方法能抑制人机不确定性并使康复者在安全速度下完成步行训练.     

基于改进型积分终端滑模控制方法的移动机器人轨迹跟踪设计与实验

为了使移动机器人获得高精度和快速收敛的跟踪性能,设计一种基于积分终端滑模和滑模观测器的轨迹跟踪控制方法.首先,考虑到移动机器人在实际运动过程中会受到地面湿滑、摩擦等原因引起的侧滑扰动的影响,建立其在该扰动影响下的运动学模型;然后,利用该动态模型设计滑模观测器来估计系统受到的扰动;接着,将估计的扰动值前馈至反馈控制器,用来抑制扰动对系统控制性能的影响,从而达到削弱抖振的目的;同时,基于跟踪误差设计积分终端滑模面,并结合滑模面和扰动估计设计新型积分终端滑模控制器;最后,基于Lyapunov稳定性理论对整个闭环系统进行稳定性分析.仿真实验结果表明,所设计的控制器具有更高的跟踪精度和更强的鲁棒性.     

高超声速飞行器轨迹跟踪的反步滑模控制

针对高超声速飞行器轨迹跟踪控制问题,基于纵向动力学的输入/输出线性化模型,设计了一种基于反步法与滑模控制相结合的反步滑模跟踪控制器;这种控制器对系统匹配不确定性和非匹配不确定性都具有较强的鲁棒性,跟踪收敛快速性良好;对反步滑模跟踪控制器和一般滑模跟踪控制器进行了控制参数仿真优化,对比研究了速度跟踪、高度跟踪、速度高度同时跟踪和正弦速度跟踪条件下两种控制器的快速性和鲁棒性,证实了文章方法的有效性。     

一类不确定系统的自适应滑模迭代学习控制

本文针对一类在有限时间内执行重复任务的不确定非线性系统状态跟踪问题,提出一种自适应滑模迭代学习控制方法,在存在初始偏移的情况下也能实现对参考轨迹的完全收敛.本文通过设计全饱和自适应迭代学习更新律,估计参数和非参数不确定性以及未知期望控制输入,并将估计值限制在指定界内,避免估计值的正向累加.文章设计的自适应滑模迭代学习控制方法对系统模型的信息需求少,在对系统非参数不确定性的上界估计时不需要Lipschitz界函数已知.本文给出严格的理论分析,证明闭环系统所有信号的一致有界性以及跟踪误差的一致收敛性,并通过仿真验证所提控制方法的有效性.     

基于时变终端滑模的任意迭代初态抑制控制EI北大核心CSCD

为解决迭代学习过程中的任意迭代初值和迭代收敛理论证明难的问题,本文构造了一种轨迹跟踪误差初值恒位于滑模面内的时变终端滑模面,将轨迹跟踪误差初值不为零的轨迹跟踪控制问题转换为滑模面初值恒为零的滑模面跟踪控制问题,建立了任意迭代初值与相同迭代初值的迭代学习控制理论连接桥梁.本文提出一种基于时变滑模面的比例–积分–微分(PID)型闭环迭代学习控制策略,基于压缩映射原理证明了迭代学习的收敛性,给出了迭代收敛条件.时变终端滑模面经有限次迭代学习收敛到零,达到轨迹跟踪误差最终稳定在时变滑模面内的目的;Lyapunov稳定理论证明了位于滑模面内的轨迹跟踪误差在有限时间内收敛到原点,达到轨迹局部精确跟踪目的.随机初态下的工业机器人轨迹跟踪控制数值仿真验证了本文方法的有效性和系统对外部强干扰的鲁棒性.     

基于低通滤波器的不确定机器人迭代学习控制

针对不确定机器人系统轨迹跟踪问题,并更好地消除系统不确定性对控制性能的影响,提出一种基于低通滤波器的迭代学习控制方法。采用滑模变结构控制(SMC)以提高控制器对系统干扰和摄动的鲁棒性,并在控制器输出端引入低通滤波器(LPF)来消除滑模控制中出现的抖振现象。将系统的不确定项描述为周期性和非周期性两部分,通过采用迭代学习算法对周期性不确定部分进行迭代学习,采用RBF神经网络对非周期性不确定部分的未知上界进行自适应学习。该控制方法不仅对系统的不确定性和有界外部扰动具有鲁棒性,而且使得整个系统在迭代域中是全局渐近稳定的。严格的理论推导和仿真结果表明了该控制策略的有效性。     

低风速风力机最大风能追踪的互补滑模控制

针对风力机系统在最大功率点跟踪(MPPT)阶段易受风速等不确定因素的影响,为了进一步提高风力机的风能捕获效率,本文在滑模控制的基础上提出了一种互补滑模控制方法.首先,建立了含有干扰项的风力机系统的线性化模型,采用广义滑模面与互补滑模面相结合的方法设计了互补滑模控制器,并在理论上证明了此控制方法能够有效保证风力机转速跟踪误差的收敛性,且能提高转速跟踪精度.其次,采用风力机专业仿真软件FAST对美国可再生能源实验室(NREL)的600 kW风力机进行了仿真实验,结果表明本文所提出的控制方法不但能提高风力机的风能捕获效率,而且能有效减小转速跟踪误差.最后,将本文所提方法与现有常见的几种控制方法相比较发现:风力机系统在互补滑模控制策略下,具有更高的风能捕获效率和更小的转速跟踪误差.     

An integrated robust iterative learning control strategy for batch processes based on 2D system

This paper studies the problem of integrated control in the 2-dimensional (2D) system with parameter uncertainties for batch processes. An integrated iterative learning control (ILC) strategy based on quadratic performance for batch processes is proposed. It realizes comprehensive control by combining robust ILC in batch-axis with model predictive control (MPC) in time-axis. The design of quadratic-criterion-based ILC for the system can be converted into a min-max problem. Then a model predictive controller with time-varying prediction horizon is designed based on a quadratic cost function. For an uncertain model, a novel integrated robust ILC scheme based on a nominal model is further proposed. As a result, the control law of the 2D system can be regulated during one batch, which leads to good tracking performance and strong robustness against the disturbance and the uncertainties. Moreover, the analyses of the convergence and tracking performance are given. The proposed methods are applied to batch reactor, and results demonstrate that the system has good robustness and convergence. This paper provides a new way for batch processes control.     

一类未知非线性系统的智能迭代学习控制

从自适应的角度设计迭代学习控制，将神经网络引入迭代学习控制中。学习控制与自适应控制相结合，使得对网络权值的学习和跟踪控制同时进行，克服 了经典迭代学习控制的一些缺陷。基于Lyapunov直接方法，证明了整个控制系统的稳定并实现了任意精度的跟踪。实例仿真结果说明了算法 的有效性及其所具有的优点。     

Initial state iterative learning for final state control in motion systems

In this work, an initial state iterative learning control (ILC) approach is proposed for final state control of motion systems. ILC is applied to learn the desired initial states in the presence of system uncertainties. Four cases are considered where the initial position or speed is a manipulated variable and the final displacement or speed is a controlled variable. Since the control task is specified spatially in states, a state transformation is introduced such that the final state control problems are formulated in the phase plane to facilitate spatial ILC design and analysis. An illustrative example is provided to verify the validity of the proposed ILC algorithms.     

Robustification of iterative learning control and repetitive control by averaging

This paper describes a recently developed averaging technique to robustify iterative learning and repetitive controllers. The robustified controllers are found by minimising cost functions that are averaged over either multiple analytical time-domain models or experimental frequency-domain data. The aim is to produce a technique that is simple and general, and can be applied to any iterative learning control (ILC) or repetitive control (RC) design that involves the minimisation of a cost function. Substantial improvement in convergence to zero tracking error in the presence of model uncertainties has been observed for both ILC and RC by this averaging technique.     

Adaptive iterative learning control for switched nonlinear continuous-time systems

In this paper, an adaptive iterative learning control (ILC) method is proposed for switched nonlinear continuous-time systems with time-varying parametric uncertainties. First, an iterative learning controller is constructed with a state feedback term in the time domain and an adaptive learning term in the iteration domain. Then a switched nonlinear continuous-discrete two-dimensional (2D) system is built to describe the adaptive ILC system. Multiple 2D Lyapunov functions-based analysis ensures that the 2D system is exponentially stable, and the tracking error will converge to zero in the iteration domain. The design method of the iterative learning controller is obtained by solving a linear matrix inequality. Finally, the efficacy of the proposed controller is demonstrated by the simulation results.     

IMC-based iterative learning control for batch processes with uncertain time delay

Based on the internal model control (IMC) structure, an iterative learning control (ILC) scheme is proposed for batch processes with model uncertainties including time delay mismatch. An important merit is that the IMC design for the initial run of the proposed control scheme is independent of the subsequent ILC for realization of perfect tracking. Sufficient conditions to guarantee the convergence of ILC are derived. To facilitate the controller design, a unified controller form is proposed for implementation of both IMC and ILC in the proposed control scheme. Robust tuning constraints of the unified controller are derived in terms of the process uncertainties described in a multiplicative form. To deal with process uncertainties, the unified controller can be monotonically tuned to meet the compromise between tracking performance and control system robust stability. Illustrative examples from the recent literature are performed to demonstrate the effectiveness and merits of the proposed control scheme.     

Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking

A novel control technique is proposed by combining iterative learning control (ILC) and model predictive control (MPC) with updating-reference trajectory for point-to-point tracking problem of batch process. In this paper, a batch-to-batch updating-reference trajectory, which passes through the desired points, is firstly designed as the tracking trajectory within a batch. The updating control law consists of P-type ILC part and MPC part, in which P-type ILC part can improve the performance by learning from previous executions and MPC part is used to suppress the model perturbations and external disturbances. Convergence properties of the integrated predictive iterative learning control (IPILC) are analyzed theoretically, and the sufficient convergence conditions of output tracking error are also derived for a class of linear systems. Comparing with other point-to-point tracking control algorithms, the proposed algorithm can perform better in robustness. Furthermore, updating-reference relaxes the constraints for system outputs, and it may lead to faster convergence and more extensive range of application than those of fixed-reference control algorithms. Simulation results on typical systems show the effectiveness of the proposed algorithm.