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1.
针对于具有初始状态不确定性的非线性时不变系统,采用矩形脉冲信号补偿传统的比例微分型一阶和二阶迭代学习控制律.在Lebesgue-p范数度量跟踪误差意义下,利用卷积的推广的Young不等式分析学习控制律的跟踪性能.分析表明,在适当选取比例学习增益,微分学习增益和非线性状态函数的Lipschitz常数以保证收敛因子小于1的前提下,渐近跟踪误差是由初始状态不确定性引起的,而且可通过调节补偿因子予以消减.数值仿真验证了补偿策略的有效性和理论分析的正确性.  相似文献   

2.
针对非线性时变系统的迭代学习控制问题提出了一种开闭环PID型迭代学习控制律,并证明了系统满足收敛条件时,具有开闭环PID型迭代学习律的一类非线性时变系统在动态过程存在干扰的情况下控制算法的鲁棒性问题.分析表明,系统在状态干扰、输出干扰和初态干扰有界的情况下跟踪误差有界收敛,在所有干扰渐近重复的情况下可以完全地跟踪给定的期望轨迹.  相似文献   

3.
针对一类线性广义系统,研究其P型迭代学习控制在离散频域中的收敛性态。在离散频域中,对广义系统进行奇异值分解后,利用傅里叶级数系数的性质和离散的Parseval能量等式,推演了一阶P型迭代学习控制律跟踪误差的离散能量频谱的递归关系和特性,获得了学习控制律收敛的充分条件;讨论了二阶P型迭代学习控制律的收敛条件。仿真实验验证了理论的正确性和学习律的有效性。  相似文献   

4.
对存在执行器故障的连续线性时变系统,给出了PID型迭代学习容错控制律的收敛条件。对连续时变故障系统设计了一种PID迭代学习容错控制律,在[λ]范数意义下给出了故障系统PID型迭代容错控制器收敛的充要条件;基于Schur补原理和不等式变换,将容错控制器收敛条件转换成线性矩阵不等式,当迭代学习收敛速度设定时,基于线性矩阵不等式能快速确定最优迭代控制增益,避免了迭代控制增益设置的盲目性。旋转控制系统的数值仿真,验证了PID迭代容错控制器优良的容错性能和跟踪性能。  相似文献   

5.
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.  相似文献   

6.
针对存在复合干扰的飞翼布局无人机(UAV)姿态控制问题,提出了一种基于分数阶积分滑模与双幂次趋近律的姿态跟踪控制方案.结合分数阶微积分及滑模变结构控制理论,设计了分数阶积分滑模面.为解决传统趋近律收敛时间长和抖振严重等不足,提出一种具有二阶滑模特性且有限时间收敛的双幂次趋近律.在名义控制律的基础上,设计super twisting滑模干扰观测器,实现对复合干扰的估计和补偿,增强内外环控制器应对复合干扰的鲁棒性.为充分利用冗余操纵面与解决非线性舵效问题,在飞行控制系统中引入了非线性控制分配环节.仿真结果验证了所提方案的有效性.  相似文献   

7.
对于具有重复运动性质的对象,迭代学习控制是一种有效的控制方法.针对一类 离散非线性时变系统在有限时域上的精确轨迹跟踪问题,提出了一种开闭环PI型迭代学习 控制律.这种迭代律同时利用系统当前的跟踪误差和前次迭代控制的跟踪误差修正控制作 用.给出了所提出的学习控制律收敛的充分必要条件,并采用归纳法进行了证明.最后用仿真 结果对收敛条件进行了验证.  相似文献   

8.
针对一类线性时不变系统,讨论存在固定初始偏移时的学习控制问题,提出带有反馈辅助项的比例微分(proportion differentiation,PD)型学习控制算法,分析所提算法在Lebesgue-p范数意义下的单调收敛性,获得对期望轨迹的渐近跟踪结果.进一步地,为获得系统输出对期望轨迹的完全跟踪,给出带有初始修正策略的比例–积分–微分(proportion multiple integration differentiation,PMID)型学习律,并给出了所提学习算法的单调收敛性能分析结果.最后,通过数值结果,验证了所提学习算法的跟踪性能和单调收敛性能.  相似文献   

9.
设计了一种基于折扣广义值迭代的智能算法, 用于解决一类复杂非线性系统的最优跟踪控制问题. 通过选取合适的初始值, 值迭代过程中的代价函数将以单调递减的形式收敛到最优代价函数. 基于单调递减的值迭代算法, 在不同折扣因子的作用下, 讨论了迭代跟踪控制律的可容许性和误差系统的渐近稳定性. 为了促进算法的实现, 建立一个数据驱动的模型网络用于学习系统动态信息, 同时构造评判网络和执行网络用于近似迭代代价函数和计算迭代跟踪控制律. 值得注意的是, 我们提出了新颖的停止准则来保证迭代跟踪控制律的有效性. 这种停止准则包含两个条件, 一个条件用来保证迭代跟踪控制律的可用性, 这有利于评估误差系统的渐近稳定性; 而另一个条件用来确保跟踪控制律的近似最优性. 最后, 通过包括污水处理在内的两个应用实例验证了本文提出的近似最优跟踪控制方法的可行性和有效性.  相似文献   

10.
陈若珠  宋军伟  李战明 《微计算机信息》2007,23(19):250-251,194
迭代学习控制是应用在具有重复运动特点的系统中,并有良好的跟踪效果的控制方法.本文针对转台控制系统中的大惯性环节,运用开环P型迭代学习控制,仿真证明该学习控制律的有效性.并证明学习控制律的收敛条件.  相似文献   

11.
This paper presents a new iterative learning control (ILC) for discrete-time single-input single-output (SISO) linear time-invariant (LTI) systems. To establish this ILC, the input of the controlled system is modified by using a novel four-parametric algorithm. This algorithm is called the extended proportional plus integral and derivative (EPID) type, since by eliminating the fourth parameter of it one would get to the PID type ILC, therefore PID type ILC is a special case of it. The convergence of the proposed ILC is analyzed and an optimal method is presented to determine its parameters. It is shown that the given ILC has a better performance than the PID-type one. Three illustrative examples are included to demonstrate the effectiveness and the preference of the presented ILC.  相似文献   

12.
In this paper, a quasi-Newton-type optimized iterative learning control (ILC) algorithm is investigated for a class of discrete linear time-invariant systems. The proposed learning algorithm is to update the learning gain matrix by a quasi-Newton-type matrix instead of the inversion of the plant. By means of the mathematical inductive method, the monotone convergence of the proposed algorithm is analyzed, which shows that the tracking error monotonously converges to zero after a finite number of iterations. Compared with the existing optimized ILC algorithms, due to the superlinear convergence of quasi-Newton method, the proposed learning law operates with a faster convergent rate and is robust to the ill-condition of the system model, and thus owns a wide range of applications. Numerical simulations demonstrate the validity and effectiveness.  相似文献   

13.
In this paper, iterative learning control (ILC) design is studied for an iteration-varying tracking problem in which reference trajectories are generated by high-order internal models (HOIM). An HOIM formulated as a polynomial operator between consecutive iterations describes the changes of desired trajectories in the iteration domain and makes the iterative learning problem become iteration varying. The classical ILC for tracking iteration-invariant reference trajectories, on the other hand, is a special case of HOIM where the polynomial renders to a unity coefficient or a special first-order internal model. By inserting the HOIM into P-type ILC, the tracking performance along the iteration axis is investigated for a class of continuous-time nonlinear systems. Time-weighted norm method is utilized to guarantee validity of proposed algorithm in a sense of data-driven control.  相似文献   

14.

This work investigates the attitude control of reentry vehicle under modeling inaccuracies and external disturbances. A robust adaptive fuzzy PID-type sliding mode control (AFPID-SMC) is designed with the utilization of radial basis function (RBF) neural network. In order to improve the transient performance and ensure small steady state tracking error, the gain parameters of PID-type sliding mode manifold are adjusted online by using adaptive fuzzy logic system (FLS). Additionally, the designed new adaptive law can ensure that the closed-loop system is asymptotically stable. Meanwhile, the problem of the actuator saturation, caused by integral term of sliding mode manifold, is avoided even under large initial tracking error. Furthermore, to eliminate the need of a priori knowledge of the disturbance upper bound, RBF neural network observer is used to estimate the disturbance information. The stability of the closed-loop system is proved via Lyapunov direct approach. Finally, the numerical simulations verify that the proposed controller is better than conventional PID-type SMC in terms of improving the transient performance and robustness.

  相似文献   

15.
Iterative learning control (ILC) is an efficient way of improving the tracking performance of repetitive systems. While ILC can offer significant improvement to the transient response of complex dynamical systems, the fundamental assumption of iteration invariance of the process limits potential applications. Utilizing abstract Banach spaces as our problem setting, we develop a general approach that is applicable to the various frameworks encountered in ILC. Our main result is that robust invariant update laws lead to stable behavior in ILC systems, where iteration-varying systems converge to bounded neighborhoods of their nominal counterparts when uncertainties are bounded. Furthermore, if the uncertainties are convergent along the iteration axis, convergence to the nominal case can be guaranteed.  相似文献   

16.
This paper presents the application of iterative learning control (ILC) to compensate hysteresis in a piezoelectric actuator. The proposed controller is a hybrid of proportional-integral-differential (PID) control, whose main function is for trajectory tracking, and a chatter-based ILC, whose main function is for hysteresis compensation. Stability analysis of the proposed ILC is presented, with the PID included in the dynamic of the piezoelectric actuator. The performance of the proposed controller is analysed through simulation and verified with experiment with a piezoelectric actuator.  相似文献   

17.
在学习型模型预测控制的框架里,迭代学习控制器被用来更新模型预测控制器的设定点.在已经发表的研究成果里,学习型模型预测控制用到的是比例型的学习率,这种学习率的学习能力有限,而且怎样设计学习增益仍然是一个开放性问题.在本文中,基于内模控制理论提出的PID型的迭代学习控制器被用来更新模型预测控制器的设定点.为了方便起见,本文提出的结合算法可称为内模强化学习型模型预测控制.本文提出的算法应用在(1)型糖尿病人的人工胰脏闭环控制上.仿真结果显示,本算法对比于比例学习型模型预测控制可以达到更好的收敛性能,而且对非重复干扰有很好的鲁棒性.  相似文献   

18.
In this paper, an intelligent transportation control system (ITCS) using wavelet neural network (WNN) and proportional-integral-derivative-type (PID-type) learning algorithms is developed to increase the safety and efficiency in transportation process. The proposed control system is composed of a neural controller and an auxiliary compensation controller. The neural controller acts as the main tracking controller, which is designed via a WNN to mimic the merits of an ideal total sliding-mode control (TSMC) law. The PID-type learning algorithms are derived from the Lyapunov stability theorem, which are utilized to adjust the parameters of WNN on-line for further assuring system stability and obtaining a fast convergence. Moreover, based on H control technique, the auxiliary compensation controller is developed to attenuate the effect of the approximation error between WNN and ideal TSMC law, so that the desired attenuation level can be achieved. Finally, to investigate the effectiveness of the proposed control strategy, it is applied to control a marine transportation system and a land transportation system. The simulation results demonstrate that the proposed WNN-based ITCS with PID-type learning algorithms can achieve favorable control performance than other control methods.  相似文献   

19.
With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.   相似文献   

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