共查询到20条相似文献，搜索用时 21 毫秒
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This paper proposes two kinds of iterative learning control (ILC) schemes for a class of the distributed parameter systems based on sensor–actuator networks which can be described by hyperbolic partial differential equations. A D-type ILC algorithm is first considered and the convergent condition of the output error is obtained via the contraction mapping methodology. Then, the PD-type ILC algorithm is considered in this hyperbolic distributed parameter systems based on sensor–actuator networks. Finally, a cable equation with air and structural damping is given to illustrate the effectiveness of the proposed methods. 相似文献
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有向网络下非线性多智能体系统的协调跟踪 总被引：1，自引：0，他引：1
基于一致性理论,在有向拓扑结构下研究非线性多智能体系统的协调跟踪控制问题．考虑智能体动力学模型为一般且仅满足Lipschitz条件的非线性系统,在仅有部分跟随智能体能获取领航智能体信息的情形下,当领航智能体与跟随智能体之间的拓扑结构具有有向生成树,即存在领航智能体到所有跟随智能体的有向路径时,所设计的分布式控制律可实现所有跟随智能体对领航智能体的跟踪,并指出该拓扑结构是系统实现跟踪的一个必要条件．最后,仿真实验验证了所设计控制算法的有效性． 相似文献
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为了解决非仿射非线性多智能体系统在给定时间区间上一致性完全跟踪问题,基于迭代学习控制方法设计一种分布式一致性跟踪控制算法.首先,由引入的虚拟领导者与所有跟随者组成多智能体系统的通信拓扑,其中虚拟领导者的作用是提供期望轨迹.然后,在只有部分跟随者能够获得领导者信息的条件下,利用每个跟随者及其邻居的跟踪误差构造每个跟随者的迭代学习一致性跟踪控制器.同时采用中值定理将非仿射非线性多智能体系统转化仿射形式,并基于压缩映射方法证明所提算法的收敛性,给出算法的收敛条件.理论分析表明,在智能体的非线性函数未知情况下,利用所提算法可以使非仿射非线性多智能体系统在给定时间区间上随迭代次数增加逐次实现一致性完全跟踪.最后,通过仿真算例进一步验证所提算法的有效性. 相似文献
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Jianxiang Zhang Baotong Cui Xisheng Dai Zhengxian Jiang 《IEEE/CAA Journal of Automatica Sinica》2020,7(3):865-871
In this paper, an open-loop PD-type iterative learning control (ILC) scheme is first proposed for two kinds of distributed parameter systems (DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and m actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes. 相似文献
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We consider the stabilization of nonlinear ODE systems with actuator dynamics modeled by a wave PDE whose boundary is moving and is a function of time and of the ODE's state. Such a problem is inspired by applications in oil drilling where the position of the drill bit is a state variable in the ODE modeling the friction‐dominated drill bit dynamics while at the same time being the position of the moving boundary of the wave PDE that models the distributed torsional dynamics of the drillstring. For moving boundaries that depend only on time, we extend the global result recently developed by Bekiaris‐Liberis and Krstic for constant boundaries. For moving boundaries that also depend on the ODE's state, we develop a local result where the initial condition is restricted in such a way that it is ensured that the rate of movement of the boundary (both ‘leftward’ and ‘rightward’) is bounded by unity in closed‐loop. For strict‐feedforward systems under wave actuator dynamics with moving boundaries, the predictor‐based feedback laws are obtained explicitly. The feedback design is illustrated through an example. Copyright © 2013 John Wiley & Sons, Ltd. 相似文献
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Xiao-Dong Li Mang-Mang Lv John K. L. Ho 《International journal of systems science》2016,47(10):2279-2289
In this article, two adaptive iterative learning control (ILC) algorithms are presented for nonlinear continuous systems with non-parametric uncertainties. Unlike general ILC techniques, the proposed adaptive ILC algorithms allow that both the initial error at each iteration and the reference trajectory are iteration-varying in the ILC process, and can achieve non-repetitive trajectory tracking beyond a small initial time interval. Compared to the neural network or fuzzy system-based adaptive ILC schemes and the classical ILC methods, in which the number of iterative variables is generally larger than or equal to the number of control inputs, the first adaptive ILC algorithm proposed in this paper uses just two iterative variables, while the second even uses a single iterative variable provided that some bound information on system dynamics is known. As a result, the memory space in real-time ILC implementations is greatly reduced. 相似文献
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For system operating repetitively, iterative learning control (ILC) has been tested as an effective method even with estimated models. However, the control performance may deteriorate due to sudden system failure or the adoption of imprecise model. The multiple model iterative learning control (MMILC) method shows great potential to improve the transient response and control performance. However, in existed MMILC, the stability can be guaranteed only by finite switching or very strict conditions about coefficient matrix, which make the application of MMILC a little difficult. In this paper, an improved MMILC method is presented. Control procedure is simplified and the ceasing condition is relaxed. Even with infinite times of model switching, system output is proved convergent to the desired trajectory. Simulation studies are carried out to show the effectiveness of the proposed method. 相似文献
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针对多轴运动系统非线性轮廓的重复跟踪,传统时域交叉耦合迭代学习控制器（Cross-coupled iterative learning control,CCILC）的设计,各轴间的耦合算子计算精度要求高,计算效率低.本文提出一种主从交叉耦合迭代学习控制方法.基于主从控制设计方法,主动轴采用时域CCILC,从动轴采用位置域交叉耦合迭代学习控制（Position domain CCILC,PDCCILC）.保证各轴间运动同步性,同时减轻对耦合算子精确性的依赖.因而可以引入轮廓误差矢量法估算耦合算子提高计算效率.采用Lifting的系统时域矩阵展开方法对所提出的算法进行了稳定性分析和性能分析.基于一个两轴毫米级运动平台,三种典型非线性轮廓跟踪（即半圆、抛物线和螺旋线）的数值仿真和实验分析验证了所提出算法的有效性. 相似文献
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非线性系统高阶迭代学习算法 总被引：2，自引：1，他引：2
结合迭代学习控制算法中的开环和闭环方案，本文针对更一般的非线性系统，讨论高阶算法的广泛适用性。理论和仿真结果表明了高阶算法在输出跟踪和干扰抑制方面的有效性。 相似文献
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This paper studies the regulation of nonlinear systems using conditional integrators. Previous work introduced the tool of conditional integrators that provide integral action inside a boundary layer while acting as stable systems outside, leading to improvement in transient response while achieving asymptotic regulation in the presence of unknown constant disturbances or parameter uncertainties. The approach, however, is restricted to a sliding mode control framework. This paper extends this tool to a fairly general class of state feedback control laws, with the stipulation that we know a Lyapunov function for the closed‐loop system. Asymptotic regulation with improvement in transient response is done by using the Lyapunov redesign technique to implement the state feedback control as a saturated high‐gain feedback and introducing a conditional integrator to provide integral action inside a boundary layer. Improvement in the transient response using conditional integrators is demonstrated with an experimental application to the pendubot. Copyright © 2005 John Wiley & Sons, Ltd. 相似文献
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Paola Falugi 《International journal of control》2013,86(4):745-753
This paper proposes a model predictive control scheme for tracking a-priori unknown references varying in a wide range and analyses its performance. It is usual to assume that the reference eventually converges to a constant in which case convergence to zero of the tracking error can be established. In this note we remove this simplifying assumption and characterise the set to which the tracking error converges and the associated region of convergence. 相似文献
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This paper studies the leader-following consensus problem for a class of second-order nonlinear multi-agent systems subject to linearly parameterized uncertainty and disturbance. The problem is solved by integrating the adaptive control technique and the adaptive distributed observer method. The design procedure is illustrated by an example with a group of Van der Pol oscillators as the followers and a harmonic system as the leader. 相似文献
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Existing results for output regulation of singular nonlinear systems via normal output feedback control require the normalizability assumption. In this paper, we will show that, for a large class of singular nonlinear systems, it is possible to construct a normal output feedback control to solve the regulation problem without the normalizability assumption. The major result is illustrated by an example. 相似文献
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In this paper, we introduce a new approach, zero dynamics inverse (ZDI) design, for designing a feedback compensation scheme achieving asymptotic regulation for a linear or nonlinear distributed parameter system in the case when the value w(t) at time t of the signal w to be tracked or rejected is a measured variable. Following the nonequilibrium formulation of output regulation, we formulate the problem of asymptotic regulation by requiring zero steady‐state error together with ultimate boundedness of the state of the system and the controller(s), with a bound determined by bounds on the norms of the initial data and w. Because a controller solving this problem depends only on a bound on the norm of w not on the particular choice of w, this formulation is in sharp contrast to both exact tracking, asymptotic tracking or dynamic inversion of a completely known trajectory and to output regulation with a known exosystem. The ZDI design consists of the interconnection, via a memoryless filter, of a stabilizing feedback compensator and a cascade controller, designed in a simple, universal way from the zero dynamics of the closed‐loop feedback system. This design philosophy is illustrated with a problem of asymptotic regulation for a boundary controlled viscous Burgers' equation, for which we prove that the ZDI is input‐to‐state stable. In infinite dimensions, however, input‐to‐state stable compactness arguments are supplanted by smoothing arguments to accommodate crucial technical details, including the global existence, uniqueness, and regularity of solutions to the interconnected systems. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
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David H. Owens Maria Tomas-Rodriguez Jari J. Hatoenen 《国际自动化与计算杂志》2006,3(3):222-228
This paper analyses the concept of a Limit Set in Parameter Optimal Iterative Learning Control (ILC). We investigate the existence of stable and unstable parts of Limit Set and demonstrates that they will often exist in practice. This is illustrated via a 2-dimensional example where the convergence of the learning algorithm is analyzed from the error's dynamic behaviour. These ideas are extended to the AT-dimensional cases by analogy and example. 相似文献
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This paper proposes a novel networked iterative learning control (NILC) scheme with adjustment factor for a class of discrete‐time uncertain nonlinear systems with stochastic input and output packet dropout modeled as 0‐1 Bernoulli‐type random variable. Firstly, the equivalence relation between the realizability of controlled system and the input‐output coupling parameter (IOCP) is established. Secondly, in order to overcome the main obstacle arising from the unknown IOCP, an identification technique is developed for it. Thirdly, it is strictly proved that, under certain conditions, the tracking errors driven by the developed NILC scheme are convergent to zero along iteration direction in the sense of expectation. Finally, an example is given to demonstrate the effectiveness of the proposed NILC scheme and the merits of adjustment factor. 相似文献
