共查询到20条相似文献，搜索用时 84 毫秒
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内模控制是一种基于过程数学模型进行控制器设计的新型控制策略,是研究预测控制重要的理论基础。预测函数控制是一种控制量计算方程简单,实时控制效果好的新型预测控制算法。本文用内模控制理论研究预测函数控制,分析了系统的鲁棒性和稳定性,最后进行了参数设计和仿真研究。 相似文献
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预测控制算法的内模结构及其统一格式 总被引：13，自引：1，他引：13
本文应用内模控制（ＩＭＣ）原理，研究了现有各类预测控制算法ＩＭＣ、ＭＡＣ、ＤＭＣ、ＧＰＣ、ＧＰＰ的控制器方程，闭环系统输入输出和误差方程，归纳出它们的统一算式，为理解各类预测控制算法的内在联系及进一步研究提供了方便． 相似文献
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针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案.同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法.仿真结果表明,该方法是有效的,其控制性能优于PID迭代学习控制系统. 相似文献
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针对注射过程具有重复运行和非线性的特性,在对预测控制与迭代学习控制进行综合应用并加以改进的基础上,给出一种模型预测迭代学习复合控制新算法,研究了控制器的设计方案。同时,将迭代学习思想引入到预测步长的在线调整,提出了预测步长的迭代学习方法。仿真结果表明,该方法是有效的,其控制性能优于PID送代学习控制系统。 相似文献
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一类非最小相位对象的内模预测控制：MIMO情形 总被引：1，自引：0，他引：1
针对一类多变量非最小相位受控对象,提出一种基于多道非因果ＦＩＲ型控制器的内模预测控制新算法,建立了控制器系数矢量与多变量受控对象输出的关系,并给出相应的控制器系数矢量估计法与仿真实例。 相似文献
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将迭代学习控制(Iterative learning control, ILC)系统看作一类具有2维动态特性的控制系统,根据模型预测控制(Model predictive control, MPC)和性能参考模型控制思想, 提出了一种基于2维性能参考模型的2维模型预测迭代学习控制系统设计方案.在该控制系统设计方案中,可以通过选择适当的2 维性能参考模型来构造2 维动态变化的设定值信号和预测控制信号,从而引导迭代学习控制系统收敛到合理的控制性能,并有效避 免系统性能收敛过程中控制输入可能发生的剧烈波动.通过对控制系统的结构分析可知,所得的迭代学习控制器本质上是由沿时 间指标的参考模型预测控制器和沿周期指标的迭代学习控制器组成,闭环系统的收敛性等价于一个2维滤波系统的稳定性.数值仿 真结果证明了该设计方案的有效性和鲁棒性. 相似文献
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迭代学习模型预测控制是针对间歇过程的先进控制方法.它能通过迭代高精度跟踪给定参考轨迹,并保证时域上的闭环稳定性.然而,现有的迭代学习模型预测控制算法大多基于线性/线性化系统,且没有考虑参考轨迹变化的情况.本文基于线性参变系统提出一种能有效跟踪变参考轨迹的鲁棒迭代学习模型预测控制算法.首先,采用线性参变模型准确涵盖原始非线性系统的动态特性.然后,将鲁棒H∞控制与传统迭代学习模型预测控制相结合,抑制变参考轨迹带来的跟踪误差波动,通过优化线性矩阵不等式约束下的目标函数求得控制输入.深入分析了鲁棒迭代学习模型预测控制的鲁棒稳定性和迭代收敛性.最后,通过对数值例子和连续搅拌反应釜系统的仿真验证了所提出算法的有效性. 相似文献
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迭代学习模型预测控制(Iterative learning model predictive control,ILMPC)具备较强的批次学习能力及突出的时域跟踪性能,在批次过程控制中发挥了重要作用.然而对于具有强非线性的快动态批次过程,传统的迭代学习模型预测控制很难实现计算效率与跟踪精度之间的平衡,这给其应用带来了挑战.对此本文提出一种高效迭代学习预测函数控制策略,将原非线性系统沿参考轨迹线性化得到二维跟踪误差预测模型,并在控制器设计中补偿所产生的线性化误差,构造优化目标函数为真实跟踪误差的上界.为加强优化计算效率,在时域上结合预测函数控制以降低待优化变量维数,从而有效降低计算负担.结合终端约束集理论,分析了迭代学习预测函数控制的时域稳定性及迭代收敛性.通过对无人车和典型快速间歇反应器的仿真实验验证所提出算法的有效性. 相似文献
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《Journal of Process Control》2014,24(10):1527-1537
Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions. 相似文献
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Generally, the classic iterative learning control(ILC)methods focus on finding design conditions for repetitive systems to achieve the perfect tracking of any specified trajectory,whereas they ignore a fundamental problem of ILC: whether the specified trajectory is trackable, or equivalently, whether there exist some inputs for the repetitive systems under consideration to generate the specified trajectory? The current paper contributes to dealing with this problem. Not only is a concept of trac... 相似文献
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Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Channel Noise 
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下载免费PDF全文 Channel noise, including sensor‐to‐controller(SC) noise and controller‐to‐actuator(CA) noise, impacts the convergence of wireless remote iterative learning control (ILC) system significantly. In this paper, the relationship between output error, SC noise and CA noise is obtained firstly by super‐vector formulation, and then the norm of output error vector covariance matrix is employed to analyze the convergence of the system in presence of SC noise and CA noise. Upper bound of the norm at any sample time reveals that the SC noise is accumulated only in iteration domain, while the CA noise is accumulated not only in iteration domain but also in time domain. Furthermore, the accumulated effect of the CA noise in time domain is ruled by system matrices, so the values of which determine the effect of the CA noise is greater or less than that of the SC noise on convergence of the system. Finally, some simulation results are given to illustrate correctness of the result. 相似文献
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This paper investigates variable-gain PD-type iterative learning control (ILC) for a class of nonlinear time-varying systems to well balance high-gain convergence rate and low-gain noise transmission. Different from the classic PD-type ILC, the control gains of the proposed method are variable. Each variable-gain consists of an amplitude-dependent term and an iteration-varying term. The amplitude-dependent terms vary with the amplitudes of tracking error and derivative of tracking error, and the iteration-varying terms are increasing along the iteration axis. The proposed ILC achieves a faster convergence rate than low-gain ILC and higher tracking accuracy with limited noise amplification than high-gain ILC. Moreover, the convergence condition of the proposed method in the presence of external noise is provided. Simulation and experimental results demonstrate the effectiveness of the proposed method. 相似文献
