共查询到19条相似文献,搜索用时 171 毫秒
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基于工业过程稳态优化中递阶控制结构和线性工业过程控制系统中的迭代学习控制规律, 本文对饱和非线性工业过程控制系统和变增益非线性工业过程控制系统施行迭代学习控制, 分别给出加权PD 型闭环迭代学习控制算法和加权幂型开闭环迭代学习控制算法, 提出了期望目标轨线的 δ 可达性和迭代学习算法的ε 收敛性的概念. 利用Bellman Gronwall不等式和λ 范数理论, 论证了算法的收敛性. 数字仿真表明, 迭代学习控制能有效改善非线性工业控制系统在稳态优化时的动态品质. 相似文献
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基于模糊模型的大系统关联平衡法的收敛性 总被引:1,自引:0,他引:1
结合模糊规划与非线性规划的收敛性分析方法,给出了基于模糊模型的关联平衡法的
收敛性分析.首先证明了经去模糊处理后形成的约束集合与子过程原有的约束集合必有交集.
并且此交集是凸集.在此基础上,分析和证明了基于模糊模型的关联平衡法可用于求解基于模
糊模型的稳态大工业过程递阶优化问题.继而通过定义迭代序列的A-内积,证明了基于模糊模
型的关联平衡法是收敛的.同时给出了保证迭代收敛的迭代系数取值范围. 相似文献
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对于非线性迭代学习控制问题,提出基于延拓法和修正Newton法的具有全局收敛性的迭代学习控制新方法.由于一般的Newton型迭代学习控制律都是局部收敛的,在实际应用中有很大局限性.为拓宽收敛范围,该方法将延拓法引入迭代学习控制问题,提出基于同伦延拓的新的Newton型迭代学习控制律,使得初始控制可以较为任意的选择.新的迭代学习控制算法将求解过程分成N个子问题,每个子问题由换列修正Newton法利用简单的递推公式解出.本文给出算法收敛的充分条件,证明了算法的全局收敛性.该算法对于非线性系统迭代学习控制具有全局收敛和计算简单的优点. 相似文献
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传统的迭代学习控制机理中, 积分补偿是典型的策略之一, 但其跟踪效用并不明确. 本文针对连续线性时
不变系统, 对传统的PD–型迭代学习控制律嵌入积分补偿, 利用分部积分法和推广的卷积Young不等式, 在Lebesgue-
p范数意义下, 理论分析一阶和二阶PID–型迭代学习控制律的收敛性态. 结果表明, 当比例、积分和导数学习增益满
足适当条件时, 一阶PID–型迭代学习控制律是单调收敛的, 二阶PID–型迭代学习控制律是双迭代单调收敛的. 数值
仿真验证了积分补偿可有效地提高系统的跟踪性能. 相似文献
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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
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非线性非仿射离散时间系统的两阶段最优迭代学习控制 总被引:1,自引:0,他引:1
针对非仿射非线性离散时间系统, 基于一种新的沿迭代轴的动态线性化技术, 提出了双层最优迭代学习控制算法. 双层意味着分别设计了两个最优学习层, 迭代的改进控制输入序列和学习增益. 其主要特点是控制器的设计和收敛性分析只依赖于动态系统的 I/O 数据. 换句话说, 不需要知道系统的任何其他信息就可以很容易的选取控制器参数. 仿真研究表明了提出的算法沿迭代轴具有几何收敛性, 这一特点在快速路交通迭代学习控制中具有重要的工程意义. 相似文献
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离散非线性系统开闭环P型迭代学习控制律及其收敛性 总被引:9,自引:3,他引:9
本文在讨论了一般开环与闭环迭代学习控制的不足后,针对一类离散非线性系统,提出了新的开闭环PG型迭代学习控制律,给出了它的收敛性证明,仿真结果表明:开闭环P型迭代律优于单纯的开环或产才环P型迭代 律。 相似文献
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In the procedure of steady-state hierarchical optimization for large-scale industrial processes, it is often necessary that the control system responds to a sequence of step function-type control decisions with distinct magnitudes. In this paper a set of iterative learning controllers are de-centrally embedded into the procedure of the steady-state optimization. This generates upgraded sequential control signals and thus improves the transient performance of the discrete-time large-scale systems. The convergence of the updating law is derived while the intervention from the distinction of the scales is analysed. Further, an optimal iterative learning control scheme is also deduced by means of a functional derivation. The effectiveness of the proposed scheme and the optimal rule is verified by simulation. 相似文献
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In this article, a set of decentralised open-loop and closed-loop iterative learning controllers are embedded into the procedure of steady-state hierarchical optimisation utilising feedback information for large-scale industrial processes. The task of the learning controllers is to generate a sequence of upgraded control inputs iteratively to take responsibility for sequential step function-type control decisions, each of which is determined by the steady-state optimisation layer and then imposed on the real system for feedback information. In the learning control scheme, the learning gains are designated to be time-varying which are adjusted by virtue of expertise experiences-based IF-THEN rules, and the magnitudes of the learning control inputs are amplified by the sequential step function-type control decisions. The aim of learning schemes is to further effectively improve the transient performance. The convergence of the updating laws is deduced in the sense of Lebesgue 1-norm by taking advantage of the Hausdorff–Young inequality of convolution integral and the Hoelder inequality of Lebesgue norm. Numerical simulations manifest that both the open-loop and the closed-loop time-varying learning gain-based schemes can effectively decrease the overshoot, accelerate the rising speed and shorten the settling time, etc. 相似文献
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Ruan Xiaoe Bien Z.Z. Park Kwang-Hyun 《IEEE transactions on systems, man, and cybernetics. Part A, Systems and humans : a publication of the IEEE Systems, Man, and Cybernetics Society》2008,38(1):238-252
In the procedure of steady-state hierarchical optimization with feedback for a large-scale industrial process, it is usual that a sequence of step set-point changes is carried out and used by the decision-making units while searching the eventual optimum. In this case, the real process experiences a form of disturbances around its operating set-point. In order to improve the dynamic performance of transient responses for such a large-scale system driven by the set-point changes, an open-loop proportional integral derivative-type iterative learning control (ILC) strategy is explored in this paper by considering the different magnitudes of the controller's step set-point change sequence. Utilizing the Hausdorff-Young inequality of convolution integral, the convergence of the algorithm is derived in the sense of Lebesgue-P norm. Furthermore, the extended higher order ILC rule is developed, and the convergence is analyzed. Simulation results illustrate that the proposed ILC strategies can remarkably improve the dynamic performance such as decreasing the overshoot, accelerating the transient response, shortening the settling time, etc. 相似文献
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In the procedure of the steady-state hierarchical optimization with feedback for large-scale industrial processes, a sequence of set-point changes with different magnitudes is carried out on the optimization layer. To improve the dynamic performance of transient response driven by the set-point changes, a filter-based iterative leaning control strategy is proposed. In the proposed updating law, a local-symmetric-integral operator is adopted for eliminating the measurement noise of output information, a set of desired trajectories are specified according to the set-point changes sequence, the current control input is iteratively achieved by utilizing smoothed output error to modify its control input at previous iteration, to which the amplified coefficients related to the different magnitudes of set-point changes are introduced. The convergence of the algorithm is conducted by incorporating frequency-domain technique into time-domain analysis. Numerical simulation demonstrates the effectiveness of the proposed strategy. 相似文献
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大规模工业过程的稳态递阶控制综述 总被引:4,自引:2,他引:2
连续运行的大规模工业过程或加工过程,其递阶控制理论有三个发展阶段:静态多级优
化、稳态递阶优化、系统优化及参数估计的综合方法等.本文评述了这三方面的主要成就并对
今后的重要研究方向提出了展望. 相似文献
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连续运行的大规模工业过程或加工过程,其递阶控制理论有三个发展阶段:静态多级优化、稳态递阶优化、系统优化及参数估计的综合方法等。本文评述了这三方面的主要成就并对今后的重要研究方向提出了展望。 相似文献
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现代工业大系统的优化控制采用递阶结构,其中以预测控制为代表的先进过程控制已经成为重要的一级.目前,主流的工业预测控制技术均采用双层结构,即包含稳态优化层和动态控制层.双层结构预测控制技术可以有效解决复杂工业过程常见的多目标优化、多变量控制的难点问题.本文简要总结了双层结构预测控制的算法,并从控制输入与被控输出稳态关系入手分析了多变量预测控制稳态解的相容性和唯一性,说明了稳态优化的重要性.针对双层结构预测控制与区间预测控制的性能比较、稳态模型的奇异性以及闭环系统动态特性等提出了一些见解,并指出了需要重点研究的主题. 相似文献