共查询到19条相似文献,搜索用时 78 毫秒
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用微粒群优化算法解决存在约束的广义预测控制的优化问题,并给出了基于微粒群优化算法的广义预测控制算法的实现方法.将该算法应用到工业过程对象中进行测试,仿真结果表明了算法的有效性和高效性,获得了良好的控制效果. 相似文献
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一种解决预测控制输入信号受约束问题的方法 总被引:17,自引:0,他引:17
本文通过适当选取指标函数中各加权多项式,提出一种解决预测控制输入信号受约束问题的方法,由于使指标函数取最小值的当前控制信号与将来的控制信号无关,因而使控制算法大大简化。 相似文献
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基于模糊目标和模糊约束的满意控制 总被引:4,自引:1,他引:3
研究在预测控制框架下进行模糊决策问题,提出一种基于模糊目标和模糊约束的预测控制方法。其目标函数以决策者的控制要求和最终控制的满意度来表示,比传统的加权方差具有更多的自由度;与基于二次型性能指标的预测相比,该方法可使系统设计更加灵活。仿真结果表明了该方法的有效性。 相似文献
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针对预测函数控制的约束不可行和优先级问题,基于混杂系统以及混合逻辑动态模型的思想提出了约束不可行及优先级问题的混杂PFC处理方法,讨论了由PFC、混杂系统、松弛变量法所组成的约束不可行及优先级问题的具体实现方法与步骤.在通常PFC跟踪性能指标的基础上结合MLD与松弛变量法,通过将命题逻辑转化为混合逻辑不等式并与系统自身... 相似文献
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This work addresses the problem of offset-free Model Predictive Control (MPC) when tracking an asymptotically constant reference. In the first part, compact and intuitive conditions for offset-free MPC control are introduced by using the arguments of the internal model principle. In the second part, we study the case where the number of measured variables is larger than the number of tracked variables. The plant model is augmented only by as many states as there are tracked variables, and an algorithm which guarantees offset-free tracking is presented. In the last part, offset-free tracking properties for special implementations of MPC schemes are briefly discussed. 相似文献
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预测柠制算法为了在自由度不足时按优先级优先保证基本控制目标,在自由度多余时充分利用自由度提高效益,在算法中引了优化 控制的策略,本文首先介绍和分析了以前对预测控制的改进工作,最后给出了基于目标规划思想的优化策略。 相似文献
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本文给出一种双层结构预测控制的整体解决方案. 该方案分为开环预测、稳态目标计算和动态控制三个模块. 开环预测基于实测被控变量值和过去的操作变量值, 在假设未来操作变量不再变化的情况下, 估计未来的被控变量值. 稳态目标计算根据开环预测结果和外部目标等要求, 计算操作变量、被控变量的稳态目标值以及软约束的放松量. 动态控制根据开环预测结果和稳态目标输出结果, 计算未来的控制作用增量序列, 采用经典的动态矩阵控制策略. 这个整体解决方案保证了三个模块在模型、约束、目标上的一致性. 该算法是在已有文献的基础上, 将三个模块统一处理得到的. 仿真与应用例子证实了该算法的有效性. 相似文献
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Hybrid Fuzzy Modelling for Model Predictive Control 总被引:1,自引:0,他引:1
Gorazd Karer Gašper Mušič Igor Škrjanc Borut Zupančič 《Journal of Intelligent and Robotic Systems》2007,50(3):297-319
Model predictive control (MPC) has become an important area of research and is also an approach that has been successfully
used in many industrial applications. In order to implement a MPC algorithm, a model of the process we are dealing with is
needed. Due to the complex hybrid and nonlinear nature of many industrial processes, obtaining a suitable model is often a
difficult task. In this paper a hybrid fuzzy modelling approach with a compact formulation is introduced. The hybrid system
hierarchy is explained and the Takagi–Sugeno fuzzy formulation for the hybrid fuzzy modelling purposes is presented. An efficient
method for identifying the hybrid fuzzy model is also proposed. A MPC algorithm suitable for systems with discrete inputs
is treated. The benefits of the MPC algorithm employing the hybrid fuzzy model are verified on a batch-reactor simulation
example: a comparison between the proposed modern intelligent (fuzzy) approach and a classic (linear) approach was made. It
was established that the MPC algorithm employing the proposed hybrid fuzzy model clearly outperforms the approach where a
hybrid linear model is used, which justifies the usability of the hybrid fuzzy model. The hybrid fuzzy formulation introduces
a powerful model that can faithfully represent hybrid and nonlinear dynamics of systems met in industrial practice, therefore,
this approach demonstrates a significant advantage for MPC resulting in a better control performance. 相似文献
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在模糊控制的过程中,通过加入预测信息来改善控制性能。数学仿真和实时控制的结果表明,预测校正的加入,使控制器的性能得到了明显了改善。 相似文献