共查询到19条相似文献,搜索用时 140 毫秒
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针对传统的广义预测控制(GPC)算法必须有精确的数学模型,计算量很大,不适合实时性要求高的系统。网络控制系统在复杂的工业控制领域应用中,受网络时延的影响,导致了控制系统性能的下降甚至不稳定,给控制系统的分析和设计带来诸多问题。该文采用基于支持向量机的广义预测控制方法,将支持向量机作为预测模型,对控制对象模型进行预测,再加之广义预测控制算法和队列机制提出不基于对象模型且实时性高的广义预测控制快速算法,并且设计了支持向量机非线性建模和非线性广义预测控制队列机制算法,并相应做出了仿真。最后的仿真结果表明基于广义预测控制算法的网络控制系统具有良好的控制效果和稳定性。 相似文献
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针对工业过程中普遍存在的非线性被控对象,提出了一种基于支持向量机(SVM)逆系统的广义预测控制算法。该方法根据广义预测控制基于预测模型的特点,将基于支持向量机系统辨识的方法应用于逆系统构建和广义预测控制。该方法利用SVM强大的非线性映射能力离线辨识被控非线性系统的α阶逆模型,并将辨识出的逆模型连接在原被控统之前形成一个α阶纯延时伪线性系统。然后采用广义预测控制(GPC)算法实现对构造出的伪线性系统的预测控制。仿真实验表明了该算法的有效性和优越性。 相似文献
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参数的优化选择对支持向量回归机的预测精度和泛化能力影响显著,鉴于此,提出一种多智能体粒子群算法(MAPSO)寻优其参数的方法,并建立MAPSO支持向量回归模型,用于非线性系统的模型预测控制,推导出最优控制率.采用该算法对非线性系统进行仿真,并与基于粒子群算法、基于遗传算法优化支持向量回归机的模型预测控制方法和RBF神经网络的预测控制方法进行比较,结果表明,所提出的算法具有更好的控制性能,可以有效应用于非线性系统控制中. 相似文献
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支持向量机在网络广义预测控制中的应用 总被引:2,自引:2,他引:0
在网络控制系统的研究中,支持向量机(SVM) 在网络广义预测控制中的应用具有良好控制效果和稳定性.为提高网络性能,对网络控制系统进行模型预测,并将SVM作为广义预测控制(GPC) 算法中的预测模型,采用支持向量机的广义预测控制算法.进行预估技术和队列机制,对被控对象选择最合适的控制信号,降低了时延对网络控制系统的危害性,并通过Matlab上仿真结果表明,与PID控制相比较,基于SVM的GPC算法在网络控制方面超调量较小,调整时间较短,控制效果更好. 相似文献
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S. Iplikci 《国际强度与非线性控制杂志
》2006,16(17):843-862
》2006,16(17):843-862
In this study, we propose a novel control methodology that introduces the use of support vector machines (SVMs) in the generalized predictive control (GPC) scheme. The SVM regression algorithms have extensively been used for modelling nonlinear systems due to their assurance of global solution, which is achieved by transforming the regression problem into a convex optimization problem in dual space, and also their higher generalization potential. These key features of the SVM structures lead us to the idea of employing a SVM model of an unknown plant within the GPC context. In particular, the SVM model can be employed to obtain gradient information and also it can predict future trajectory of the plant output, which are needed in the cost function minimization block. Simulations have confirmed that proposed SVM‐based GPC scheme can provide a noticeably high control performance, in other words, an unknown nonlinear plant controlled by SVM‐based GPC can accurately track the reference inputs with different shapes. Moreover, the proposed SVM‐based GPC scheme maintains its control performance under noisy conditions. Copyright © 2006 John Wiley & Sons, Ltd. 相似文献
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基于支持向量机的激光焊接过程辨识与控制 总被引:1,自引:1,他引:0
激光焊接过程数学模型足一个较强非线性的数学模型,通常的线性辨识方法无法得到它精确的数学模型.支持向量机作为一种新的机器学习方法,具有较强的非线性拟合能力,应用支持向量机非线性系统回归建模方法,辨识出具有典型非线性特性的焊接过程模型,并采用预测控制算法对焊接过程进行控制.实验证明,支持向量机对非线性系统具有很好的拟合效果,基于支持向量机的预测控制具有较好的非线性控制效果. 相似文献
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基于ANN的非线性系统GPC算法及仿真研究 总被引:2,自引:0,他引:2
将神经网络(ANN)技术应用于常规GPC算法,设计了基于ANN的非线性系统GPC结构方案,并对其控制原理和控制算法进行研究,基于ANN高度非线性映射等特性,运用数字仿真方法,对所设计的控制结构方案进行仿真研究,仿真结果显示,基于ANN的非线性系统GPC结构方案合理可行,并取得了满意的控制效果. 相似文献
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本文在广义预测控制(GPC)算法[1,2]的基础上提出了改进算法.并对改进的算法[4]进
行了稳定性分析,得到了简练的特征多项式,然后进一步证明其特征多项式中的参数可以直接
用预测模型、被控对象模型和控制参数代替,不必另行计算.其结论仍适用于原有的GPC算
法[1,2,5]的稳定性分析. 相似文献
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基于在线最小二乘支持向量机的广义预测控制 总被引:5,自引:0,他引:5
This paper proposes a practical generalized predictive control(GPC)algorithm based on online least squares support vector machines(LS-SVM)which can deal with nonlinear systems effectively.At each sampling period the algorithm recursively modifies the model by adding a new data pair and deleting the least important one out of the consideration on realtime property.The data pair deleted is determined by the absolute value of lagrange multiplier from last sampling period.The paper gives the recursive algorithm of model parameters when adding a new data pair and deleting an existent one,respectively,and thus the inversion of a large matrix is avoided and the memory can be controlled by the algorithm entirely.The nonlinear LS-SVM model is applied in GPC algorithm at each sampling period.The experiments of generalized predictive control on pH neutralizing process show the effectiveness and practicality of the proposed algorithm. 相似文献
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In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems. 相似文献
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Shouling He 《Applied Artificial Intelligence》2013,27(2):151-167
This paper proposes a neural-based predictive control algorithm for online control of a force-acting industrial hydraulic actuator. In the algorithm, a multilayer feedforward neural network is employed to modeling the highly nonlinear hydraulic actuator. The nonlinear neural model is instantaneously linearized at each sampling point. Estimated parameters from the linearized model are used in the generalized predictive control (GPC) algorithm to control the contact force. Simulation and experimental results show that the neural-based predictive controller can adapt to different environments and keep the contact force in a desired value despite high nonlinearity and uncertainty in the hydraulic actuator system. 相似文献