共查询到19条相似文献,搜索用时 125 毫秒
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多步预测性能指标函数下的神经网络逆动态控制方法 总被引:20,自引:3,他引:17
将预测控制与神经网络逆动态控制相结合,提出了多步预测性能指标函数下的神经网络逆动态控制方法。该方法用多步预测性能指标函数训练神经网络逆动态控制器的权值,使整个系统具有预测控制的特点,有比通常的神经网络逆动态控制快得多的响应速度和更好的响应性能。 相似文献
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针对一类多输入多输出非线性被控对象,利用前向神经网络逼近原系统的逆系统,将其作为控制器,采用预测滚动优化性能指标训练该神经网络逆控制器,以克服干扰和不确定性影响,实现对多变量非线性对象的解耦控制。对某微型锅炉对象进行了控制算法仿真,结果表明,所提出的控制方法能够克服模型误差的影响,实现稳定解耦控制,且易于实现。在仿真过程中通过实验方法建立该锅炉对象的神经网络预测模型,并注意采用泛化方法采集训练样本数据和训练神经网络,以提高神经网络模型的泛化能力。 相似文献
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非线性系统多步预测控制的复合神经网络实现 总被引:11,自引:1,他引:10
提出一种基于神经网络的非线性多步预测控制,采用由线性网络和动态递归神经网络构成的复合神经网络。在此基础上将线性系统的广义预测控制器扩展为非线性系统的多步预测控制器。通过对非线性过程CSTR的仿真表明,该方法的稳定性和鲁棒性明显优于线性DMC预测控制。 相似文献
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该文针对不平滑、多映射动态迟滞非线性系统,提出了一种基于神经网络自适应控制方案.在该方案中,通过利用神经网络来逼近模型误差,避免了目前常用逆模型补偿方案中,需求取复杂逆模型的问题.应用Lyapnov稳定定理,证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内.仿真结果表明,所提出的控制方案能够有效补偿迟滞非线性对系统的影响. 相似文献
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该文针对不平滑、多映射动态迟滞非线性系统,提出了一种基于神经网络自适应控制方案。在该方案中,通过利用神经网络来逼近模型误差,避免了目前常用逆模型补偿方案中,需求取复杂逆模型的问题。应用Lyapnov稳定定理,证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内。仿真结果表明,所提出的控制方案能够有效补偿迟滞非线性对系统的影响。 相似文献
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Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks 总被引:1,自引:0,他引:1
Po-Feng Tsai Ji-Zheng Chu Shi-Shang Jang Shyan-Shu Shieh 《Journal of Process Control》2003,13(5):423-435
Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system. 相似文献
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The prediction of dynamic behavior of the nonlinear time‐varying process plays an important role in predictive control applications. Although neural network algorithms have been intensively researched in modeling and controlling nonlinear systems in recent years, most of them mainly focused on the static dynamics. In this paper, a variable‐structure gradient radial basis function (RBF) network is implemented for nonlinear real‐time model predictive control, which is achieved by the proposed gradient orthogonal model selection (GOMS) algorithm. By learning the gradient message of real‐time updated data in a sling window, the structure and the connecting parameters of the network can be adaptively adjusted to adapt to the time‐varying dynamics. The proposed algorithm is evaluated with Mackey‐Glass chaotic time series prediction. Moreover, the variable structure network achieved by GOMS algorithm is applied as a multi‐step predictor in a ship course‐tracking control study, results demonstrate the applicability and effectiveness of the proposed GOMS algorithm and the variable‐RBF‐network based predictive control strategy. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
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A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed. 相似文献
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为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器+前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。 相似文献
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基于Hammerstein 模型的感应电机变频器调速系统神经网络控制 总被引:1,自引:0,他引:1
针对感应电机变频器调速系统的非线性特点,提出一种基于Hammerstein模型的神经网络控制方法。 Hammerstein模型由静态非线性模块和动态线性模块组成。首先,利用ARMA模型实现对感应电机变频器调速系统的线性动态模块辨识;然后,基于该辨识模型,实现调速系统非线性静态模块神经网络逆模型辨识与系统直接逆控制;最后,针对控制过程中存在的电机负载扰动问题,设计了神经网络直接逆控制器在线学习与控制策略。仿真实验表明,所提出的控制策略可以获得满意的控制效果。 相似文献
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在非线性系统的模糊动力学模型基础上,提出一种模糊神经网络变结构自适应控制器;网络的结构根据非线性系统特性动态构成,基于该网络提出非线性预测器,基于梯度法提出了一种网络参数学习算法,并分析了收敛性及其性质。将网络预测器与参数学习算法相结合,构成自适应控制算法,证明了算法的收敛性。仿真结果证实了算法的有效性。 相似文献