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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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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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提出了基于小波变换的非线性广义预测控制算法。预测模型采用Hammerstein模型,对于其静态非线性部分采用小波网络来辨识,动态线性部分用最小二乘法来辨识。这种辨识方法比传统的多项式拟合的模型误差要小得多。基于这种预测模型广义预测控制器弥补了传统广义预测控制的模型失配问题。以CSTR为例对所设计的控制器进行仿真研究,结果表明控制器能够取得良好的控制效果。 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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Regarding to the variations of the load and unmodeled dynamic, robot manipulators are known as a nonlinear dynamic system. Overcoming such problems like uncertainties and nonlinear characteristics in the model of two-link manipulator is the principal goal of this paper. To approach to this aim, a neural network is combined with a linear robust control in which the result has the advantages of, the first, approximated nonlinear elements and the second, the guaranteed robustness. To design the proposed controller, at first, multivariable feedback linearization is employed to convert the nonlinear model to linear one. Second, the unknown parameters of the system are identified by neural network based on a new proposed learning rule. Third, Mixed linear feedback-H?∞? robust control method is proposed to stabilize the closed loop system. The closed loop system based on the proposed controller is analyzed and some numerical simulations are performed. Results show suitable responses of the closed loop system. 相似文献
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永磁同步电机高效非线性模型预测控制 总被引:6,自引:0,他引:6
永磁电机控制器要求电机有很强的转速跟踪能力,并且要保证系统参数变化及负荷扰动下系统的鲁棒性. 永磁电机包含很多不确定因素,是强耦合的非线性系统,传统的线性控制器很难对其进行控制. 针对永磁电机的转速控制构造非线性模型预测控制方法. 非线性永磁电机模型通过输入-输出反馈线性化策略解耦成为新的线性系统. 为保证可行解的收敛性,提出一种迭代二次规划方法来处理由输入-输出反馈线性化导致的非线性约束. 仿真结果表明,控制器能有效降低计算负担,具有很好的动态控制性能,能抑制转矩脉动,并保证在参数变化和负荷扰动下控制系统的鲁棒性. 相似文献
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针对利用Wiener模型表达的具有动态非线性的传感器进行系统辨识和性能补偿。将系统分解为动态非线性环节和静态线性环节,利用函数链人工神经网络和遗传算法分别进行系统辨识,通过静态非线性补偿将系统简化为线性系统,再进行动态性能补偿。利用LabVIEW设计虚拟仪器,经过仿真表明该方法是有效的。 相似文献
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基于神经网络与多模型的非线性自适应广义预测解耦控制 总被引:1,自引:0,他引:1
针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性. 相似文献
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针对一类具有特殊模型的非线性系统本文提出了一种新型神经网络预测控制算法。该算法利用线性系统预测控制技术和神经网络的非线性映射及并行处理能力来求实际控制量,避免了解非线性方程和非线性预测控制所需的在线数值寻优计算,减少了计算量和计算时间。仿真结果表明了该算法的何效性。 相似文献
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A recurrent neuro-fuzzy network-based nonlinear long range model predictive control strategy is proposed in this paper. The
process operation is partitioned into several fuzzy operating regions. Within each region, a local linear model is used to
model the process. The global model output is obtained through the centre of gravity defuzzification. Based upon a neuro-fuzzy
network model, a nonlinear model-based predictive controller can be developed by combining several local linear model-based
predictive controllers which usually have analytical solutions. This strategy avoids the time consuming numerical optimisation
procedure, and the uncertainty in convergence to the global optimum which are typically seen in conventional nonlinear model-based
predictive control strategies. Furthermore, control actions obtained based on local incremental models contain integration
actions which can nat-urally eliminate static control offsets. The technique is demonstrated by an application to the modelling
and control of liquid level in a water tank. 相似文献
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LIU Meiqin School of Electrical Engineering Zhejiang University Hangzhou China 《中国科学F辑(英文版)》2006,49(2):137-154
The research on the theory and application of artificial neural networks has achieved a great success over the past two decades. Recently, increasing attention has been paid to recurrent neural networks, which are rich in dynamics, highly parallelizable, and easily implementable with VLSI. Due to these attractive features, RNNs have widely been applied to system identification, control, optimization and associative memories[1]. Stability analysis, which is critical to any applications of R… 相似文献
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Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation 总被引:3,自引:0,他引:3
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future dynamic behavior of the nonlinear process in real time. An efficient training algorithm for the proposed network is developed using automatic differentiation (AD) techniques. By automatically generating Taylor coefficients, the algorithm not only solves the differentiation equations of the network but also produces the sensitivity for the training problem. The same approach is also used to solve the online optimization problem in the predictive controller. The proposed neural network and the nonlinear predictive controller were tested on an evaporation case study. A good model fitting for the nonlinear plant is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The CTRNN trained is used as an internal model in a predictive controller and results in good performance under different operating conditions. 相似文献
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基于神经网络非线性系统的广义预测 总被引:1,自引:0,他引:1
为了对复杂的非线性系统进行广义预测控制 ,避免较长的离线训练 ,采用受控自回归积分滑动平均模型来描述线性子系统 ,用神经网络来逼近非线性子系统 ,利用递推最小二乘法和 Davidon最小二乘法分别作为线性子系统和非线性子系统的在线学习算法 ,建立了一种适合于广义预测控制的非线性系统控制模型。仿真结果证明 ,该模型在非线性系统的广义预测中的有效性 ,在实时控制中具有极其广阔的应用前景。 相似文献