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针对传统预测控制算法在解决非线性系统控制问题时,存在难以建立精确的数学模型、控制精度不高等缺点,提出一种新的非线性系统预测控制方案。以多BP神经网络作为并行预测模型,克服误差积累以及网络规模庞大的缺点;运用粒子群优化(PSO)算法完成非线性预测控制的滚动优化。仿真表明,该方案的控制效果比常规动态矩阵控制效果有所提高,该方案是可行和有效的。 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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随着系统复杂度的提高和对象不确定性因素的增加,为克服线性PID动态性能和稳态性能差的缺陷,分析了非线性PID控制器各控制参数对误差的理想变化过程,构造非线性PID控制器。由于增益参数大量增加,传统参数优化方法不再适用,在分析蚁群算法的基础上,提出了基于感知自适应蚁群算法,并加入模糊自适应信息素更新机制,用于优化非线性PID控制器的设计方法。通过仿真实验将该控制器与基于蚁群算法的非线性PID控制器和基于蚁群算法、Z-N法的PID控制器进行对比,并对控制性能和收敛性能进行了分析,结果表明该算法有效克服了传统蚁群算法收敛速度较慢、容易陷入局部最优而停滞的缺陷,该控制器具有更好的动态性能和稳态性能。 相似文献
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In this paper, a feedforward neural network with sigmoid hidden units is used to design a neural network based iterative learning controller for nonlinear systems with state dependent input gains. No prior offline training phase is necessary, and only a single neural network is employed. All the weights of the neurons are tuned during the iteration process in order to achieve the desired learning performance. The adaptive laws for the weights of neurons and the analysis of learning performance are determined via Lyapunov‐like analysis. A projection learning algorithm is used to prevent drifting of weights. It is shown that the tracking error vector will asymptotically converges to zero as the iteration goes to infinity, and the all adjustable parameters as well as internal signals remain bounded. 相似文献
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The problem of traffic congestion in modern day motorways calls for the design and implementation of efficient control strategies. It is argued in this paper that in order to have efficient, generic, and systematic solutions to a wide range of traffic control problems, macroscopic motorway traffic flow models in state-space form, that are relevant for the control problem and computationally non-intensive, are most appropriate. Such models allow the exploitation of available powerful, systematic, and theoretically supported automatic control concepts. Based on these concepts an Extended Kalman Filter for traffic state estimation, a multivariable LQI controller for coordinated ramp metering on a motorway stretch, and an integrated optimal control strategy for motorway networks are shortly presented. The criteria of a model's relevance for a given traffic control problem and its computational requirements are subsequently examined. Finally, the application of an advanced coordinated ramp metering control strategy, based on the optimal control approach, to the ring-road of Amsterdam, The Netherlands, is provided as an illustrative example. 相似文献
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计算机网络的飞速发展带来了日益严重的网络拥塞问题,采用适当的方法进行拥塞控制已成为当前研究的热点。文章在对网络拥塞及拥塞控制分析的基础上,详细介绍遗传算法、人工神经网络、模糊逻辑等智能优化算法在网络拥塞控制中的应用,并分析了各种算法的优缺点。 相似文献
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基于最优控制理论及线性近似方法,研究具有众多非线性子系统关联耦合组成的大型混沌网络系统的控制问题。由于集中控制结构非常复杂, 工程设计困难,且难以实现,故从可靠性和经济性出发,建立了分散控制策略。仿真结果验证了这一理论的正确性。 相似文献
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In this paper, performance oriented control laws are synthesized for a class of single‐input‐single‐output (SISO) n‐th order nonlinear systems in a normal form by integrating the neural networks (NNs) techniques and the adaptive robust control (ARC) design philosophy. All unknown but repeat‐able nonlinear functions in the system are approximated by the outputs of NNs to achieve a better model compensation for an improved performance. While all NN weights are tuned on‐line, discontinuous projections with fictitious bounds are used in the tuning law to achieve a controlled learning. Robust control terms are then constructed to attenuate model uncertainties for a guaranteed output tracking transient performance and a guaranteed final tracking accuracy. Furthermore, if the unknown nonlinear functions are in the functional ranges of the NNs and the ideal NN weights fall within the fictitious bounds, asymptotic output tracking is achieved to retain the perfect learning capability of NNs. The precision motion control of a linear motor drive system is used as a case study to illustrate the proposed NNARC strategy. 相似文献
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网络控制系统(NCS)是基于网络的分布式控制系统。文章结合网络调度和系统控制,以网络的可调度性和系统稳定性为约束条件,给出了NCS的性能优化模型,并利用基于Matlab/Simulink的实时控制系统仿真软件包True Time对其进行了仿真。仿真结果表明:该方法既提高了控制系统的性能,又优化了网络调度性能,提高了网络资源的利用率。 相似文献
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本文提出一种用自组织自学习适应思想解决非线性动力系统控制问题的新方法。在每个小区域感受野,可以把非线性系统近似展开为线性,由神经元执行控制。各神经元的凝视点,感受野和功能由自组织自学习自适应方法进行调节。大量仿真结果验证了本方法的正确性和实用性。 相似文献
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一类非匹配不确定性非线性系统子波网络稳定自适应控制 总被引:3,自引:1,他引:2
针对一类非匹配不确定性非线性系统,提出一种基于子波网络的稳定自适应跟踪控制方案,对子波网络逼近误差界进行自适应处理,不需要逼近误差界的先验信息,仿真结果表明所提出的方案是有效的。 相似文献
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一种新的非线性规划神经网络模型 总被引:1,自引:0,他引:1
提出一种新型的求解非线性规划问题的神经网络模型.该模型由变量神经元、Lagrange乘子神经元和Kuhn-Tucker乘子神经元相互连接构成.通过将Kuhn-Tucker乘子神经元限制在单边饱和工作方式,使得在处理非线性规划问题中不等式约束时不需要引入松弛变量,避免了由于引入松弛变量而造成神经元数目的增加,有利于神经网络的硬件实现和提高神经网络的收敛速度.可以证明,在适当的条件下,文中提出的神经网络模型的状态轨迹收敛到与非线性规划问题的最优解相对应的平衡点. 相似文献
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Defeng He 《IEEE/CAA Journal of Automatica Sinica》2017,4(3):526-533
The paper presents a new dual-mode nonlinear model predictive control (NMPC) scheme for continuous-time nonlinear systems subject to constraints on the state and control. The idea of control Lyapunov functions for nonlinear systems is used to compute the terminal regions and terminal control laws with some free-parameters in the dual-mode NMPC framework. The parameters of the terminal controller are selected offline to estimate the terminal region as large as possible; and the parameters are optimized online to gain optimality of the terminal controller with respect to given cost functions. Then a dual-mode NMPC algorithm with varying time-horizon is formulated for the constrained system. Recursive feasibility and closed-loop stability of this NMPC are established. The example of a spring-cart is used to demonstrate the advantages of the presented scheme by comparing to the dual-mode NMPC via the linear quadratic regulator (LQR) method. 相似文献
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针对传统的PID神经网络(PIDNN)应用范围受限及积分误差规则难以获取的问题.为实现对非线性多变量系统的有效控制,拓展神经网络控制系统的应用范围,提出了基于改进型粒子群算法在PID神经网络控制系统设计中的解决方案,取代了传统的BP反向传播算法.仿真结果表明,与传统的PIDNN相比,系统的稳定性、鲁棒性及精确性都有了明... 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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神经网络控制的应用和发展 总被引:2,自引:0,他引:2
神经网络控制的应用和发展TheApplicationandDevelopmentofNeuralNetworkControl●费峻涛鲍远律朱民杨梅FeiJuntaoBaoYuanluZhuMinYangMei1引言神经网络首先用于解决模式识别问题。随... 相似文献
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基于混沌变量优化的神经网络PID控制 总被引:1,自引:0,他引:1
对神经网络模型辨识器的输入量Y(k),u(k)进行归一化处理,一种规范化PID控制方法作为控制器。采用Logistic映射构造多个不同的混沌变量,应用到神经网络PID参数域中,根据控制系统性能指标进行混沌寻优,获得近似最优解后,再通过时变因子Z(t)在近似最优解的附近继续混沌局部寻优。仿真实验表明该方案是有效的。 相似文献