共查询到19条相似文献，搜索用时 234 毫秒
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针对铝电解控制系统的大滞后、大惯性以及动态特性随工况变化的不确定性等特点,本文提出了ADRC技术的氧化铝浓度控制方案。该控制方案能使铝电解过程很快进入稳态,超调量较小,提高了铝电解过程的动态和稳态性能。 相似文献
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大型建筑内中央空调出风口的自适应优化分布设计可以提高制冷性能,同时减少能耗。由于建筑物的不规则形,空调出风口分布节点阵列难以实现全网能量均衡优化。传统方法采用自适应模糊神经控制系统控制出风口的节点分布,然而算法自组织学习能力差,空调耗能难以实现最优控制。提出一种基于模糊自适应神经网络系统控制的空调节点自适应优化分布方案,采用全网能量均衡控制策略,优化空调出风口节点自适应分布。算法能实现以最小的耗能达到最优的制冷效果,实现全网络能量均衡控制,达到节能减排的要求。实验结果证明,算法能提高每个出风口节点的应用效能,降低能耗的同时增强了空调应用性能。 相似文献
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本文采用神经网络与自适应神经网络模糊推理（Anfis）工具对一级倒立摆进行控制。在神经网络控制的基础上,将神经网络控制与模糊控制相结合,利用神经网络学习模糊控制规则数据,对模糊神经控制器进行训练。实验表明,当模型参数改变及干扰作用时,自适应神经网络模糊推理系统有良好的自适应能力,能使倒立摆小车抵抗外界干扰并能较准确地到达预定位置。 相似文献
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《科技通报》2015,(10)
研究非线性系统的鲁棒性,在大扰动条件下,提高系统的稳定控制性能。传统的控制方法采用PID神经网络控制,在参数自适应过程中产生控制偏差。提出一种基于单神经元纠偏控制的非线性系统鲁棒性改进方法。控制结构是一个三层前向神经元网络,采用单神经元纠偏控制,自适应调节神经元输入输出层权重,给定模型的不确定性分为参数的不确定性和未建模的动态特性不确定性,由此得到偏移控制非线性小扰动方程,进行控制系统鲁棒性和稳健性证明。仿真结果表明,采用该算法实现对非线性系统的控制,自适应调节时间短,超调量小,纠偏性能较好,自适应跟踪控制性能优越,误差减少,控制精度较高,鲁棒性较优。 相似文献
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铝电解过程是一个非常复杂的非线性、时变和大滞后的工业过程体系,因而采用常规的控制方法很难达到良好的控制效果。针对此问题本文提出了采用改进的Elman神经网络对其进行建模,介绍了改进Elman神经网络结构及其学习算法;分析了影响氧化铝浓度的主要因素,并根据实际情况确定了输入层和中间隐层的维数,从而确定了模型的结构。通过对现场采集的数据进行了仿真,仿真结果表明：与常规Elman相比,神经网络收敛速度和稳定性上都有明显提高,得到了令人满意的结果。 相似文献
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在针对矿井提升机的PLC控制系统中,采用传统PID控制虽能达到相应的控制要求,但因其控制的响应时间长、控制精度低、稳定性差等缺陷,不能广泛应用于有高精度要求的控制系统中。本文将模糊控制与自适应PID控制结合起来,设计了模糊自适应PID控制器。利用模糊推理方法实现对PID参数的在线自整定,进一步完善PID控制器的性能,提高系统的控制精度。MATLAB/SIMULIK下的仿真结果表明该方法的控制效果优于常规的PID控制,并能消除模糊控制稳态误差较大、控制精度低等缺点。 相似文献
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多源数据辨识系统广泛应用在机载数据辨识控制、大型机械设备故障诊断和云存储系统数据库集成等领域。对多源数据的辨识系统并行查询和数据调度中,因数据的静态非线性测量过程影响了查询效益,需要对辨识系统并行查询链路进行扩展。提出一种基于振幅调节Fourier变换边缘逆理论的辨识系统并行查询扩展算法。进行多源数据辨识系统基本构造和模型设计,提取查询特征信息,采用RBF神经网络系统进行多源数据输入自适应学习,构建神经网络辨识系统的边缘逆向量,采用边缘逆理论进行振幅调节Fourier变换实现多源查询数据的状态重组,实现查询链路扩展设计改进。仿真结果表明,该算法提高了辨识系统的查询通道的链路相位,多源数据调度的时间成本及空间成本大幅降低,加速比提高,算法将在系统状态识别、机械故障智能诊断等领域具有较高的应用价值。 相似文献
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In this paper, a modified adaptive neural network for the compensation of deadzone is described, and simulated on a hydraulic positioning system, in which the dynamic model is separated into a series of connection of a nonlinear (deadzone) subsystem and a linear plant. The proposed approach uses two neural networks. One is the radial basis function (RBF) neural network, which is used for identifying parameters of deadzone. Based on the penalty function used in optimization theory, a multi-objective cost function with constraint is adopted to provide the best deadzone approximation. The result is used to train the other neural network for the inverse compensation of deadzone. The RBF neural network also generates the parameters of the linear plant for the design of an adaptive controller. A convergence analysis for the network training process is also presented. 相似文献
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《Journal of The Franklin Institute》2019,356(12):5944-5960
Due to the unknown system structure of the froth flotation process and frequent fluctuations in production conditions, design of control strategy is a challenging problem. As a result, manual operation is still widely applied in practice by observing froth image features. However, since the manual observation is subjective and the production conditions are time-varying, the manual operation cannot make decisions quickly and accurately. In this paper, a data-driven-based adaptive fuzzy neural network control strategy is developed to implement the automatic control of the antimony flotation process. The strategy is composed of fuzzy neural network (FNN) controllers, a data-driven model, and an on-line adaptive algorithm. The FNN is constructed to derive the control laws of the reagent dosages. The parameters of the FNN controllers are tuned by gradient descent algorithm. To obtain the real-time error feedback information, the data-driven model is established, which integrates the long short term memory (LSTM) network and radial basis function neural network (RBFNN). The LSTM network is utilized as a primary model, and the RBFNN is used as an error compensation model. To handle the challenges of the frequent fluctuations in the production conditions, the on-line adaptive algorithm is proposed to tune the parameters of the FNN controllers. Simulations and experiments are carried out in a real-world antimony flotation plant in China. The results demonstrate that the proposed adaptive fuzzy neural network control strategy produces better control performance than the other two existing methods. 相似文献
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《Journal of The Franklin Institute》2023,360(9):6296-6320
The objective of this article is to present an adaptive neural inverse optimal consensus tracking control for nonlinear multi-agent systems (MASs) with unmeasurable states. In the control process, firstly, to approximate the unknown state, a new observer is created which includes the outputs of other agents and their estimated information. The neural network is used to reckon the uncertain nonlinear dynamic systems. Based on a new inverse optimal method and the construction of tuning functions, an adaptive neural inverse optimal consensus tracking controller is proposed, which does not depend on the auxiliary system, thus greatly reducing the computational load. The developed scheme not only insures that all signals of the system are cooperatively semiglobally uniformly ultimately bounded (CSUUB), but also realizes optimal control of all signals. Eventually, two simulations provide the effectiveness of the proposed scheme. 相似文献
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In this paper, the subspace identification based robust fault prediction method which combines optimal track control with adaptive neural network compensation is presented for prediction the fault of unknown nonlinear system. At first, the local approximate linear model based on input-output of unknown system is obtained by subspace identification. The optimal track control is adopted for the approximate model with some unknown uncertainties and external disturbances. An adaptive RBF neural network is added to the track control in order to guarantee the robust tracking ability of the observation system. The effect of the system nonlinearity and the error caused by subspace modeling can be overcome by adaptive tuning of the weights of the RBF neural network online without any requisition of constraint or matching conditions. The stability of the designed closed-loop system is thus proved. A density function estimation method based on state forecasting is then used to judge the fault. The proposed method is applied to fault prediction of model-unknown fighter F-8II of China airforce and the simulation results show that the proposed method can not only predict the fault, but has strong robustness against uncertainties and external disturbances. 相似文献
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电厂主汽温被控对象是一个大惯性、大迟延、非线性且对象变化的系统,基于BP神经网络的PID控制,利用神经网络的自学习、非线性和不依赖模型等特性实现PID参数的在线自整定,充分利用PID和神经网络的优点。用一个多层前向神经网络,采用反向传播算法,依据控制要求实时输出Kp、Ki、Kd,依次作为PID控制器的实时参数,代替传统PID参数靠经验的人工整定和工程整定,以达到对大迟延主汽温系统的良好控制。对这样一个系统在MATLAB平台上进行仿真研究,仿真结果表明基于BP神经网络的自整定PID控制具有良好的自适应能力和自学习能力,对大迟延和变对象的系统可取得良好的控制效果。 相似文献