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神经网络 26

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Design, analysis, and neural control of a bionic parallel mechanism

《机械工程前沿（英文）》 2021年第16卷第3期页码 468-486 doi: 10.1007/s11465-021-0640-8

摘要： Although the torso plays an important role in the movement coordination and versatile locomotion of mammals, the structural design and neuromechanical control of a bionic torso have not been fully addressed. In this paper, a parallel mechanism is designed as a bionic torso to improve the agility, coordination, and diversity of robot locomotion. The mechanism consists of 6-degree of freedom actuated parallel joints and can perfectly simulate the bending and stretching of an animal’s torso during walking and running. The overall spatial motion performance of the parallel mechanism is improved by optimizing the structural parameters. Based on this structure, the rhythmic motion of the parallel mechanism is obtained by supporting state analysis. The neural control of the parallel mechanism is realized by constructing a neuromechanical network, which merges the rhythmic signals of the legs and generates the locomotion of the bionic parallel mechanism for different motion patterns. Experimental results show that the complete integrated system can be controlled in real time to achieve proper limb–torso coordination. This coordination enables several different motions with effectiveness and good performance.

关键词： neural control behavior network rhythm motion pattern

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PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

《机械工程前沿（英文）》 2010年第5卷第4期页码 418-422 doi: 10.1007/s11465-010-0117-7

摘要： Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

关键词： PID neural network membrane structure

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Frequency domain a9ctive vibration control of a flexible plate based on neural networks

Jinxin LIU, Xuefeng CHEN, Zhengjia HE

《机械工程前沿（英文）》 2013年第8卷第2期页码 109-117 doi: 10.1007/s11465-013-0252-z

摘要：

A neural-network (NN)-based active control system was proposed to reduce the low frequency noise radiation of the simply supported flexible plate. Feedback control system was built, in which neural network controller (NNC) and neural network identifier (NNI) were applied. Multi-frequency control in frequency domain was achieved by simulation through the NN-based control systems. A pre-testing experiment of the control system on a real simply supported plate was conducted. The NN-based control algorithm was shown to perform effectively. These works lay a solid foundation for the active vibration control of mechanical structures.

关键词： active vibration control (AVC) neural network (NN) low frequency noise frequency domain control multi-frequency control

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Neural network control for earthquake structural vibration reduction using MRD

Khaled ZIZOUNI, Leyla FALI, Younes SADEK, Ismail Khalil BOUSSERHANE

《结构与土木工程前沿（英文）》 2019年第13卷第5期页码 1171-1182 doi: 10.1007/s11709-019-0544-4

摘要： Structural safety of building particularly that are intended for exposure to strong earthquake loads are designed and equipped with high technologies of control to ensure as possible as its protection against this brutal load. One of these technologies used in the protection of structures is the semi-active control using a Magneto Rheological Damper device. But this device need an adequate controller with a robust algorithm of current or tension adjustment to operate which is further discussed in the following of this paper. In this study, a neural network controller is proposed to control the MR damper to eliminate vibrations of 3-story scaled structure exposed to Tōhoku 2011 and Boumerdès 2003 earthquakes. The proposed controller is derived from a linear quadratic controller designed to control an MR damper installed in the first floor of the structure. Equipped with a feedback law the proposed control is coupled to a clipped optimal algorithm to adapt the current tension required to the MR damper adjustment. To evaluate the performance control of the proposed design controller, two numerical simulations of the controlled structure and uncontrolled structure are illustrated and compared.

关键词： MR damper semi-active control earthquake vibration neural network linear quadratic control

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Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

《机械工程前沿（英文）》 2010年第5卷第2期页码 149-156 doi: 10.1007/s11465-010-0008-y

摘要： With the automation development of manufacturing processes, artificial intelligence technology has been gradually employed to increase the automation and intelligence degree in quality control using statistical process control (SPC) method. In this paper, an SPC method based on a fuzzy adaptive resonance theory (ART) neural network is presented. The fuzzy ART neural network is applied to recognize the special disturbance of the manufacturing processes based on the classification on the histograms, which shows that the fuzzy ART neural network can adaptively learn the features of the histograms of the quality parameters in manufacturing processes. As a result, the special disturbance can be automatically detected when a feature of the special disturbance starts to appear in the histograms. At the same time, combined with spectrum analysis of the autoregressive model of quality parameters, the fuzzy ART neural network can also be utilized to adaptively detect the abnormal patterns in the control chart.

关键词： statistical process control (SPC) fuzzy adaptive resonance theory (ART) histogram control chart time series analysis

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Power maximization of variable-speed variable-pitch wind turbines using passive adaptive neural faulttolerant control

Hamed HABIBI, Hamed RAHIMI NOHOOJI, Ian HOWARD

《机械工程前沿（英文）》 2017年第12卷第3期页码 377-388 doi: 10.1007/s11465-017-0431-4

摘要：

Power maximization has always been a practical consideration in wind turbines. The question of how to address optimal power capture, especially when the system dynamics are nonlinear and the actuators are subject to unknown faults, is significant. This paper studies the control methodology for variable-speed variable-pitch wind turbines including the effects of uncertain nonlinear dynamics, system fault uncertainties, and unknown external disturbances. The nonlinear model of the wind turbine is presented, and the problem of maximizing extracted energy is formulated by designing the optimal desired states. With the known system, a model-based nonlinear controller is designed; then, to handle uncertainties, the unknown nonlinearities of the wind turbine are estimated by utilizing radial basis function neural networks. The adaptive neural fault tolerant control is designed passively to be robust on model uncertainties, disturbances including wind speed and model noises, and completely unknown actuator faults including generator torque and pitch actuator torque. The Lyapunov direct method is employed to prove that the closed-loop system is uniformly bounded. Simulation studies are performed to verify the effectiveness of the proposed method.

关键词： wind turbine nonlinear model maximum power tracking passive fault tolerant control adaptive neural control

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A modified neural learning algorithm for online rotor resistance estimation in vector controlled induction

A. CHITRA,S. HIMAVATHI

《能源前沿（英文）》 2015年第9卷第1期页码 22-30 doi: 10.1007/s11708-014-0339-1

摘要： Online estimation of rotor resistance is essential for high performance vector controlled drives. In this paper, a novel modified neural algorithm has been identified for the online estimation of rotor resistance. Neural based estimators are now receiving active consideration as they have a number of advantages over conventional techniques. The training algorithm of the neural network determines its learning speed, stability, weight convergence, accuracy of estimation, speed of tracking and ease of implementation. In this paper, the neural estimator has been studied with conventional and proposed learning algorithms. The sensitivity of the rotor resistance change has been tested for a wide range of variation from -50% to+50% on the stability of the drive system with and without estimator. It is quiet appealing to settle with optimal estimation time and error for the viable realization. The study is conducted extensively for estimation and tracking. The proposed learning algorithm is found to exhibit good estimation and tracking capabilities. Besides, it reduces computational complexity and, hence, more feasible for practical digital implementation.

关键词： neural networks back propagation (BP) rotor resistance estimators vector control induction motor

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QPSO-ILF-ANN-based optimization of TBM control parameters considering tunneling energy efficiency

《结构与土木工程前沿（英文）》 2023年第17卷第1期页码 25-36 doi: 10.1007/s11709-022-0908-z

摘要： In recent years, tunnel boring machines (TBMs) have been widely used in tunnel construction. However, the TBM control parameters set based on operator experience may not necessarily be suitable for certain geological conditions. Hence, a method to optimize TBM control parameters using an improved loss function-based artificial neural network (ILF-ANN) combined with quantum particle swarm optimization (QPSO) is proposed herein. The purpose of this method is to improve the TBM performance by optimizing the penetration and cutterhead rotation speeds. Inspired by the regularization technique, a custom artificial neural network (ANN) loss function based on the penetration rate and rock-breaking specific energy as TBM performance indicators is developed in the form of a penalty function to adjust the output of the network. In addition, to overcome the disadvantage of classical error backpropagation ANNs, i.e., the ease of falling into a local optimum, QPSO is adopted to train the ANN hyperparameters (weight and bias). Rock mass classes and tunneling parameters obtained in real time are used as the input of the QPSO-ILF-ANN, whereas the cutterhead rotation speed and penetration are specified as the output. The proposed method is validated using construction data from the Songhua River water conveyance tunnel project. Results show that, compared with the TBM operator and QPSO-ANN, the QPSO-ILF-ANN effectively increases the TBM penetration rate by 14.85% and 13.71%, respectively, and reduces the rock-breaking specific energy by 9.41% and 9.18%, respectively.

关键词： tunnel boring machine control parameter optimization quantum particle swarm optimization artificial neural network tunneling energy efficiency

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基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制 Research

Fan XU, Jin WANG, Guo-dong LU

《信息与电子工程前沿（英文）》 2018年第19卷第11期页码 1316-1327 doi: 10.1631/FITEE.1601707

摘要：针对基座参数欠精确环境下双机械臂刚体夹持系统的自适应调控问题进行研究。提出一种自适应神经鲁棒控制器，能同时解决基座参数欠精确、系统内力、建模不确定性、关节摩擦以及外部干扰等多种问题。该控制器采用一个径向基神经网络来逼近系统包括非预期内力在内的全部动力学部分。结合仿真实验和分析，该控制器能有效保证轨迹跟踪误差渐进收敛于0，并保持内力在可接受范围。在自适应调节机制下，该方法能对系统中双机械臂进一步在线精确标定。为保证系统全局稳定性，该控制器建立定制化鲁棒补偿，结合李雅普诺夫理论，证明该控制器在基座欠精确以及其他多种不确定环境下的鲁棒性。

关键词：协同机械臂；神经网络；欠精确基座平移坐标；自适应控制；鲁棒控制

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高频真空木材干燥的模糊神经网络控制方法研究

姜滨,孙丽萍,曹军,周正

《中国工程科学》 2014年第16卷第4期页码 17-20

摘要：

高频真空木材干燥是一种干燥速度快、能源消耗低、环境污染小的新型联合干燥技术。在木材高频真空联合干燥过程的理论分析基础上，针对神经网络方法建立的木材干燥模型，设计了木材干燥模糊控制器和模糊神经网络控制器。对模糊控制和模糊神经网络两种控制方法进行了仿真实验，结果表明模糊神经网络方法控制效果更好，如温度上升快，控制精度高，稳定性好。模糊神经网络控制方法对实现木材干燥过程的全自动控制具有重要研究意义。

关键词：高频真空木材干燥模糊神经网络

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Comparative study of various artificial intelligence approaches applied to direct torque control of induction

Moulay Rachid DOUIRI, Mohamed CHERKAOUI

《能源前沿（英文）》 2013年第7卷第4期页码 456-467 doi: 10.1007/s11708-013-0264-8

摘要： In this paper, three intelligent approaches were proposed, applied to direct torque control (DTC) of induction motor drive to replace conventional hysteresis comparators and selection table, namely fuzzy logic, artificial neural network and adaptive neuro-fuzzy inference system (ANFIS). The simulated results obtained demonstrate the feasibility of the adaptive network-based fuzzy inference system based direct torque control (ANFIS-DTC). Compared with the classical direct torque control, fuzzy logic based direct torque control (FL-DTC), and neural networks based direct torque control (NN-DTC), the proposed ANFIS-based scheme optimizes the electromagnetic torque and stator flux ripples, and incurs much shorter execution times and hence the errors caused by control time delays are minimized. The validity of the proposed methods is confirmed by simulation results.

关键词： adaptive neuro-fuzzy inference system (ANFIS) artificial neural network direct torque control (DTC) fuzzy logic induction motor

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时滞系统的辨识及NARMA模型的修正

王冬青

《中国工程科学》 2006年第8卷第2期页码 39-43

摘要：

对现有神经网络对非线性时滞系统的时滞辨识方法进行了补充说明和分析，同时指出现有的NARMA模型修正方法对时滞系统的不当之处。以时滞系统神经网络预测控制为例，介绍了NARMA模型的正确修正方法，仿真证明了所提出的修正方法能获得好的控制性能及抗干扰能力。

关键词：辨识 NARMA模型神经网络预测控制

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Multiscale computation on feedforward neural network and recurrent neural network

Bin LI, Xiaoying ZHUANG

《结构与土木工程前沿（英文）》 2020年第14卷第6期页码 1285-1298 doi: 10.1007/s11709-020-0691-7

摘要： Homogenization methods can be used to predict the effective macroscopic properties of materials that are heterogenous at micro- or fine-scale. Among existing methods for homogenization, computational homogenization is widely used in multiscale analyses of structures and materials. Conventional computational homogenization suffers from long computing times, which substantially limits its application in analyzing engineering problems. The neural networks can be used to construct fully decoupled approaches in nonlinear multiscale methods by mapping macroscopic loading and microscopic response. Computational homogenization methods for nonlinear material and implementation of offline multiscale computation are studied to generate data set. This article intends to model the multiscale constitution using feedforward neural network (FNN) and recurrent neural network (RNN), and appropriate set of loading paths are selected to effectively predict the materials behavior along unknown paths. Applications to two-dimensional multiscale analysis are tested and discussed in detail.

关键词： multiscale method constitutive model feedforward neural network recurrent neural network

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A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

《机械工程前沿（英文）》 2022年第17卷第2期 doi: 10.1007/s11465-022-0673-7

摘要： Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.

关键词： deep reinforcement learning hyper parameter optimization convolutional neural network fault diagnosis

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Automated classification of civil structure defects based on convolutional neural network

Pierclaudio SAVINO, Francesco TONDOLO

《结构与土木工程前沿（英文）》 2021年第15卷第2期页码 305-317 doi: 10.1007/s11709-021-0725-9

摘要： Today, the most commonly used civil infrastructure inspection method is based on a visual assessment conducted by certified inspectors following prescribed protocols. However, the increase in aggressive environmental and load conditions, coupled with the achievement of many structures of the life-cycle end, has highlighted the need to automate damage identification and satisfy the number of structures that need to be inspected. To overcome this challenge, this paper presents a method for automating concrete damage classification using a deep convolutional neural network. The convolutional neural network was designed after an experimental investigation of a wide number of pretrained networks, applying the transfer-learning technique. Training and validation were conducted using a database built with 1352 images balanced between “undamaged”, “cracked”, and “delaminated” concrete surfaces. To increase the network robustness compared to images in real-world situations, different image configurations have been collected from the Internet and on-field bridge inspections. The GoogLeNet model, with the highest validation accuracy of approximately 94%, was selected as the most suitable network for concrete damage classification. The results confirm that the proposed model can correctly classify images from real concrete surfaces of bridges, tunnels, and pavement, resulting in an effective alternative to the current visual inspection techniques.

关键词： concrete structure infrastructures visual inspection convolutional neural network artificial intelligence