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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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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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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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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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An adaptive sliding mode control technology for weld seam tracking

Jie LIU,Youmin HU,Bo WU,Kaibo ZHOU,Mingfeng GE

《机械工程前沿（英文）》 2015年 第10卷 第1期   页码 95-101 doi: 10.1007/s11465-015-0332-3

摘要：

A novel adaptive sliding mode control algorithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The proposed algorithm does not require the precise dynamic model, and is more practical. Its robustness is verified by the Lyapunov stability theory. The analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than traditional sliding mode control algorithm under various disturbances.

关键词： weld seam tracking     welding robotic manipulator     adaptive control     sliding mode control    

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Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

《机械工程前沿（英文）》 2023年 第18卷 第3期 doi: 10.1007/s11465-023-0753-3

摘要： To achieve the collision-free trajectory tracking of the four-wheeled mobile robot (FMR), existing methods resolve the tracking control and obstacle avoidance separately. Guaranteeing the synergistic robustness and smooth navigation of mobile robots subjected to motion uncertainties in a dynamic environment using this non-cooperative processing method is difficult. To address this challenge, this paper proposes an obstacle-circumventing adaptive control (OCAC) framework. Specifically, a novel anti-disturbance terminal slide mode control with adaptive gains is formulated, incorporating specified control laws for different stages. This formulation guarantees rapid convergence and simultaneous chattering elimination. By introducing sub-target points, a new sub-target dynamic tracking regression obstacle avoidance strategy is presented to transfer the obstacle avoidance problem into a dynamic tracking one, thereby reducing the burden of local path searching while ensuring system stability during obstacle circumvention. Comparative experiments demonstrate that the proposed OCAC method can strengthen the convergence and obstacle avoidance efficiency of the concerned FMR system.

关键词： four-wheeled mobile robot     obstacle-circumventing adaptive control     adaptive anti-disturbance terminal sliding mode control     sub-target dynamic tracking regression obstacle avoidance    

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A model reference adaptive control based method for actuator delay estimation in real-time testing

Cheng CHEN, James M. RICLES

《结构与土木工程前沿（英文）》 2010年 第4卷 第3期   页码 277-286 doi: 10.1007/s11709-010-0072-8

摘要： Real-time testing provides a viable experimental technique to evaluate the performance of structural systems subjected to dynamic loading. Servo-hydraulic actuators are often utilized to apply calculated displacements from an integration algorithm to the experimental structures in a real-time manner. The compensation of actuator delay is therefore critical to achieve stable and reliable experimental results. The advances in compensation methods based on adaptive control theory enable researchers to accommodate variable actuator delay and achieve good actuator control for real-time tests. However, these adaptive methods all require time duration for actuator delay adaptation. Experiments show that a good actuator delay estimate can help optimize the performance of the adaptive compensation methods. The rate of adaptation also requires that a good actuator delay estimate be acquired especially for the tests where the peak structural response might occur at the beginning of the tests. This paper presents a model reference adaptive control based method to identify the parameter of a simplified discrete model for servo-hydraulic dynamics and the resulting compensation method. Simulations are conducted using both numerical analysis and experimental results to evaluate the effectiveness of the proposed estimation method.

关键词： real-time testing     actuator delay     compensation     adaptive control     MIT rule     discrete transfer function    

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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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Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networksand an adaptive-network-based fuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam

《化学科学与工程前沿（英文）》 2013年 第7卷 第3期   页码 357-365 doi: 10.1007/s11705-013-1336-3

摘要： Various simulation tools were used to develop an effective intelligent system to predict the effects of temperature and pressure on an oil extraction yield. Pomegranate oil was extracted using a supercritical CO (SC-CO ) process. Several simulation systems including a back-propagation neural network (BPNN), a radial basis function neural network (RBFNN) and an adaptive-network-based fuzzy inference system (ANFIS) were tested and their results were compared to determine the best predictive model. The performance of these networks was evaluated using the coefficient of determination ( ) and the mean square error (MSE). The best correlation between the predicted and the experimental data was achieved using the BPNN method with an of 0.9948.

关键词： oil recovery     artificial intelligence     extraction     neural networks     supercritical extraction    

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Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Yue ZHAO, Feng GAO, Qiao SUN, Yunpeng YIN

《机械工程前沿（英文）》 2021年 第16卷 第2期   页码 271-284 doi: 10.1007/s11465-020-0623-1

摘要： Legged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

关键词： terrain classification     hexapod robot     legged robot     adaptive locomotion     gait control    

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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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飞航导弹高精度自适应预测控制设计

孙明玮,陈增强,袁著祉,任强,杨明

《中国工程科学》 2005年 第7卷 第10期   页码 23-27

摘要：

飞航导弹的飞行主要是通过姿态稳定与调节来实现的。通过以小扰动模型为基础的导弹动力学特性分析，建立了导弹姿态控制回路的串级控制结构，并且以离散模型作为基于递推最小二乘法的自适应预测控制的被控对象，把原先响应较慢的质心控制转换为反应较快而且精度高的弹道角控制。根据导弹的特性，在姿态内回路采用广义预测控制，在弹道外回路采用一种积分形式的预测控制。在参考信号上，实现了质心指令到弹道指令的有效变换，为高精度小超调跟踪奠定了基础。这种方法实现了姿态参考信号与导引指令的统一，姿态控制与质心控制的统一，充分降低了对气动等数据的精度要求，参数选择简单。数值仿真结果说明了这种方法的有效性；提出了进一步的研究方向。

关键词： 飞航导弹     自适应控制     模型预测控制     鲁棒性    

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输出受限的柔性海洋立管自适应神经网络边界控制 Research Article

余初阳1,楼旭阳1,马艺飞1,叶倩2,张今旗3

《信息与电子工程前沿（英文）》 2022年 第23卷 第8期   页码 1229-1238 doi: 10.1631/FITEE.2100586

摘要： 针对具有未知非线性扰动和输出限制的柔性海洋立管系统，提出一种基于自适应神经网络的边界控制方法抑制振动。首先，通过偏微分方程分布参数系统描述柔性海洋立管系统的动态特性。为补偿非线性扰动对系统影响，利用径向基神经网络构造一个基于神经网络的边界控制器以减少振动。在所提边界控制器下，基于李亚普诺夫方法，保证柔性海洋立管系统一致有界。该方法为其他柔性机器人系统的边界控制提供了一种集成神经网络的思路。最后，通过数值仿真验证所提方法的有效性。

关键词： 海洋立管系统；偏微分方程；神经网络；输出限制；边界控制；未知扰动    

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标题 作者 时间 类型 操作

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

Hamed HABIBI, Hamed RAHIMI NOHOOJI, Ian HOWARD

期刊论文

Statistical process control with intelligence using fuzzy ART neural networks

Min WANG, Tao ZAN, Renyuan FEI,

期刊论文

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

Fan XU, Jin WANG, Guo-dong LU

期刊论文

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

Moulay Rachid DOUIRI, Mohamed CHERKAOUI

期刊论文

An adaptive sliding mode control technology for weld seam tracking

Jie LIU,Youmin HU,Bo WU,Kaibo ZHOU,Mingfeng GE

期刊论文

Obstacle-circumventing adaptive control of a four-wheeled mobile robot subjected to motion uncertainties

期刊论文

A model reference adaptive control based method for actuator delay estimation in real-time testing

Cheng CHEN, James M. RICLES

期刊论文

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

期刊论文

Predicting the yield of pomegranate oil from supercritical extraction using artificial neural networksand an adaptive-network-based fuzzy inference system

J. Sargolzaei, A. Hedayati Moghaddam

期刊论文

Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

Yue ZHAO, Feng GAO, Qiao SUN, Yunpeng YIN

期刊论文

PID neural network control of a membrane structure inflation system

Qiushuang LIU, Xiaoli XU

期刊论文

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

Jinxin LIU, Xuefeng CHEN, Zhengjia HE

期刊论文

Neural network control for earthquake structural vibration reduction using MRD

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

期刊论文

飞航导弹高精度自适应预测控制设计

孙明玮,陈增强,袁著祉,任强,杨明

期刊论文

输出受限的柔性海洋立管自适应神经网络边界控制

余初阳1,楼旭阳1,马艺飞1,叶倩2,张今旗3

期刊论文