检索结果-内蒙古大学图书馆

Experimental implementation of a vehicle anti-lock brake system employing intelligent active force control with a P-type iterative learning algorithm

引用

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第13期45卷 2554-2565页

作者： Al-Mola, Mohammed H. Mailah, Musa Abdelmaksoud, Sherif, I Univ Mosul Coll Petr & Min Engn Dept Petr & Refining Engn Mosul Nineveh Iraq Univ Teknol Malaysia UTM Fac Mech Engn Utm Skudai Malaysia King Fahd Univ Petr & Minerals Aerosp Engn Dept Dhahran Saudi Arabia Univ Mosul Coll Petr & Min Engn Dept Petr & Refining Engn Mosul 41002 Nineveh Iraq

The anti-lock brake system (ABS) is an active safety device used in ground vehicles to increase the brake force between the tire and the road during panic braking. Due to the high non-linearity of the tire and road interaction plus uncertainties derived from vehicle dynamics, a standard proportional-integral-derivative (PID) controller is not deemed enough for the system to produce optimum performance. An active force control (AFC) based scheme is proposed to enhance the robustness of the system and reject undesirable disturbances. A P-type iterative learning algorithm (ILA) is implemented in the AFC loop to estimate the vital parameter continuously for force feedback compensation. In this paper, the control scheme to be known as PID-ILAFC was validated experimentally through its implementation on a test rig. A hardware-in-the-loop (HIL) test via LabVIEW was formulated with novel intelligent control schemes to execute the algorithm in real-time, thereby practically verifying the response of the ABS in the wake of parametric changes and varied operating and loading conditions. The PID-ILAFC controller is specifically designed to provide a proper slip ratio close to the reference value, a reduced stopping distance, and stability in vehicle movement during panic braking. The results clearly exhibit more robustness and superior performance of the AFC-based ABS in achieving the reduced stopping distance and good slip ratio in comparison to the PID and passive counterparts for a dry road condition setting.

关键词： Anti-lock brake system active force control iterative learning algorithm dry road condition stopping distance wheel slip ratio

来源：评论

学校读者我要写书评

暂无评论

Nonlinear sliding mode control of manipulator based on iterative learning algorithm

引用

JOURNAL OF ELECTRICAL SYSTEMS 2021年第4期17卷 421-437页

作者： Xu, Hupeng Li, Meiqin Lu, Chenglang Wang, Wenan Wenzhou Univ Technol Coll Mech & Elect Engn Wenzhou 325035 Peoples R China Zhejiang Inst Mech & Elect Engn Sch Modern Informat Technol Hangzhou 310051 Peoples R China

Manipulator is a complex nonlinear system. Due to its uncertainty and other factors, it is difficult to establish an accurate control model, which brings some difficulties to the highprecision estimation and tracking of manipulator. Aiming at the problems of traditional manipulator control trajectory control, joint angular displacement control and joint position tracking deviation control, a nonlinear sliding mode control method of manipulator based on iterative learning algorithm is proposed. Taking the 7-DOF manipulator as the research object, according to its structural characteristics, the adaptive control method is adopted to realize the adaptive control of different loads. Considering Stribeck friction and external interference, a seven degree of freedom mechanical manual model is established, and the ideal input of manipulator control is obtained by using the model. In order to ensure the high robustness of the nonlinear motion system of the manipulator, the reset circuit is designed. Finally, the output of the controller is applied to the controlled system by using the iterative learning algorithm to obtain the output of the controlled system, so as to realize the nonlinear sliding mode control of the manipulator. The experimental results show that the control trajectory of this method fits well with the actual trajectory, the tracking error accuracy of the manipulator is high, the tracking deviation of joint angular displacement and joint position is small, and it has strong experience learning ability and robustness, which has practical application value.

关键词： iterative learning algorithm manipulator nonlinear sliding mode control adaptive sliding mode control Stribeck friction reset circuit

来源：评论

学校读者我要写书评

暂无评论

iterative learning control algorithm for optimal path industrial manipulator

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 5875-5882页

作者： Lu, Zhengjie Hechi Univ Sch Mech & Elect Engn Yizhou Guangxi Peoples R China

In this paper, a two-degree-of-freedom manipulator is taken as the research object, and the relevant dynamic model is established, the iterative learning controller is designed, and the trajectory tracking control of the manipulator is carried out by using the iterative learning control algorithm. iterative learning control (ILC) has a better control effect on a two-degree-of-freedom manipulator with repetitive motion characteristics for its non-linear system. In the case of disturbance, a PD-type iterative learning control law is designed. With the increasing number of iterations of the system, the required correction interval is shortened by modifying the gain matrix in real time in the interval, so as to accelerate the convergence speed. The simulation results show that the convergence speed of PD-type ILC is faster than that of P-type ILC, and the convergence effect of PD-type ILC with disturbance is better than that of traditional disturbance-type ILC. The industrial robot system is guaranteed to have good dynamic performance.

关键词： Two-degree-of-freedom manipulator PD learning law iterative learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Fault diagnosis of coal-mine-gas charging sensor networks using iterative learning-control algorithm

引用

PHYSICAL COMMUNICATION 2020年 43卷 101175-101175页

作者： Zhang, Jianyu Huang, Kai Anhui Univ Sci & Technol Coll Energy & Safety Huainan 232001 Peoples R China Anhui Univ Sci & Technol Key Lab Mine Safety & High Efficient Min Jointly Huainan 232001 Anhui Peoples R China Anhui Univ Sci & Technol Educ Minist Huainan 232001 Anhui Peoples R China

To detect and estimate the faults of discrete linear time-varying uncertain systems, a discrete learning strategy is applied to fault diagnosis, and a new fault-detection and estimation algorithm is proposed. The algorithm adopts the threshold-limit technology. In the selected optimal time domain, a residual signal is used to perform iterative learning correction for the introduced virtual faults so that the virtual faults in an actual system approach the actual faults. The same method is repeated in the remaining optimal time domain to achieve the objective of fault diagnosis. The algorithm not only completes the fault detection and estimation of a discrete linear time-varying uncertain system but also improves the reliability of fault detection and reduces the false alarm rate. Finally, the simulation results verify the effectiveness of the proposed algorithm. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Sensor fault diagnosis iterative learning algorithm Discrete system

来源：评论

学校读者我要写书评

暂无评论

Force tracking impedance control with unknown environment via an iterative learning algorithm 3

Force tracking impedance control with unknown environment vi...

引用

3rd IEEE International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Liang, Xiuquan Zhao, Huan Li, Xiangfei Ding, Han Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781538670668

Within impedance control framework, since force tracking accuracy is affected by the robotic tracking error, an adaptive force control scheme combined with an iterative learning algorithm is proposed in this paper. First, the unknown environment stiffness and location arc observed in real time based on the actual contact force and robot position, and the adaptive reference trajectory is obtained by the estimated environment information. Then, position-based impedance control scheme is adopted to adjust the dynamical relationship between the end-effector position and the contact force. Considering the effect of the position tracking error on the force tracking accuracy, an iterative learning control method is utilized to compensate the motion error effectively by the force error. The proposed method can effectively reduce the steady state force error by simply combining the indirectly adaptive reference trajectory generation technique with position tracking error regulation method. Stability and convergence conditions are presented with a Lyapunov function and frequency domain method. Simulations and experiments are conducted on a biaxial platform to demonstrate the effectiveness of the proposed method.

关键词： impedance control iterative learning algorithm unknown environment adaptive reference trajectory force tracking

来源：评论

学校读者我要写书评

暂无评论

Intelligent Active Force Control of a Helicopter Seat Suspension using iterative learning algorithm 6

Intelligent Active Force Control of a Helicopter Seat Suspen...

引用

6th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Ahmadi, Samaneh Gohari, Mohammad Tahmasebi, Mona Arak Univ Technol Fac Mech Engn Arak Iran Agr Res Educ & Extens Org Markazi Agr & Nat Resources Res & Educ Ctr Agr Engn Res Dept Arak Iran

ISBN: (纸本)9781509035861

The high level of noise and vibrations in helicopters is not preventable and happens through flight operations. This high level of vibrations can produce uneasiness and may affect aircrew performance and their health. Correspondingly, their concentration on flight operation and decision making is strongly depended to comfort ability. Therefore, vibration attenuation can improve flight control, and aircrews feel better conditions. In this study, the helicopter structure was modeled in ANSYS software and natural frequencies have been obtained. The seat suspension and pilot body were modeled by Lumped modeling method. The active force control (AFC) scheme hybridized by iterative learning (IL) to determine the estimated mass called AFCIL was used in helicopter seat suspension system to reduce the vibrations transmitted to the pilot body. The simulation was performed with sinusoidal and random disturbance signals and results demonstrated in both the time and frequency domains. Attained results were compared with the passive system, PID controller and AFCANN schemes. The AFCIL scheme had superior performance in pilot head displacement reduction compared to the classical PID controller. The results of the AFCIL and the AFCANN were similar together while AFCIL results were marginally superior to AFCANN.

关键词： Active seat suspension active force control helicopter seat suspension iterative learning algorithm artificial neural network

来源：评论

学校读者我要写书评

暂无评论

Design and Experimental Development of Wireless iterative learning Fault Estimation algorithm With Quantization and Packet Losses

引用

IEEE ACCESS 2021年 9卷 150120-150127页

作者： Herve, Samba Aime Aurelien, Yeremou Tamtsia Leandre, Nneme Nneme Som, Idellette Judith Hermine Alphonse, Houwe Univ Douala Natl Higher Polytech Sch Douala Cameroon Univ Douala Adv Teachers Training Coll Tech Educ Douala Cameroon Univ Maroua Fac Sci Maroua Cameroon

In this paper, a wireless iterative learning fault estimation algorithm (WILFEA) is proposed and validated experimentally with the aim to achieve perfect tracking of a prescribed reference trajectory for systems with packet losses and quantizer measurements that operate repetitively. First, state variables, Markov chain process of random packet losses, and a logarithmic quantizer are considered to establish an extended-state-space system model. Next, based on this model, sufficient conditions for linear repetitive processes are developed with the Lyapunov-Krasovskii technique and H-infinity approach is applied to calculate the observer gain and the learning gain. Then, WILFEA based fault estimation is constructed. Compared with the existing methods, the proposed WILFEA improves the fault estimation performance in the current iteration by consider both state error and fault estimation error. Finally, the simulation and experimental results are used for DC-Servomotor system to illustrate the effectiveness of the proposed approach using Matlab/simulink software, LabVIEW Software, ZigBee Xbee and Arduino board.

关键词： Estimation Packet loss Quantization (signal) Heuristic algorithms iterative algorithms Delays Wireless communication iterative learning algorithm quantiser measurements random packet losses fault estimation

来源：评论

学校读者我要写书评

暂无评论

Dynamic event-triggered attitude synchronization of multi-spacecraft formation via a learning Chebyshev neural network control approach

引用

AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2025年第2期97卷 162-169页

作者： Li, Genghuan Jia, Qingxian Wu, Yunhua Liao, He Zheng, Zhong Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Peoples R China Nanjing Tech Univ Nanjing Peoples R China Jiangsu Open Univ Sch informat technol Nanjing Peoples R China

PurposeThis paper aims to investigate the attitude synchronization issue of multi-spacecraft formation flying systems under the limited communication ***/methodology/approachThe authors propose a distributed learning Chebyshev neural network controller (LCNNC) combining a dynamic event-triggered (DET) mechanism and a learning CNN model to achieve accurate multi-spacecraft attitude synchronization under communication *** proposed method can significantly reduce the internal communication frequency and improve the attitude synchronization *** implicationsThis method requires the low communication resources, has a high control accuracy and is thus suitable for engineering ***/valueA novel DET mechanism-based LCNNC is proposed to achieve the accurate multi-spacecraft attitude synchronization under communication constraints.

关键词： Spacecraft formation Attitude synchronization Chebyshev neural network Dynamic event-triggered iterative learning algorithm

来源：评论

学校读者我要写书评

暂无评论

Singular value decomposition based learning identification for linear time-varying systems: From recursion to iteration

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2023年第12期33卷 6986-7003页

作者： Song, Fazhi Li, Li Liu, Yang Dong, Yue Harbin Inst Technol Ctr Ultra Precis Optoelect Instrument Engn Harbin Peoples R China Minist Ind & Informat Technol Harbin Inst Technol Key Lab Ultra Precis Intelligent Instrumentat Harbin Peoples R China Harbin Inst Technol Ctr Ultraprecis Optoelect Instrument Engn 92Xidazhi St Harbin Peoples R China

System identification is a critical task in various engineering applications such as motion control, signal processing and robotics. In this article, the identification of linear time-varying (LTV) systems that perform tasks repetitively over a finite-time interval is investigated. Traditional LTV system identification typically adopts recursive algorithms in the time domain, which are incapable of tracking drastic-varying parameters and are subject to estimation lag and numerical instability. To address these issues, this article proposes the utilization of an iteration axis in addition to the time axis for estimating repetitive time-varying parameters. Specifically, the proposed approach involves an estimation algorithm for the time-varying parameters based on a recursive least squares (RLS) method along the iteration axis, as well as an update algorithm for the covariance matrix based on singular value decomposition (SVD) to enhance numerical stability. Additionally, a bias compensation method based on noise variance estimation is introduced for the sake of eliminating estimation error induced by measurement noise. Numerical comparisons with existing methods are conducted to demonstrate the effectiveness and superiority of the proposed method.

关键词： bias compensation iterative learning algorithm parameter estimation singular value decomposition time-varying systems

来源：评论

学校读者我要写书评

暂无评论

Novel iterative Machine learning for Accurate Photon-Counting Poisson Channel Estimation

引用

IEEE COMMUNICATIONS LETTERS 2022年第9期26卷 2081-2085页

作者： Arya, Sudhanshu Chung, Yeon Ho Pukyong Natl Univ Dept Artificial Intelligence Convergence Busan 48513 South Korea Pukyong Natl Univ Dept Informat & Commun Engn Busan 48513 South Korea

In this work, we present a new machine learning-based framework for the accurate estimation of the shot-noise limited photon-counting Poisson channel by developing a state-of-art iterative unsupervised learning algorithm for intelligent optical communications. By accurate estimation, we mean that it achieves very low estimation error even when the effect of the received data is unpredictable. We consider a realistic situation where modulated symbols are assumed to be hidden or unobserved latent variables, thereby making conventional estimation algorithms based on maximum likelihood approach unsuitable or inefficient. In particular, we consider a probabilistic model and assume that the received data is not labeled. With this unpredictable data considered, a novel iterative machine learning framework is developed based on an expectation and maximization algorithm. The proposed algorithm avoids the need to choose an appropriate step size as required in gradient method based algorithms. It is shown that it significantly outperforms the least square and the Viterbi detection technique.

关键词： Estimation Photonics Machine learning algorithms Channel estimation Wireless communication iterative algorithms High-speed optical techniques iterative learning algorithm Poisson channel optical wireless communications unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：