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

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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

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A gait rehabilitation strategy inspired by an iterative learning algorithm

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MECHATRONICS 2012年第2期22卷 213-221页

作者： Bae, Joonbum Tomizuka, Masayoshi UNIST Sch Mech & Adv Mat Engn Ulsan South Korea Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA

Robotic gait rehabilitation devices enable efficient and convenient gait rehabilitation by mimicking the functions of physical therapists. In manual gait rehabilitation training, physical therapists have patients practice and memorize normal gait patterns by applying assistive torque to the patient's joint once the patient's gait deviates from the normal gait. Thus, one of the most important factors in robotic gait rehabilitation devices is to determine the assistive torque to the patient's joint during rehabilitation training. In this paper, the gait rehabilitation strategy inspired by an iterative learning algorithm is proposed, which uses the repetitive characteristic of gait motions. In the proposed strategy, the assistive joint torque in the current stride is calculated based on the information from previous strides. Simulation results and experimental results using an active knee orthosis are presented, which verify that the proposed strategy can be used to calculate appropriate assistive joint torque to excise the desired motions for rehabilitation. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Gait rehabilitation strategy iterative learning algorithm Robotic gait rehabilitation device

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

Force tracking impedance control with unknown environment vi...

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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

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

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

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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

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Nonlinear sliding mode control of manipulator based on iterative learning algorithm

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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

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

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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

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iterative learning control algorithm for optimal path industrial manipulator

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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

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

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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

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iterative learning Fault Diagnosis algorithm for Non-uniform Sampling Hybrid System

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IEEE/CAA Journal of Automatica Sinica 2017年第3期4卷 534-542页

作者： Hongfeng Tao Dapeng Chen Huizhong Yang Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxi 214122China

For a class of non-uniform output sampling hybrid system with actuator faults and bounded disturbances,an iterative learning fault diagnosis algorithm is ***,in order to measure the impact of fault on system between every consecutive output sampling instants,the actual fault function is transformed to obtain an equivalent fault model by using the integral mean value theorem,then the non-uniform sampling hybrid system is converted to continuous systems with timevarying delay based on the output delay ***,an observer-based fault diagnosis filter with virtual fault is designed to estimate the equivalent fault,and the iterative learning regulation algorithm is chosen to update the virtual fault repeatedly to make it approximate the actual equivalent fault after some iterative learning trials,so the algorithm can detect and estimate the system faults *** results of an electro-mechanical control system model with different types of faults illustrate the feasibility and effectiveness of this algorithm.

关键词： Equivalent fault model fault diagnosis iterative learning algorithm non-uniform sampling hybrid system virtual fault

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iterative learning Fault Estimation algorithm for Time-delay Systems Based on Extended Observer 7

Iterative Learning Fault Estimation Algorithm for Time-delay...

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IEEE 7th Data Driven Control and learning Systems Conference (DDCLS)

作者： Tao, Hongfeng Wei, Qiang Jiangnan Univ Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Peoples R China

ISBN: (纸本)9781538626184

For a class of multivariable linear, time-delay systems with actuator fault and measurement bounded disturbances in output, an iterative learning fault estimation algorithm based on extended observer is proposed. The extended observer is designed in terms of the linear matrix inequality technique such that the states and disturbances can be estimated simultaneously in every trials, then the faults and disturbances can be separated for avoiding impact to each other. Afterwards, the iterative learning fault estimation algorithm by defining estimation residual is chosen to adaptively approximate the actuator fault with initial error, then the necessary and sufficient conditions for the existence of the learning algorithm is given through. norm theory and Bellman-Gronwall inequality, and the uniform convergence criteria of the control algorithm is also discussed. Simulation results verify the feasibility and effectiveness of this algorithm.

关键词： Time-delay extended observer fault estimation iterative learning algorithm

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