检索结果-内蒙古大学图书馆

IEEE 12th Data Driven control and learning Systems Conference (DDCLS)

作者： Mo, Congcong Yan, Qiuzhen Lin, Mingjun Chen, Qiang Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Zhejiang Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9798350321050

This work studies the angle tracking problem of robot manipulators with input deadzone and nonzero initial errors. An robust initial-rectification adaptive iterative learning control scheme is proposed to solve this problem. First, the rectification reference trajectory is constructed for dealing with nonzero initial errors during ILC design. Then, based on system parameterization of robot manipulators, by developing Lyapunov function candidate, together with reasonable dealing with the deadzone nonlinearity, a novel robust adaptive iterative learning control scheme is proposed for uncertain robotic system with unknown input deadzone. All closed closed-loop signals are proved to be bounded, with the desired tracking performance achieved. At the end, a numerical simulation is carried out to verify the effective of the proposed robust adaptive iterative learning control scheme.

关键词： adaptive iterative learning control initial position problem nonlinearly parameterized systems

来源：评论

学校读者我要写书评

暂无评论

A Novel adaptive iterative learning control Approach for Nonlinear Output-Constrained Systems With Input Quantization

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年

作者： Chen, Yong Huang, Deqing Zhang, Yanhui Yang, Guang Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou Peoples R China

In practice, the communication bandwidth and physical limitations are the two main categories of threats that the networked control systems may encounter. Therefore, this paper focuses on adaptive iterative learning control (ILC) of nonlinear strict-feedback systems with input quantization and asymmetric output constraint. Through constructing an error transformation mechanism, the original output-constrained control system is converted into the unconstrained form. Subsequently, a novel adaptive ILC algorithm is established by virtue of the command filtered backstepping technique, in which the uncertain terms of Lyapunov function (LF) are decomposed into the parameter compensation components of the controller and the iteration-convergent lumps via the hyperbolic tangent function. Specially, to accommodate the input-related uncertainties brought by quantizer, the devised ILC law adapts a nested structure, thus achieving the estimation of unknown parameters associated with the quantized bias. The convergence of error along the iteration axis is rigorously proven by the composite energy function (CEF). Finally, the proposed approach is applied to two examples, the results of which illustrate the effectiveness of the scheme.

关键词： adaptive iterative learning control asymmetric time-varying output constraint composite energy function input quantization

来源：评论

学校读者我要写书评

暂无评论

adaptive iterative learning control and inverse compensation of macro fiber composite system with hysteresis

引用

JOURNAL OF SOUND AND VIBRATION 2024年 569卷

作者： Chen, Tehuan Lu, Ronghua Ren, Zhigang Ningbo Univ Sch Mech Engn & Mech Ningbo 315211 Peoples R China Shanghai Jiao Tong Univ Ningbo Artificial Intelligence Inst Ningbo 315000 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

Macro fiber composite (MFC) actuators, which exhibit excellent flexibility and strong deformability, are widely used in aerospace exploration, flexible robotics, and other applications. However, the rate-dependent hysteresis nonlinearity of the flexible beam driven by MFCs significantly affects positioning accuracy. This study utilizes a new Hammerstein model to describe the hysteresis of an MFC system and proposes a control strategy that combines adaptive iterative learning control and inverse compensation. The adaptive iterative learning control allows the learning gain parameters to be adjusted based on the tracking error. A high gain ensures the convergence speed of iterative learning, whereas a small learning gain provides high robustness and convergence accuracy. Trajectory tracking experiments are conducted to verify the effectiveness of the proposed control strategy. Compared with the case of direct inverse compensation, after the sixth iteration, the tracking maximum absolute error, relative error, and corresponding root mean square error of the proposed control strategy reduce by 73% on average. Moreover, compared with the case of P-type iterative learning control combined with inverse compensation, the metrics above reduce by 12% on average. The proposed control strategy offers higher tracking accuracy and greater practicality in the trajectory tracking of MFC systems.

关键词： Piezoelectric MFC Improved PI hysteresis model Hysteresis adaptive iterative learning control Inverse compensation

来源：评论

学校读者我要写书评

暂无评论

Backstepping based adaptive iterative learning control for non-strict feedback systems with unknown input nonlinearities

引用

INTERNATIONAL JOURNAL OF adaptive control AND SIGNAL PROCESSING 2024年第7期38卷 2385-2403页

作者： Shi, Huihui Chen, Qiang Li, Yaqian He, Xiongxiong ZJUT Moganshan Inst Deqing Peoples R China Zhejiang Univ Technol Coll Informat Engn Data Driven Intelligent Syst Lab Hangzhou Peoples R China Zhejiang Univ Technol Coll Informat Engn Data Driven Intelligent Syst Lab Hangzhou 310023 Peoples R China

The initial state inconsistency and iteration-varying trajectory problems are considered in adaptive iterative learning control (AILC) to enhance the tracking performance of the non-strict feedback systems with unknown input nonlinearities. Through constructing an error reference trajectory independence of the reference signal, the restrictions on the initial condition and reference trajectory are both relaxed. Subsequently, a backstepping-based AILC methodology is systematically presented to ensure that the error reference trajectory can be followed by the actual tracking error. Integral Lyapunov functions are employed to design the recursive controllers, avoiding potential singularity problems resulting from the differentiation of gain functions. Rigorous analysis is provided without imposing constraints on the control gain functions to demonstrate tracking error convergence. Numerical simulations are included to illustrate the efficacy of the proposed method.

关键词： adaptive iterative learning control backstepping design error-tracking approach input nonlinearities non-strict feedback systems

来源：评论

学校读者我要写书评

暂无评论

adaptive iterative learning control for 2D nonlinear systems with nonrepetitive uncertainties

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第4期31卷 1168-1180页

作者： Xing, Jianmin Chi, Ronghu Lin, Na Qingdao Univ Sci & Technol Sch Math & Phys Qingdao Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Inst Artificial Intelligence & Control Qingdao 266061 Peoples R China

This paper explores the iterative learning control (ILC) problem for two-dimensional (2D) multi-input multi-output nonlinear parametric systems by taking all nonrepetitive uncertainties of stochastic initial shifts, different tracking tasks and nonuniform trial lengths into consideration. A 2D stochastic variable is defined with Bernoulli stochastic distribution for the first time to handle iteration-varying trial lengths. The desired output is incorporated into the learning control law as a feedback to compensate the iterative changes of the tracking tasks. An iterative 2D parameter updating law is established using a new defined virtue tracking error to well address the systems uncertainties and varying trial lengths. Consequently, a novel 2D adaptive ILC (2D-AILC) is presented by incorporating the control law and parameter updating law. The convergence is proved by introducing both the Lyapunov stability principle and the 2D key technique lemma into the repetitive 2D systems even though the dynamic evolution along with two-dimensional directions makes it more difficult in the mathematic analysis. The simulation study tests the theoretical results: the presented 2D-AILC scheme can still accomplish a tracking task exactly even though there exist random initial states, nonrepetitive reference trajectories, and iteration-varying trial lengths.

关键词： 2D nonlinear systems adaptive iterative learning control nonrepetitive references nonuniform trial lengths stochastic initial states

来源：评论

学校读者我要写书评

暂无评论

adaptive iterative learning control for discrete-time nonlinear systems with multiple iteration-varying high-order internal models

引用

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2021年第15期31卷 7390-7408页

作者： Yu, Miao Chai, Sheng Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China

In this work, an adaptive iterative learning control (AILC) method is designed for a class of parametric discrete-time nonlinear systems with random initial condition, unknown time-varying input gain and multiple time-iteration-varying factors including multiple unknown time-iteration-varying parameters and unknown time-iteration-varying external disturbance. The iteration-varying factors can be generated by virtue of multiple iteration-varying high-order internal models, respectively, where iteration-varying high-order internal model means it has iteration-varying order or coefficients. Moreover, the parameter updating law is designed based on the recursive least squares algorithm. Using the designed AILC based on iteration-varying high-order internal model, the learning convergence in the iteration domain is guaranteed through rigorous theoretical analysis under Lyapunov theory. Finally, two simulation examples are given to demonstrate that the proposed scheme is effective.

关键词： adaptive iterative learning control iteration-varying high-order internal model Lyapunov theory multiple-iteration-varying factors non-repetitiveness

来源：评论

学校读者我要写书评

暂无评论

adaptive iterative learning control-Based Rotor Position Harmonic Error Suppression Method for Sensorless PMSM Drives

引用

AMERICAN JOURNAL OF CLINICAL NUTRITION 2021年第11期69卷 10870-10881页

作者： Bi, Guangdong Zhang, Guoqiang Wang, Gaolin Wang, Qiwei Hu, Yihua Xu, Dianguo Harbin Inst Technol Sch Elect Engn & Automat Harbin 150001 Peoples R China Univ York Dept Elect Engn York YO10 5DD N Yorkshire England

Accurate rotor position estimation is important to ensure the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. To suppress the estimated rotor position harmonic error (ERPHE) caused by inverter nonlinearities, flux spatial harmonics, and the current measurement error in high-frequency signal injection-based schemes, an adaptive iterative learning control-based online suppression method is proposed in this article. This method constructs a P-type iterative learning rotor position observer with the forgetting factor to obtain the tracking error and generate the compensation value in real time. The stability, the convergence, and the parameter sensitivity of this observer are analyzed in detail. To improve the suppression effect of the ERPHE for different loads and speeds, an iterative learning gain self-tuning strategy is investigated. The proposed method does not require the offline detection and the discrimination of harmonic frequencies, which has strong suppression ability for different harmonic error components. The effectiveness is verified by experiments on a 2.2-kW interior PMSM drive platform.

关键词： Harmonic analysis Power harmonic filters Rotors Hafnium Estimation Inverters Current measurement adaptive iterative learning control high frequency signal injection (HFSI) online suppression permanent magnet synchronous motor (PMSM) rotor position harmonic error

来源：评论

学校读者我要写书评

暂无评论

adaptive iterative learning control method for finite-time tracking of an aircraft track angle system based on a neural network

引用

FRONTIERS IN PHYSICS 2022年 10卷

作者： Zhang, Chunli Tian, Xu Yan, Lei Xian Univ Technol Shaanxi Key Lab Complex Syst Control & Intelligent Xian Peoples R China

Based on a neural network, this paper presents a new adaptive iterative learning control method for the finite-time tracking control problem of an uncertain aircraft track angle system, which can control the aircraft track inclination through the designed control input rudder deflection angle, so that it can track the preset trajectory in a finite time interval. First, the flight path angle system of the aircraft is abstractly modeled by variable substitution to obtain a triangular model in the form of strict feedback. Second, radial basis function neural network approximation is used to model the uncertain part of the system, aiming at the abstract strict feedback model, and two virtual quantities are designed through the three-layer inversion design method, and then, Lyapunov functions are designed for each subsystem to derive virtual control laws, the actual control law, and the neural network weight adaptive laws. Through Lyapunov stability analysis, it can be seen that the designed controller and adaptive laws can make the whole closed-loop system tend to be stable and realize the tracking of a target trajectory in a finite time interval. Finally, the feasibility and effectiveness of the theory are verified by a simulation example.

关键词： aircraft track angle system adaptive iterative learning control neural network Lyapunov stability finite-time interval tracking

来源：评论

学校读者我要写书评

暂无评论

Error tracking approach to discrete-time adaptive iterative learning control against arbitrary initial perturbations

引用

INTERNATIONAL JOURNAL OF adaptive control AND SIGNAL PROCESSING 2022年第4期36卷 943-964页

作者： Sun, Mingxuan Zhan, Yizhao Li, Wei He, Xiongxiong Zhejiang Univ Technol Coll Informat Engn Liuhe Rd Hangzhou 310023 Peoples R China

Initial condition problem is crucial to a conventional iterative learning control (ILC) scheme, which steers the tracking error from arbitrary initial value to zero in the time steps equaling to the relative degree of the system undertaken. The implementation may be difficult, due to the practical limitation for the control amplitude. This paper presents an error-tracking approach to discrete-time adaptive ILC designs for tracking nonidentical tasks in the presence of initial repositioning errors, which may be quite large. A prototype iterative learning algorithm is derived for estimating the time-varying unknowns, and the saturation is introduced for assuring the estimates to keep away from zero. A key technical lemma, tailored for the analysis purpose in the iteration domain, is presented and applied to analysis of the ILC scheme. The tracking performance of the closed-loop system is evaluated and explored in detail. It is shown that the perfect tracking for the error between the tracking error and the desired error is achieved at every time instant, while the input and output signals remain bounded. By the simulation example, the proposed scheme is verified to be applicable to tracking tasks without restriction on initial repositioning.

关键词： adaptive iterative learning control discrete-time systems initial condition problem nonidentical tasks time-variant systems

来源：评论

学校读者我要写书评

暂无评论

Barrier adaptive iterative learning control for Tank Gun control Systems Under Nonzero Initial Error Condition

引用

IEEE ACCESS 2022年 10卷 8664-8672页

作者： Zhou, Zhimin Wang, Wei Zhang, Yuntao Yan, Qiuzhen Cai, Jianping Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Peoples R China Hebei GEO Univ Shijiazhuang 050031 Hebei Peoples R China Zhejiang Univ Water Resources & Elect Power Coll Elect Engn Hangzhou 310018 Peoples R China

In this paper, a barrier adaptive iterative learning control scheme is proposed to solve the trajectory-tracking problem for tank gun control systems under nonzero initial error condition. A novel construction method of rectified reference trajectory is presented for dealing with the initial position problem of iterative learning control for tank gun control systems. With a quadratic form barrier Lyapunov function adopted to controller design, the quadratic form of system error is constrained within the preset range during each iteration. adaptive iterative learning control technique and robust control technique are jointly used to compensate for the parametric/nonparametric uncertainties and nonsymmetric deadzone nonlinearity. As the iteration number increases, the system state of tank gun control systems may accurately track the rectified reference trajectory, which leads to a excellent tracking performance during the part operation interval of tank gun control systems. Simulation results are presented to verify the effectiveness of the proposed barrier adaptive iterative control scheme.

关键词： Tank gun control systems adaptive iterative learning control barrier Lyapunov function initial position problem

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：