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adaptive iterative learning control for a class of parameterized fractional-order systems subjected to backlash-like hysteresis

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND control ENGINEERING 2024年第3期238卷 410-424页

作者： Wang, Hong Wei, Jianming Wang, Zhe Naval Aviat Univ Yantai Peoples R China Naval Univ Engn Wuhan Peoples R China Naval Univ Engn Wuhan 430033 Peoples R China

In this article, a fractional-order adaptive iterative learning control scheme is proposed for a class of parameterized fractional-order systems with unknown control gain and backlash-like hysteresis nonlinearity under disturbance. Based on the sufficient condition for the stability of linear fractional-order systems, a sliding mode surface of tracking errors is constructed to facilitate the controller design and stability analysis. A new boundary layer function is designed by using Mittag-Leffler function to relax the restriction of the identical initial condition of iterative learning control design. The fractional-order differential-type adaptive laws and difference-type learning law are designed to estimate unknown constant parameters and time-varying parameter, respectively. To deal with the influence of backlash-like hysteresis nonlinearity reminder term and unknown bounded external disturbance, a robust control term is designed by employing the hyperbolic tangent function with a convergent series sequence which can guarantee the learning convergence along iteration axis. By constructing Lyapunov-like composite energy function, the stability analysis is presented to prove the convergence of the system output to a small neighborhood of the desired trajectory and the boundedness of all the closed-loop signals. Finally, a simulation example of second-order nonlinear fractional-order system is presented, which demonstrates the effectiveness of the proposed fractional-order adaptive iterative learning control scheme.

关键词： adaptive iterative learning control parameterized fractional-order systems backlash-like hysteresis Mittag-Leffler function boundary layer function frequency distributed model

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adaptive iterative learning control for high-order nonlinear systems with different types of uncertainties

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2024年第8期34卷 5399-5418页

作者： Li, Xuefang Chen, Yanfang Sun, Hui-Jie Liu, Wanquan Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen Guangdong Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus66 Gongchang Rd Shenzhen 518107 Guangdong Peoples R China

The present work aims at investigating the adaptive iterative learning control (AILC) design for uncertain high-order fully actuated nonlinear systems. In order to show the design principles, three types of nonlinear systems are considered, including systems with just parametric uncertainty, systems with both parametric and input distribution uncertainties as well as systems with both parametric uncertainty and an unknown control gain matrix. For different systems, the corresponding AILC schemes are proposed with different techniques in dealing with various system uncertainties, for which the convergence analysis are conducted rigorously based on the composite energy function. The effectiveness of the proposed AILC strategies is verified through numerical simulations.

关键词： adaptive iterative learning control composite energy function high-order fully actuated systems nonlinear systems uncertainty

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adaptive iterative learning control of internal temperature for high-speed trains

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第1期38卷 463-473页

作者： Chen, Chunjun Zheng, Qin Yang, Lu Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu 610031 Peoples R China

The setting curve and control method of internal temperature in trains are studied in this paper to achieve energy saving control for air conditioning systems. First, a model of interior temperature that considers the heat transmission from infiltration air is established according to the relationship between the equivalent leakage area for the train body and the pressure difference between the internal and external carriage. Second, the interior temperature setting curve, which changes adaptively with the ambient temperature, is established considering the energy consumption of air conditioning systems and passengers' thermal comfort. Finally, an adaptive iterative learning control (AILC) algorithm is proposed and applied to control the interior temperature according to the periodicity of train operation. In this paper, meteorological data from a typical meteorological year are used for simulation analysis. Simulation results show that the AILC algorithm can effectively track the interior temperature setting value and has a better control effect when the train runs on the same line repeatedly. Under the action of the AILC algorithm, the root-mean-square error is reduced to 0.069 degrees C in the third iteration. Furthermore, the energy saving for a single carriage in a single process reaches up to 1.534 kW center dot h, thus achieving energy saving control.

关键词： adaptive iterative learning control Air conditioning systems High-speed train Temperature control

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adaptive iterative learning control for Nonsquare Nonlinear Multi-Agent Systems Subject to Unknown Fading Channels

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NONLINEAR DYNAMICS 2025年 1-20页

作者： Zeng, Dezheng Li, Xiao-Dong Li, Xuefang Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Guangdong Prov Key Lab Fire Sci & Intelligent Emer Shenzhen 518107 Guangdong Peoples R China

This paper investigates the consensus tracking control problem of nonsquare nonlinear multi-agent systems (MASs) subject to unknown fading channels. In order to compensate for the impact of the unknown fading channels, a learning-based estimation approach is proposed to estimate the unknown fading gain, based on which a novel adaptive iterative learning control (AILC) scheme is then developed to achieve the consensus tracking task in presence of system uncertainties and unknown fading channels. In contrast to the existing AILC algorithms, the proposed AILC scheme is applicable to nonsquare systems without using the invertibility property of the control gain matrices. In addition, the convergence analysis of the proposed AILC strategy is carried out with the aid of the composite energy function (CEF) method. The proposed AILC scheme is also implemented to consensus tracking of autonomous vehicles, where the effectiveness is verified.

关键词： adaptive iterative learning control Multi-agent systems Consensus tracking Nonsquare Unknown fading channels

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adaptive iterative learning control for Nonlinearly Parameterized Non-Strict Feedback Nonlinear Systems with Full-State Constraints

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JOURNAL OF control AUTOMATION AND ELECTRICAL SYSTEMS 2024年第1期35卷 24-40页

作者： Benslimane, Hocine Abdelhafid Boussouf Univ Ctr Dept Mech & Electromech Engn POB 26 Mila 43000 Algeria

This paper presents a novel adaptive iterative learning control scheme for state-constrained nonlinearly parametrized non-strict feedback nonlinear systems performing the same operation during a finite time interval under the alignment condition. A novel systematic design procedure is constructed via the barrier Lyapunov function combined with a command filter adaptive backstepping. The barrier Lyapunov function keeps all states within a predefined region. Command filter solves the problems of restrictions on high-order differentiability of virtual learning controller and avoids the complexity that arises due to the explosion of terms in adaptive backstepping design. A new barrier composite energy function is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iterations. Simulation results are provided to show the effectiveness of the proposed control scheme.

关键词： adaptive iterative learning control Backstepping design Non-strict feedback systems States constraints

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adaptive iterative learning control for continuous systems with non-repetitive uncertainties and unknown state delays

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2023年第4期33卷 2796-2810页

作者： Wu, Xingzheng Park, Ju H. H. Shen, Mouquan Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing Peoples R China Yeungnam Univ Dept Elect Engn Kyonsan South Korea Yeungnam Univ Dept Elect Engn 280 Daehak Ro Kyonsan 38541 South Korea Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing 211816 Peoples R China

This paper is devoted to tracking control of nonlinear multiple-input multiple-output systems subject to unknown state delays and non-repetitive uncertainties. An adaptive iterative learning control scheme is proposed by integrating a P-type feedback term, an iterative updating term and an additional compensation term. Among these terms, the first one is to alleviate the ill effect caused by non-repetitive uncertainties and the third one is utilized to handle the unwell learned part. The composite energy function methodology is employed to elaborate the bounded convergence of the tracking error. The validity of the proposed method is compared with existing results via simulation studies.

关键词： adaptive iterative learning control composite energy function non-repetitive uncertainties time-delay

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adaptive iterative learning control for Underactuated Systems with Unknown Input Distribution Matrices: Ensuring Input Continuity 3

Adaptive Iterative Learning Control for Underactuated System...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Yin, Mali Li, Xuefang Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

This work investigates the adaptive iterative learning control (AILC) problem for a class of underactuated systems with unknown input distribution matrices. By introducing a newly designed control gain matrix, a novel AILC algorithm is proposed, which is able to deal with the unknown nonsquare control gain matrix effectively. Additionally, an auxiliary system is adopted to deal with the input saturation. The convergence of the proposed control algorithm is rigorously analyzed by using the composite energy function (CEF) method. The effectiveness is demonstrated through a numerical example.

关键词： adaptive iterative learning control Underactuated systems Unknown control gains Input saturation Input continuity

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adaptive iterative learning control for Full States Constrained non-Strict Feedback Nonlinear Systems 2

Adaptive Iterative Learning Control for Full States Constrai...

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2nd International Conference on Electrical Engineering and Automatic control (ICEEAC)

作者： Benslimane, Hocine Univ Ctr Mila Dept Mech & Electromech Engn Mila Algeria

ISBN: (纸本)9798350349740;9798350349757

This paper presents an adaptive iterative learning control scheme for state-constrained non-strict feedback nonlinear systems performing the same operation during a finite time interval under the alignment condition. A systematic design procedure is constructed via the barrier Lyapunov function combined with a command filter adaptive backstepping. The barrier Lyapunov function keeps all states within a predefined region. Command filter solves the problems of restrictions on high order differentiability of virtual learning controller and avoids the complexity that arises due to the explosion of terms in adaptive backstepping design. A new barrier composite energy function is used to prove the boundedness of all signals of the resulting closed-loop system and the convergence of the tracking errors to zero over iterations. Simulation results are provided to show the effectiveness of the proposed control scheme.

关键词： adaptive iterative learning control Backstepping design Non-strict feedback systems States constraints

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adaptive iterative learning control for Permanent Magnet Synchronous Motors with Uncertainties 3

Adaptive Iterative Learning Control for Permanent Magnet Syn...

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3rd Conference on Fully Actuated System Theory and Applications (FASTA)

作者： Yang, Ke Li, Xuefang Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen Campus Shenzhen 518000 Peoples R China

ISBN: (纸本)9798350373707;9798350373691

This paper proposes a novel adaptive iterative learning control (AILC) strategy for position tracking of permanent magnet synchronous motor (PMSM) servo systems. The primary objective of this paper is to achieve precise position tracking control in the presence of both the load disturbances and the friction forces. The designed control scheme compensates for the load disturbance indirectly by adjusting the desired control input in the controller. Moreover, an adaptive parametric update law is employed to estimate the unknown parameters. The convergence properties of the proposed control algorithms are analyzed using the composite energy function (CEF) methodology. Additionally, the effectiveness of the proposed method for the PMSM system is validated through simulations conducted in MATLAB.

关键词： adaptive iterative learning control Permanent magnet synchronous motor (PMSM) Composite Energy Function

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adaptive iterative learning control for high-order nonlinear systems with random initial state shifts

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ISA TRANSACTIONS 2022年 130卷 205-215页

作者： Li, Guojun Lu, Tiantian Han, Yishi Xu, Zhijiang Zhejiang Police Coll Basic Courses Dept Hangzhou 310053 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China Zhejiang Inst Mech & Elect Engn Sch Automat Hangzhou 310053 Peoples R China

Aiming at the tracking problem for a class of high-order nonlinear systems with arbitrary initial state, this paper proposes different initial state shifts rectifying schemes for systems with different orders from the perspective of solving differential equations to ensure that the systems achieve complete tracking over the specified interval. This method relaxes the requirement of initial positioning in iterative learning control, and solves the problem of iterative learning control for high-order nonlinear systems with arbitrary initial state error. The theoretical analysis shows that the proposed schemes can make all signals in the system bounded, and ensure that the tracking error in the preset interval tends to zero as the number of iterations increases. When designing the controller, the arctangent function is used to avoid flutter of the control signal and related variables. The final simulations verify the effectiveness of the proposed algorithms. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： adaptive iterative learning control Differential equation Initial rectifying Convergence

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