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Privacy-Enabled Secure iterative learning control of Networked Systems Subject to Disclosure Attacks

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第1期71卷 196-200页

作者： Chen, Yong Huang, Deqing Tan, Shuangquan Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China

In this brief, a secure iterative learning control architecture is proposed for networked systems, aiming to track the desired output and protect the privacy of signals. The main technique challenges lie in that the homomorphic encryption is integrated into the control scheme without destructing the performance of systems. Therefore, the iterative learning control algorithm based on the quantized feedback and saturated input is presented to support the design of encrypted controller, and the conditions on the parameters of cryptosystem are established to ensure that the inputs obtained by actuator are reliable. As the results, the calculation of secure control law only relies on the encrypted data, thus immunizing the disclosure attacks of adversaries. To the best of our knowledge, there is currently little work to investigate the secure iterative learning control scheme, which can enhance the practicability and security of systems due to the data-driven strategy and encryption mechanism.

关键词： iterative learning control control systems Quantization (signal) Convergence Signal resolution Public key Privacy networked systems secure control architecture disclosure attacks

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Optimization-based iterative learning control scheme for point-to-point tracking of nonlinear systems

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NONLINEAR DYNAMICS 2025年第3期113卷 2487-2503页

作者： Liu, Chen Ruan, Xiaoe Liu, Yanze Chien, Chiang-Ju Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Univ Wisconsin Madison Dept Mech Engn Madison WI 53706 USA Huafan Univ Dept Technol Smart Living New Taipei City 22301 Taiwan

For a robotic manipulator executing a repetitive carriage task, the primary target is to pick up an object from one position and place it at the intended position. In this circumstance, the manipulator is required to only pass certain key positions. This forms a multi-point-to-point trajectory tracking problem for which the controller only stimulates the manipulator at the predetermined instants. This paper considers an iterative learning control scheme for a nonlinear system with unknown parameters under the premise of minimizing the performance index, which is composed of the output tracking error and the rated input increment. For the optimized control scheme, the unknown system parameters are iteratively updated to obey two criteria: to minimize the output estimation error and the rated parameter estimation increment and to minimize only the output estimation error. A theoretical proof shows that the output tracking error is convergent along the iteration axis. Two numerical simulations are provided to demonstrate the effectiveness of the proposed learning scheme.

关键词： iterative learning control Multi-point-to-point tracking Nonlinear systems Optimization index Parameter estimation

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Experimentally Validated Vector Lyapunov Function-Based iterative learning control Design Under Input Saturation

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2024年第1期32卷 189-201页

作者： Pakshin, Pavel Mandra, Slawomir Emelianova, Julia Rogers, Eric Erwinski, Krystian Galkowski, Krzysztof RE Alekseev Nizhny Novgorod State Tech Univ Arzamas Polytech Inst Arzamas 607227 Russia Nicolaus Copernicus Univ Torun Inst Engn & Technol Fac Phys Astron & Informat PL-87100 Torun Poland Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England Univ Zielona Gora Inst Automat Elect & Elect Engn PL-65516 Zielona Gora Poland

This article addresses the problem of input saturation in iterative learning control (ILC), a problem area that has seen relatively little research effort. A new design is developed based on a branch of 2-D systems theory and, in particular, Lyapunov-based stability theory. The resulting design is compared in simulation with an alternative. In the final part of this article, the results of an in-depth experimental investigation of the new designs properties are reported.

关键词： Trajectory Symmetric matrices Stroke (medical condition) Observers Medical services iterative learning control Convergence control input saturation iterative learning control (ILC) design repetitive processes vector Lyapunov functions

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Predictive fault-tolerant control for nonlinear batch processes with small time delays via iterative learning control: A Lyapunov-Razumikhin approach

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2024年第13期46卷 2521-2533页

作者： Li, Hui Wang, Shiqi Shi, Huiyuan Li, Ping Su, Chengli Univ Sci & Technol Liaoning Sch Elect & Informat Engn Liaoning Peoples R China Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China Liaodong Univ Sch Informat Engn Dandong Peoples R China

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov-Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov-Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

关键词： Batch process iterative learning control fault tolerance control predictive control Lyapunov-Razumikhin function

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Each step time-limited iterative learning control for a cushion robot with motion velocity constraints

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2024年第6期46卷 1105-1119页

作者： Shan, Rui Sun, Ping Wang, Shuoyu Chang, Hongbin Shenyang Univ Technol Sch Artificial Intelligence Shenyang Peoples R China Kochi Univ Technol Dept Intelligent Mech Syst Engn Kochi Japan Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China

An each step time-limited iterative learning control model was developed for a cushion robot with velocity constraints. A predictive modeling method was proposed to solve the velocity mutation problem by employing a kinematic model to constrain the velocity inputs, which can then constrain the robot's actual velocity. Furthermore, a tracking error system was established that used constrained motion velocity and a dynamic model. A new iterative controller with each step time-limited learning was built to reduce the transient adjustment time. Simulation results and comparative analyses revealed that the proposed control method quickly stabilizes the system and ensures that the human-robot system operates at a safe velocity.

关键词： Cushion robot each step time-limited learning motion velocity constraints iterative learning control

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Improved data-driven high-order model-free adaptive iterative learning control with fast convergence for trajectory tracking systems

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2024年第15期46卷 2884-2896页

作者： Huang, Liang Huang, Hua Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou Peoples R China Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou 730050 Peoples R China

The data-driven high-order pseudo-partial derivative-based model-free adaptive iterative learning control (HOPPD-MFAILC) is always slow to converge and difficult to have excellent tracking results. To address the problem, an improved high-order pseudo-partial derivative-based model-free adaptive iterative learning control (iHOPPD-MFAILC) with fast convergence is proposed. First, to reduce the impact of the initial value of the pseudo-partial derivative (PPD) on the convergence speed of the algorithm, the initial PPD is corrected by introducing the high-order model estimation error. Second, to reduce the influence of system noise on the control performance, the original HOPPD-MFAILC control law is improved by introducing time-varying iterative proportional and time-varying iterative integral terms. Then, the convergence of the proposed improved control algorithm is demonstrated by theoretical analysis. Finally, simulations and experiments on the ball screw motion system show that the proposed iHOPPD-MFAILC can track the desired trajectory better. In addition, iHOPPD-MFAILC has better robustness in the noisy environment and achieves better convergence as well as trajectory tracking performance under different initial PPD conditions. The proposed control scheme has excellent application potential in precision motion control.

关键词： Trajectory tracking system data-driven model-free adaptive control iterative learning control convergence

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Pseudo-model-based iterative learning control for nonlinear multi-phase batch processes

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2024年第16期46卷 3169-3181页

作者： Geng, Yan Ruan, Xiaoe Yang, Xuan Xian Polytech Univ Sch Sci Dept Appl Math Xian Peoples R China Jiaotong Univ Sch Math & Stat Dept Appl Math Xian Peoples R China Xian Polytech Univ Sch Sci Dept Appl Math Xian 710048 Peoples R China

In this article, a pseudo-model-based iterative learning control (ILC) is exploited for multi-phase batch processes which can be described as a nonlinear switched system with unknown functions and identical states in different phases. The nonlinear switched system is converted into a linear model whose system parameter matrix is approximated by minimizing the discrepancy from the real system output increment to the approximated system output increment. A data-driven ILC is constructed in an interactive form with system parameter matrix approximate algorithm. Meanwhile, the signs of the diagonal elements of system lower triangular parameter matrix are introduced into the construction of control input law. Theoretical analysis shows that the pseudo-model-based ILC (PM-ILC) concept can be extended to multi-phase batch processes with non-identical states in different phases. Furthermore, the approximation error of the system parameters matrix is bounded and the proposed PM-ILC is robust if the parameter is appropriately chosen. Simulation results illustrate the effectiveness and practicability of the proposed PM-ILC.

关键词： iterative learning control pseudo-model approximation error multi-phase batch processes

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Robust Adaptive iterative learning control for a Generic Class of Uncertain Non-Square MIMO Systems

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第4期69卷 2721-2728页

作者： Li, Xuefang Hou, Zhongsheng Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Guangdong Prov Key Lab Fire Sci & Intelligent Emer Guangzhou 510006 Peoples R China Qingdao Univ Sch Automat Qingdao 266071 Peoples R China

In this work, the adaptive iterative learning control (AILC) for a generic class of nonsquare nonlinear systems is investigated in presence of unknown control gain matrices and nonparametric iteration-varying uncertainties. Differently from the existing approaches, the present work develops a unified, structurally simple and user-friendly AILC method, which is effective to handle nonlinear systems with parametric or nonparametric uncertainties, square or nonsquare input matrices, known or unknown control directions. From the design point of view, the proposed approach extends the AILC approach to nonsquare systems with unknown control gain matrices, which contributes significantly not only to refine the theory of AILC, but also to widen its application scope. The convergence of the proposed control algorithms are analysed rigorously by using the composite energy function methodology, and their effectiveness have been verified through an illustrated example.

关键词： Uncertainty MIMO communication Convergence iterative learning control control systems Task analysis Nonlinear systems Adaptive iterative learning control (AILC) composite energy function mismatched uncertainties nonparametric uncertainties nonsquare systems

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Dissipativity-based iterative learning control for singular systems

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2024年第7期55卷 1410-1419页

作者： Zhang, Meiyu Gu, Panpan Tian, Senping South China Univ Technol Sch Automat Sci & Engn Guangzhou Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei Peoples R China

In this paper, the framework of dissipativity-based iterative learning control for singular systems (SSs) is established for the first time. Firstly, under the P-type learning algorithm, it has been demonstrated that the dissipative SSs can achieve the accurate tracking for the desired trajectory in a finite time interval. Secondly, a sufficient condition for the SSs to satisfy dissipativity is given by using the linear matrix inequality (LMI) method, and the solvability criterion of the LMI is further presented. Moreover, the relationship between dissipativity and positive realness is revealed. Finally, two examples are given to verify the effectiveness of the proposed method.

关键词： Singular systems iterative learning control dissipativity P-type learning algorithm positive realness

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Integrated adaptive iterative learning control based on inter-trial iteration and real-time correction for nonlinear systems with external morphologically-similar disturbances

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JOURNAL OF VIBRATION AND control 2024年第19-20期30卷 4395-4407页

作者： Yang, Lu Chen, Chunjun He, Zhiying Deng, Ji Southwest Jiaotong Univ Sch Mech Engn 111 North 1st SectSecond Ring Rd Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Maintena Chengdu Peoples R China

This paper proposes an adaptive iterative learning control (ILC) scheme for nonlinear systems with iteration-varying trial lengths under external morphologically-similar disturbances, utilising inter-trial iteration and real-time correction. To attenuate external quasi-periodic disturbances and non-repetitive uncertainties, and further achieve better tracking performance along iteration and time axes, the proposed scheme combines the iteration-to-iteration proportional-type (P-type) ILC with the within-iteration P-type scheme. The tracking error with dead zone property and zero filling treatment is constructed. In addition, as opposed to the existing two-dimensional (2D) ILC works, the integrated framework is formed through the connection of adaptive weights, which are calculated by the adaptive weight determination method based on the ideology of the Kalman filter. The convergence of the algorithm is proved based on the contraction mapping principle. Compared with the traditional ILC schemes, illustrative and applicational simulations are provided to demonstrate the effectiveness and the superiority of the proposed framework.

关键词： iterative learning control nonlinear system morphologically-similar disturbances adaptive weights iteration-varying trial lengths

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