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37th Chinese Control Conference (CCC)

作者： Tan, Fuxiao Guan, Xinping Fuyang Normal Univ Sch Comp & Informat Engn Fuyang 236037 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China

ISBN: (纸本)9789881563958

Because it is necessary to solve the nonlinear Hamilton-Jacobi-Bellman (HJB) partial differential equations or the nonlinear two-point boundary value problems, the optimal control of nonlinear system is quite difficultly. Based on adaptive dynamic programming ( ADP) and adaptive critic designs (ACDs), the optimal control of nonlinear system theory has been intensive studied. However, based on neural network, the ADP still have the problem of network structure design and learning parameters optimization. Because the kernel function can reflect the similarity among data by the form of inner product of data, the traditional linear learning algorithm can be extended to nonlinear theoretical framework by kernel function. Based on online kernel learning, the kernel-based adaptive critic designs is studied in this paper. Then, the critic of ADP is designed by using the kernel recursive least square algorithm. Thus, the realization process of the KHDP and KDHP algorithms are obtained. Finally, example and computer simulation are shown that the kernel-based ACDs can effectively solve the optimal control of nonlinear discrete-time system.

关键词： Adaptive Dynamic Programming (ADP) Adaptive Critic Designs (ACDs) Online Kernel Learning nonlinear discrete-time system Optimal Control

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Neural network-based AILC for nonlinear discrete-time system with iteration varying initial error and reference trajectory 5

Neural network-based AILC for nonlinear discrete-time system...

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5th International Conference on Information Science and Control Engineering (ICISCE)

作者： Xu, Qing-Yuan Xu, Peng Sun Yat Sen Univ Nanfang Coll Sch Elect & Comp Engn Guangzhou Guangdong Peoples R China China Elect Prod Reliabil & Environm Testing Res Software Qual Engn Res Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538655009

Based on the neural network approximator, an online adaptive iterative learning control (AILC) algorithm is proposed for a class of nonlinear discrete-time system. Here, the random iteration initial error and trajectory reference are considered. The nonlinear system is transformed to a predictor form, and the desired control signal is thus achieved by implicit function theorem. A neural network using only a norm adaptation law is utilized to approximate this desired control signal iteratively. By using Lyapunov analysis, it is proven that all the system signals are bounded and the system tracking error converges to a neighborhood around zero as iteration number goes to infinity. In contrast to the existing AILC results, the most advantage of the proposed neural network-based AILC is that the number of the adjustable parameters is highly reduced.

关键词： nonlinear discrete-time system neural network adaptive iterative learning control (AILC) norm adaptation law

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Kernel-based Adaptive Critic Designs for Optimal Control of nonlinear discrete-time system

Kernel-based Adaptive Critic Designs for Optimal Control of ...

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第37届中国控制会议

作者： Fuxiao Tan Xinping Guan School of Computer and Information Engineering Fuyang Normal University Department of Automation Shanghai Jiao Tong University

Because it is necessary to solve the nonlinear Hamilton-Jacobi-Bellman(HJB) partial differential equations or the nonlinear two-point boundary value problems, the optimal control of nonlinear system is quite difficultly. Based on adaptive dynamic programming(ADP) and adaptive critic designs(ACDs), the optimal control of nonlinear system theory has been intensive studied. However, based on neural network, the ADP still have the problem of network structure design and learning parameters optimization. Because the kernel function can reflect the similarity among data by the form of inner product of data,the traditional linear learning algorithm can be extended to nonlinear theoretical framework by kernel function. Based on online kernel learning, the kernel-based adaptive critic designs is studied in this paper. Then, the critic of ADP is designed by using the kernel recursive least square algorithm. Thus, the realization process of the KHDP and KDHP algorithms are obtained. Finally,example and computer simulation are shown that the kernel-based ACDs can effectively solve the optimal control of nonlinear discrete-time system.

关键词： Adaptive Dynamic Programming(ADP) Adaptive Critic Designs(ACDs) Online Kernel Learning nonlinear discrete-time system Optimal Control

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Fully Actuated system Approaches: Predictive Elimination Control for discrete-time nonlinear time-Varying systems With Full State Constraints and time-Varying Delays

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IEEE TRANSACTIONS ON CIRCUITS AND systemS I-REGULAR PAPERS 2024年第1期71卷 383-396页

作者： Wang, Xiubo Duan, Guangren Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China Southern Univ Sci & Technol Ctr Control Sci & Technol Shenzhen 518055 Peoples R China

This paper concentrates on the optimal tracking control for a discrete-time nonlinear time-varying fully-actuated system (FAS) with full state constraints and time-varying delays. An explicit analytical predictive controller is constructed by incorporating the predictive control scheme and the constraint elimination technique into FAS approaches. The proposed PEC-FAS scheme dexterously eliminates full state constraints without introducing new constraints and uncertainties, makes full use of full-actuation property to compensate the time-delay actively. Especially, it reduces the coupling degree of the states, and eliminates the nonlinearities arising from the original system and constraints elimination process simultaneously. These efforts largely improve the solvability of the strongly nonlinear and coupled optimization problem with constraints and time-delay. Meanwhile, by analytically expressing the predictive information by off-line calculation, the nonlinear optimization problem is converted into a series of linear convex optimization problems without constraints and time-delay, which fundamentally mitigates the computational burden and the design complexity compared with the previous nonlinear predictive control approaches. Theoretically, it is demonstrated that the proposed controller is recursively feasible, which also owns a recursive explicit analytical predictive controller sequence in each predictive horizon, and the tracking error system is asymptotically stable under the certain condition with predictive parameters. Finally, the simulation of a benchmark under-actuated application of a rotational translational actuator (RTAC) system, demonstrates the effectiveness and the simplicity of the proposed PEC-FAS scheme.

关键词： Optimization Predictive control time-varying systems Predictive models Delays nonlinear systems Complexity theory Fully actuated system approaches predictive elimination control full state constraints time-varying delays nonlinear discrete-time system benchmark under-actuated application

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Output fluctuation and overshoot restraining for a class of nonlinear discrete-time systems using a modified model-free adaptive control

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第10期47卷 1964-1973页

作者： Wang, Peng Liu, Lanqing Xia, Yu Wang, Yuan Tang, Yanling Yangzhou Univ Sch Mech Engn 196 Huayang Rd Yangzhou 225127 Jiangsu Peoples R China

This paper modifies the prototype model-free adaptive control (PMFAC) approach in which system output fluctuation and overshoot (SOFO) is easy to occur. Targeting a class of nonlinear discrete-time single-input single-output systems, historical input/output sensor data are utilized to online construct equivalent explicit data model for control design, making the controller model-free. Then, a novel compensation input term including a compensation gain and a signum function parameterized by incremental output is proposed to soften system inputs, which can in turn restrain the SOFO. Numerical simulations and a speed control experiment for a direct current motor verify that the modified approach can restrain the SOFO successfully and reduce response time compared with the PMFAC.

关键词： Output fluctuation and overshoot model-free adaptive control single-input single-output systems data models nonlinear discrete-time system

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Multidimensional-Taylor-network-based robust optimal tracking control for MIMO nonlinear discrete-time systems

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ISA TRANSACTIONS 2023年 142卷 606-614页

作者： Sun, Qiming Yan, Ying Xu, Yihan Yu, Jingjing Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China Nanjing Univ Informat Sci & Technol Sch Automat C MEIC CICAEET Nanjing 210044 Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Jiangsu Peoples R China East China Elect Power Design Inst Co Ltd China Power Engn Consulting Grp Shanghai 200001 Peoples R China

In practical engineering, system control is indispensable. However, due to the influence of model uncertainty, speed unavailability, input nonlinearity (such as actuator dead zone/fault), and multi-input coupling, the control results are not satisfactory. In this paper, a robust optimal tracking control strategy is proposed for a class of nonlinear multi-input-multi-output discrete-time systems with unknown uncertainties. This control strategy is to minimize the cost function in the process of uncertainty processing and stabilize the closed-loop system by establishing an adaptive controlling approach based on a combination of actor MTN and critic MTN based on the Multi-dimensional Taylor Network (MTN). By using the approximation property of MTN, the optimal control signal is generated by action MTN, which is used to approach the controller, and the cost function is approximated by critic MTN, which is tuned online because the cost function cannot be obtained in hands-on experience. By designing a new cost function, the amount of calculation in the control process is reduced, and the adaptive critic design control idea is integrated into the controller design to deal with the uncertainty of the system. The simulation results verify the effectiveness of the control strategy proposed in the essay. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： nonlinear discrete-time system Optimal control Optimization of performance index Multi-dimensional Taylor network

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Feedback Feedforward Iterative Learning Control for Networked nonlinear system Under Iteratively Variable Trial Lengths and Data Dropouts

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TSINGHUA SCIENCE AND TECHNOLOGY 2025年第5期30卷 1897-1910页

作者： Wei, Yunshan Xiong, Sixian Shang, Wenli Guangzhou Univ Sch Elect & Commun Engn Guangzhou 510006 Peoples R China

This paper proposed a feedback feedforward Iterative Learning Control (ILC) law for nonlinear system with iteratively variable trial lengths under a networked systems structure, where the both sensor and actuator occurs random data lost separately. The feedforward ILC part includes the calculated input signal, actual input signal, and the modified tracking error of last iteration. Some tracking signal would be lost at last iteration because of the iterative varying trial lengths. In order to offset the missing signal of last trial, the tracking error of present trial is adopted by feedback control part. It is established that the convergence relied on the feedforward control gain merely, while the rate of convergence is also expedited by the feedback control component. When the initial state expectation equals to the reference one, it is established that the tracking error expectation can be controlled to zero. With an illustrative simulation, the effectiveness of the developed algorithm can be demonstrated.

关键词： iterative learning control variable trial lengths data dropouts feedback control nonlinear discrete-time system iterative learning control variable trial lengths data dropouts feedback control nonlinear discrete-time system

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A Data-Driven ILC Framework for a Class of nonlinear discrete-time systems

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第7期52卷 6143-6157页

作者： Yu, Xian Hou, Zhongsheng Polycarpou, Marios M. Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing 100044 Peoples R China Univ Cyprus KIOS Res & Innovat Ctr Excellence CY-1678 Nicosia Cyprus Qingdao Univ Sch Automat Qingdao 266071 Peoples R China Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus

In this article, we propose a data-driven iterative learning control (ILC) framework for unknown nonlinear nonaffine repetitive discrete-time single-input-single-output systems by applying the dynamic linearization (DL) technique. The ILC law is constructed based on the equivalent DL expression of an unknown ideal learning controller in the iteration and time domains. The learning control gain vector is adaptively updated by using a Newton-type optimization method. The monotonic convergence on the tracking errors of the controlled plant is theoretically guaranteed with respect to the 2-norm under some conditions. In the proposed ILC framework, existing proportional, integral, and derivative type ILC, and high-order ILC can be considered as special cases. The proposed ILC framework is a pure data-driven ILC, that is, the ILC law is independent of the physical dynamics of the controlled plant, and the learning control gain updating algorithm is formulated using only the measured input-output data of the nonlinear system. The proposed ILC framework is effectively verified by two illustrative examples on a complicated unknown nonlinear system and on a linear time-varying system.

关键词： time-domain analysis Data models Control systems Convergence nonlinear dynamical systems Technological innovation Optimization methods Data-driven iterative learning control (ILC) dynamic linearization (DL) Newton-type optimization method nonlinear discrete-time system

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State-space modelling approach for control and observer design in property-controlled reverse flow forming 11

State-space modelling approach for control and observer desi...

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11th Vienna International Conference on Mathematical Modelling (MATHMOD)

作者： Kersting, Lukas Arian, Bahman Vasquez, Julian Rozo Traechtler, Ansgar Homberg, Werner Walther, Frank Fraunhofer IEM D-33102 Paderborn Germany Paderborn Univ Forming & Machining Technol LUF D-33098 Paderborn Germany TU Dortmund Univ Chair Mat Test Engn WPT D-44227 Dortmund Germany Paderborn Univ Heinz Nixdorf Inst D-33102 Paderborn Germany

Flexible, metal forming processes such as flow forming are exposed to many disturbances that affect geometry and material properties during forming and reduce the reproducibility of those processes. In this context, closed-loop property control is a feasible solution for process improvement. To include advanced controllers and observers in closed-loop property control concepts, mathematical models in terms of state-space models are advantageous. However, especially in case of flow forming these models are not spread due to the complex process kinematics, nonlinearity of the process and spatial dimensions of the workpiece. This makes mathematic state-space modelling challenging. Thus, a model has to be newly developed. This paper therefore presents a novel discrete-time state-space model for flow forming. The model treats the workpiece geometry as discrete and represents the complex process kinematics in the form of a local sampling of the roller trajectory. It can be shown that the model is valid and that the resulting system dynamics especially results the from the displacement between roller and sensor which is considered in the model. Thus, the result of this paper is a valid state-space model that can be prospectively used in a model-based state observer for flow forming. This state observer could later extent a closed-loop property control to enable more complex property structures than before. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Closed-loop property control flow forming state-space model nonlinear discrete-time system

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Event-based adaptive NN controller design for strict-feedback discrete-time nonlinear systems with input dead zone and saturation

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INTERNATIONAL JOURNAL OF CONTROL 2022年第1期95卷 218-233页

作者： Xu, Wenqi Liu, Xiaoping Wang, Huanqing Zhou, Yucheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan Peoples R China Lakehead Univ Fac Engn Thunder Bay ON P7B 5E1 Canada

In this paper, an event-based adaptive neural network controller design method is proposed for a type of uncertain strict-feedback discrete-time nonlinear systems. This system contains uncertain functions and has input nonlinearities in the form of saturation and non-symmetric dead zone. Both event-triggered policy and adaptive law are considered. Radial basis function neural networks are employed to accomplish function approximation. Input dead zone and saturation are estimated by a summation of a known affine function and a bounded unknown function. A stabilising controller and adaptive law are designed via backstepping. The stability of the controlled systems is elaborated via the difference Lyapunov analysis method. Simulation results are given to verify the effectiveness of the proposed design scheme.

关键词： nonlinear discrete-time system dead zone saturation backstepping event-triggered

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