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FPGA-Based Adaptive PID controller Using MLP Neural network for Tracking Motion systems

IEEE ACCESS 2024年 12卷 91568-91574页

作者： Ngo, van-Quang-Binh Kim Anh, Nguyen Khanh Quang, Nguyen Hue Univ Univ Educ Fac Phys Thua Thien Hue 530000 Vietnam Univ Danang Univ Sci & Technol Fac Elect Engn Da Nang 550000 Vietnam

In this study, the Field Programmable Gate Array (FPGA) technology is employed to integrate multi-loop controllers for motion systems. To provide precise positioning and trajectory tracking for multi-axis systems, the proportional-integral (PI) control is used in the speed control loop and adaptive PID control in the position control loop. The motion system under consideration comprises an X-Y table driven by permanent magnet synchronous motors (PMSMs), and controlled by two programmable servo systems, each designed to regulate a separate axis. Each axis of this system consists of a motion planning module, a speed PI controller in the inner loop, and an adaptive PID position controller in the outer loop. The adaptive PID controller is specifically designed using a multilayer perceptron (MLP) neural network and parameter tuning methods. The control objective is to enhance trajectory tracking accuracy, especially in the presence of dynamic variations and uncertain disturbances. The Very High-speed IC Hardware Description Language (VHDL) is utilized to implement the desirable features of the control system. The control development is based on an FPGA device using Altera's Quartus ii and Nios ii software environment. The VHDL designs are analyzed and synthesized within this software environment. Simulation results demonstrate that the on-chip control system can achieve accurate positioning and tracking performance for the X-Y table motion.

关键词： Field programmable gate arrays Trajectory control systems Adaptive systems PI control Biological neural networks Artificial neural networks PD control FPGA PMSM drives MLP neural network adaptive PID controller X-Y table

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Adaptive neural network control of robotic manipulators with input constraints and without velocity measurements

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IET control THEORY AND APPLICATIONS 2024年第10期18卷 1232-1247页

作者： Zhang, Heng Zhao, Yangyang Wang, Yang Liu, Lin ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Sun Yat sen Univ Sch Adv Mfg Guangzhou Guangdong Peoples R China

This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems under the effect of external disturbances. The main challenges lie in the input constraints and the lack of measurements of joint velocities. An extend-state-observer is utilized to estimate the velocity signals;then, a neural-network-based adaptive controller is proposed to solve the problem, where a term based on the nominal model is included to enhance the tracking ability, and the effect of uncertainties and disturbances are compensated by a neural-network term. Compared with the existing methods, the main distinctive features of the presented approach are: (i) The control law is guaranteed to be bounded by design, instead of directly bounded by a saturation function. (ii) The trade-off between the performance and robustness of the presented controller can be easily tuned by a parameter that depends on the size of model uncertainties and external disturbances. By virtue of the Lyapunov theorem, the convergence properties of the proposed controller are rigorously proved. The performance of the controller is validated via both simulations and experiments conducted on a two-degree-of-freedom robot manipulator. This paper addresses the trajectory tracking problem for a class of uncertain manipulator systems in the presence of external disturbances, input constraints, and the lack of measurement of joint velocities. The control law is guaranteed to be bounded by design, instead of using a saturation function. The performance of the controller is verified via the simulation and experiment of a Kinova manipulator. image

关键词： adaptive control constraint handling manipulator dynamics tracking uncertain systems

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ICSC: Intent-Based Closed-Loop Security control System for Cloud-Based Security Services

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IEEE COMMUNICATIONS MAGAZINE 2025年第4期63卷 169-175页

作者： Lingga, Patrick Jeong, Jaehoon Paul Dunbar, Linda Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea Sungkyunkwan Univ Dept Comp Sci & Engn Seoul South Korea Futurewei Technol San Jose CA USA

This article proposes an intent-based closedloop security control (ICSC) system for intelligent and effective security service management. Recent advancements in computer network technologies have led to the emergence of intent-based networking (IBN), significantly improving network security management. This article presents novel contributions to IBN, emphasizing intent fulfillment and intent assurance within network security. The proposed approach in this article utilizes a standardized framework called interface to network security functions (I2NSF) with standardized communication protocols and data models, allowing the deployment of security policies across multi-vendor environments. Furthermore, the existing security policy translator for an intent is extended to support dynamic translation, enabling the immediate integration of new security solutions into the network. An analytics component with machine learning is also introduced for continuous network monitoring, proactively identifying anomalies, and triggering automated threat mitigation. Additionally, the ICSC system's performance is assessed in various scenarios and configurations, providing a thorough understanding of its strengths and limitations. Thus, it is shown that the ICSC system can establish robust and adaptive network security management.

关键词： Security Translation Data models network security Monitoring Real-time systems Adaptive systems Protocols Data mining Complexity theory

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Sliding Mode control-Based Synchronization of Complex-Valued Neural networks

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IEEE TRANSACTIONS ON control OF network systems 2024年第3期11卷 1251-1261页

作者： Di Palo, Eugenia Olm, Josep M. Doria-Cerezo, Arnau Bernardo, Mario di Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy Univ Politecn Cataluna Dept Appl Math Barcelona 08028 Spain Univ Politecn Cataluna Inst Ind & Control Engn Barcelona 08028 Spain

Synchronization is a typical dynamical behavior of interconnected systems that is being extensively studied in neural networks. However, most of the research considers real-valued neural networks, and fewer results have been obtained on their complex-valued counterparts. This article presents two sliding mode control strategies to achieve synchronization in a complex-valued neural network (CVNN). The former simplifies an already existing technique that splits the control into real and imaginary parts. The latter extends a fully complex-valued sliding approach for generic complex-valued dynamical systems to the multi-input-multi-output case, and shows its efficiency and higher performance in terms of finite reaching time in the synchronization of CVNNs. The approach is validated via numerical simulations.

关键词： Synchronization Biological neural networks control systems Stability analysis Neurons network systems Mathematical models Complex-valued neural networks (CVNNs) finite-time synchronization sliding modes

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Model-Free Prescribed-Time control Under Input Amplitude and Rate Saturations for Uncertain Mechatronic systems With Mismatched Disturbances

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2024年第1期71卷 201-205页

作者： He, Dingxin Wang, Haoping Tian, Yang Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

This brief considers a trajectory tracking control issue of n-DOF mechatronic systems subject to uncertainties, mismatched disturbances, and input amplitude and rate saturations. In practical mechatronic systems, mismatched disturbances and input amplitude and rate saturations are common phenomenon affecting control performance, which require the development of an advanced and high-performance control strategy. In this brief, a neural network observer (NNO)-based model-free prescribed-time saturated controller (NNO-MFPTSC) is designed which consists of ultra-local model-based adaptive RBF neural network observer, prescribed-time sub-controller, and auxiliary dynamic system. The proposed NNO-MFPTSC that overcomes mismatched disturbances achieves stabilization and convergence within predefined time and provides a torque with amplitude and rate saturations. After that, the stability and prescribed-time convergence are analyzed by using Lyapunov theorem. Finally, the co-simulation of 3-DOF PUMA 560 robotic manipulator based on SolidWorks and MATLAB is realized. The results compared to adaptive intelligent controller (AIC) are given to show the effectiveness and superiority of NNO-MFPTSC.

关键词： Model-free control prescribed-time convergence input amplitude and rate saturations mismatched disturbances neural network observer

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NSGA-ii-based load resource management for frequency and voltage support

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Global Energy Interconnection 2025年第2期8卷 258-268页

作者： Wang, Yaxin Zhu, Zhihang Yu, Zhihong Wang, Zigan China Electric Power Research Institute Beijing100048 China College of Electrical Engineering Zhejiang University Hangzhou310058 China

Ensuring stable frequency and voltage has recently become increasingly challenging for modern power systems. This is primarily due to the fluctuating and intermittent nature of renewable energy sources and the uncertain electricity demand. To address these issues, this study proposes a load resource management (LRM) method to cope with the sudden power disturbances. The LRM method supports primary frequency and voltage regulation, and its integration with network dynamics minimizes the established disutility function caused by load participation. For better control performance, a non-dominated sorting genetic algorithm-ii (NSGA-ii)-based gain-tuning procedure was utilized for LRM, aiming to enhance the frequency/voltage nadir, reduce the frequency/voltage steady-state error, and minimize the total load control efforts. To validate the effectiveness of the proposed approach, comparative experiments were conducted with three load–resource management technologies for primary regulation auxiliary services in MATLAB/Simulink. Compared to the conventional optimal load control or using LRM alone, the improved NSGA-ii-based LRM demonstrates superior performance. It achieves better frequency response, voltage transients, and steady-state responses, while also considering disutility. © 2025

关键词： network model Load resource management Primary response Smart grid Multi-objective optimization

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SOTIF-Oriented Distributed control for Connected Autonomous Vehicles Platoons

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第4期55卷 3049-3063页

作者： Ge, Xiaohua Lui, Dario Giuseppe Motta, Carlo Petrillo, Alberto Santini, Stefania Swinburne Univ Technol Sch Sci Comp & Engn Technol Melbourne Vic 3122 Australia Univ Naples Federico II Distributed Automat Syst Lab I-80125 Naples Italy Univ Naples Federico II I-80125 Naples Italy

This article extends the problem of safety of the intended functionality (SOTIF) to the platooning of connected autonomous vehicles (CAVs). In such multivehicle systems, SOTIF objectives are particularly challenging, since new issues arise due to the presence of interactions that propagate and intensify the effects and the risk connected to faults/failures/uncertainties, triggered by any platoon member and external conditions. To tackle and solve the SOTIF-oriented platoon control problem, we propose a robust and resilient decentralized control protocol, based on distributed observer which exploits the information shared via the Vehicle-to-Everything (V2X) communication network. The approach allows maintaining acceptable SOTIF performance in adversarial conditions, mitigating the effects of the unreasonable risks originated from sensors/actuators performance limitations/faults and disturbances. The uniformly ultimate bounded (UUB) stability and the input-to-state string stability (ISSS) of the platoon dynamics are analytically proved via the Lyapunov theory. Feasible linear matrix inequalities (LMIs) are obtained as stability conditions, whose solution allows adapting the distributed control/observer gains according to leader-tracking performance and robustness margins. A detailed analysis via the vehicular co-simulation platform mixed traffic simulator (MiTraS) corroborates the theoretical derivations and shows the achievement of the SOTIF platoon goals for a set of nontrivial and uncertain driving scenarios.

关键词： Actuators Vehicle dynamics Sensors Autonomous vehicles Linear matrix inequalities Uncertainty Hazards Fault tolerant systems Stability criteria Robustness Connected autonomous vehicles (CAVs) platoon control safety of the intended functionality (SOTIF)

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Large-Scale Reverse Pumping for Rigid-Wing Airborne Wind Energy systems

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JOURNAL OF GUIDANCE control AND DYNAMICS 2024年第8期47卷 1748-1758页

作者： Mohammed, Tareg Busk, Jorgen Oland, Espen Fagiano, Lorenzo Polytech Univ Milan I-20133 Milan Italy Kitemill AS N-5704 Voss Norway UiT Arctic Univ Norway N-8514 Narvik Norway Polytech Univ Milan I-20133 Milan Italy

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Regret-Optimal Cross-Layer Co-Design in networked control systems-Part I: General Case

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IEEE COMMUNICATIONS LETTERS 2023年第11期27卷 2874-2878页

作者： Mamduhi, Mohammad H. Maity, Dipankar Johansson, Karl H. Lygeros, John Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland Univ North Carolina Charlotte Dept Elect & Comp Engn Charlotte NC 28223 USA KTH Royal Inst Technol Div Decis & Control Syst S-11428 Stockholm Sweden Digital Futures S-10044 Stockholm Sweden

performance of control systems interacting over a shared communication network is tightly coupled with how the network provides services and distributes resources. Novel networking technology such as 5G is capable of providing tailored services for a variety of network demands. Stringent control requirements and their critical performance specification call for online adaptable and control-aware network services. This perspective suggests a co-design of physical and network layers aiming to ensure that the necessary quality-of-service is provided to achieve the desired quality-of-control. An optimal co-design is in general challenging due to cross-layer couplings between the physical and network layers and their layer-specific functionalities. Furthermore, the complexity of the co-design depends on the level of actionable information the layers share with each other. In this Part I of a two-letter series, we present a general co-design of physical operations and service allocation aiming to minimize a social regret measure for networked control systems. We introduce an optimal networked co-design scenario using the regret index as the joint quality-of-control and quality-of-service (QoC-QoS) measure, and discuss the role of cross-layer awareness in the structure of optimization problems. We mainly focus on the finite-horizon case but we briefly present the infinite-horizon case as well. In Part ii, we discuss regret-optimal cross-layer policies for Gauss-Markov systems and derive the optimal solutions based on the general problems introduced in Part I.

关键词： Regret-optimal co-design networked systems service allocation QoC-QoS trade-off awareness structure

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Multilayer Neuroadaptive Output Feedback control of Constrained Robotic Manipulators With Disturbance Compensation

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IEEE TRANSACTIONS ON CIRCUITS AND systems ii-EXPRESS BRIEFS 2024年第2期71卷 727-731页

作者： Yang, Guichao Shi, Zhiying Wang, Hua Yang, Fengbo Nanjing Tech Univ Sch Mech & Power Engn Nanjing 211816 Peoples R China Nanjing Forestry Univ Coll Mech & Elect Engn Nanjing 210037 Peoples R China

This brief focuses on the motion control issue for uncertain robotic manipulators with unknown nonlinear dynamics and exogenous disturbances simultaneously via the output position measurement only. In detail, the extended state observer is employed to obtain the estimations of immeasurable system state information and lumped disturbances simultaneously. Meanwhile, the multilayer neural network with good approximation performance is incorporated into the observer-controller scheme to identify the unknown nonlinear dynamics. As a result, both large nonlinear dynamics and strong lumped disturbances can be compensated feedforwardly. Significantly, prescribed tracking performance and asymmetric time-varying state constraints can be realized simultaneously.

关键词： Uncertainty Output feedback Observers Manipulator dynamics Time-varying systems Nonhomogeneous media Neural networks Extended state observer multilayer neural network output feedback control robotic manipulator prescribed performance state constraint

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