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Dynamic control Allocation for a Twin Ducted Fan UAV

Dynamic Control Allocation for a Twin Ducted Fan UAV

引用

International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Meng, Chaoheng Pei, Hailong Cheng, Zihuan Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong South China University of Technology Guangzhou510640 China

ISBN: (纸本)9789811581540

Twin ducted fan UAV is a novel aircraft with redundant control effectors and the interactions between actuators and control allocators can not be ignored. In this paper, dynamic control allocation is used to deal with the control allocation problem of Twin ducted fan UAV. First, the actuator rate is penalized in the optimization function of the control allocation based on the quadratic programming method. Second, a post-compensation method is applied to compensate for command increment attenuation. The simulation results show that the dynamic control allocation method with actuator dynamic compensation can prevent the high bandwidth actuator from saturating in advance, and the virtual control command is effectively allocated to actuators with different bandwidths. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Actuators

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MARS: Multi-Agent Deep Reinforcement Learning for Real-Time Workflow Scheduling in Hybrid Clouds with Privacy Protection 30

MARS: Multi-Agent Deep Reinforcement Learning for Real-Time ...

引用

30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Cheng, Long He, Haoyang Gu, Yan Liu, Qingzhi Zhao, Zhiming Fang, Fang North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering Beijing China Wageningen University and Research Information Technology Group Netherlands University of Amsterdam Research Group of Multi-Scale Networked Systems Netherlands

ISBN: (纸本)9798331515966

Scheduling workflows in hybrid cloud environments presents significant challenges due to the inherent complexity of workflows and the dynamic nature of cloud resources. This complexity is further increased when attempting to balance workflow performance with privacy protection. Recent efforts have leveraged deep reinforcement learning (DRL) to address these challenges. However, most of these approaches rely on single-agent models, which can lead to security issues and scalability problems due to their centralized processing. Specifically, the properties of workflows are transferred to the single agent, which risks leaking privacy information. Our paper addresses these issues by introducing MARS, a real-time workflow scheduling method that prioritizes privacy protection in hybrid clouds. MARS leverages multi-agent deep reinforcement learning (MADRL) to optimize the workflow scheduling of cloud virtual machines (VMs). The benefit of our solution is that it relies on the collaborative learning of multi-agents on multiple VMs, which could assign user data to specific cloud servers for privacy protection while sharing training experiences between agents. In our implementation, MARS aims to reduce workflow completion time and operational costs while complying with strict privacy protection guidelines. The experimental results demonstrate that MARS can significantly surpass existing methods, reducing makespan by an average of 53.18% and costs by 61.98% compared to basic techniques, and achieving 20.26% and 25.71% improvements over the latest advanced methods, respectively. © 2024 IEEE.

关键词： Deep reinforcement learning

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Semi-Global Robust Practical Output Voltage Tracking for Single-Phase UPS Inverters via Sampled-Data control

Semi-Global Robust Practical Output Voltage Tracking for Sin...

引用

第42届中国控制会议

作者： Kaiji Zeng Wei Liu Weijie Sun Weidong Liu Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

This paper deals with the sampled-data semi-global robust practical output voltage tracking problem for single-phase voltage source uninterruptible power supply *** construct an aperiodic sampled-data state feedback control law in an internal model principle *** analysis shows that as long as the upper bound of the sampling time interval of the controller is less than an explicitly computable threshold,our problem can be handled by the proposed sampled-data control *** the tracking error is guaranteed to approach a given neighborhood of the origin asympotically.

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Event-Triggered Output Voltage Practical Tracking control for Uninterruptible Power Supply systems

Event-Triggered Output Voltage Practical Tracking Control fo...

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第35届中国控制与决策会议

作者： Ben Wu Wei Liu Weijie Sun Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of Technology

In this paper,by using the output regulation theory,we research the output voltage practical tracking problem for uninterruptible power supply systems under event-triggered control *** configuring the desired poles for a controllable pair and solving the regulator equations,a state feedback gain and a feedforward gain can be obtained for designing the control ***,under event-triggered mechanism,a control law that can be applied to a digital platform directly is designed to solve our ***,it can be verified that the voltage tracking error can converge to a pre-specified neighborhood of the origin.

关键词： Uninterruptible power supply output regulation event-triggered control

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Natural Gas Load Forecasting Based on Multi-Stage Model Identification 14

Natural Gas Load Forecasting Based on Multi-Stage Model Iden...

引用

14th IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems, CYBER 2024

作者： Zhang, Pengbin Wang, Hongrui Xing, Kunpeng Yuan, Mingzhe Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China China Nuclear Power Engineering Co. Ltd. Shenzhen518124 China

ISBN: (纸本)9798331506056

Natural gas load forecasting is crucial for the safe operation of urban natural gas. Based on the system identification method, the short-term natural gas load forecasting is studied in the paper. Employing a multi-stage identification framework, the initial values of the model parameters using the least squares and the instrumental variables method, while the model parameters are further optimized by the prediction error method to improve the accuracy of the model identification. The proposed method is validated by the simulation based on the public natural gas data in the U.S., demonstrating the algorithm's effectiveness in forecasting the daily natural gas load effectively. © 2024 IEEE.

关键词： Prediction models

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A NOVEL PID SELF-TUNING METHOD BASED ON CLOSED-LOOP IDENTIFICATION USED IN NUCLEAR POWER PLANTS 29

A NOVEL PID SELF-TUNING METHOD BASED ON CLOSED-LOOP IDENTIFI...

引用

2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Zhang, Pengbin Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Wang, Hongrui State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886472

Nuclear power is one of several significant clean energy and its production capacity is increasing rapidly. This paper proposed a novel PID self-tuning method based on closed-loop identification used in nuclear power plants to deal with the turbine's load tracking problem. A setpoint-shifting method is adopted to achieve the informativity of the closed-loop system, and the PID parameters are designed through an optimization method. For validating the proposed method, this paper modeled a pressurized-water reactor (PWR) nuclear power plant, a complex system consisting of three circuits, through the first principle. The primary circuit contains three main devices: a nuclear reactor, a pressurizer, and a steam generator. The models of these three devices are provided by previous literature separately. The whole model is generated through mass and energy balance. This mechanism model is used as the plant model, while the identified model is used as the control model. The initial PID parameters are set through open-loop identification using an external excitation. Then the proposed method works online to track the turbine's load in time. The effectiveness of the proposed method is illustrated by simulations. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Extended State Observer Based Fault-Tolerant Predictive control for a Four-Mecanum Wheels Mobile Robot 14

Extended State Observer Based Fault-Tolerant Predictive Cont...

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14th IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems, CYBER 2024

作者： Yang, Binghao Wang, Dongliang Wen, Ziling Wei, Wu Li, Wenji Fan, Zhun Shantou University Key Lab of Digital Signal and Image Processing of Guangdong Province Guangdong 515063 China Shantou University School of Department of Electronic and Information Engineering Guangdong 515063 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Guangdong Guangzhou510640 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China

ISBN: (纸本)9798331506056

The application of unmanned vehicles in industrial cargo transportation is becoming increasingly widespread, particularly playing a significant role in transporting heavy-duty goods. However, when performing such tasks, whether in single-vehicle transportation or multi-vehicle collaborative transportation, the occurrence of wheel failures can potentially lead to serious safety accidents. Therefore, this paper proposes a fault-tolerant control scheme, establishing a kinematic model considering disturbances and failures, and adopts model predictive control for trajectory tracking. Additionally, an extended state observer is introduced to observe system states and counteract disturbances. To address potential failure issues, a controller reconstruction strategy is proposed. Finally, the effectiveness and superiority of the controller is validated through numerical simulation and comparative experiment. © 2024 IEEE.

关键词： Mobile robots

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A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

A discrete moth-flame optimization algorithm for multiple au...

引用

作者： Zeng, Junhai Xie, Wei Pan, Mi School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangdong Guangzhou510641 China Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing South China University of Technology Guangdong Guangzhou510641 China Department of Civil and Environmental Engineering University of Macau 999078 China

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more

关键词： Materials handling

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RESEARCH ON THE PID PARAMETER SELF-TUNING OF THE PRESSURIZER control system OF NUCLEAR POWER PLANT 29

RESEARCH ON THE PID PARAMETER SELF-TUNING OF THE PRESSURIZER...

引用

2022 29th International Conference on Nuclear Engineering, ICONE 2022

作者： Wang, Hongrui Xing, Kunpeng Zou, Tao Yang, Zhijia Wang, Jingyang Zhang, Pengbin State Key Laboratory of Robotics Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Nuclear Power Engineering Co. Ltd Shenzhen China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Key Laboratory of Networked Control System Institutes for Robotics and Intelligent Manufacturing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9780791886373

The pressurizer is an essential part of the safe operation of the nuclear power plant. It contains both the steam phase and the liquid phase, which requires simultaneous control of the pressure and water level, and the control system is strongly coupled. When using the PID control method, it is difficult to tune the parameters, and the difficulty is exacerbated when the set value fluctuates. This paper aims at the multi-input and multioutput pressurizer control system, based on the particle swarm optimization to realize the PID controller parameters self-tuning, adopting the time and absolute error integral as the fitness function to evaluate the merits of the PID parameters, and constantly iterating to update and obtain the PID parameters that better satisfy the current working conditions. For coupled pressurizer control systems, the PID parameter tuning method using particle swarm optimization algorithms can give a group of PID controller parameters that make the system operation stable despite the frequent fluctuation of the set value, and compared with the traditional PID controller parameter tuning method, this method can realize the PID parameter self-tuning, eliminating the trouble of manual tuning. Copyright © 2022 by ASME.

关键词： Nuclear power plants

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Prescribed Time Performance Security Tracking control of Quadrotors Under Unknown Attacks

Prescribed Time Performance Security Tracking Control of Qua...

引用

第43届中国控制会议

作者： Liping Wang Hailong Pei Key Laboratory of Autonomous Systems and Networked Control Ministry of EducationGuangdong Engineering Technology Research Center of Unmanned Aerial Vehicle SystemsSouth China University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper investigates the prescribed time performance security tracking control problem of quadrotors under unknown attacks. First,by using a prescribed time performance function and coordinate transformation,the tracking error can remain within a given region in a prescribed time. In the backstepping design process,a first-order sliding mode differentiator is given to surmount the problem of "the explosion of complexity",and the Nussbaum function is developed to deal with the unknown attacks problem. A prescribed time performance security attitude tracking control method is designed to prove the boundedness of the closed-loop signals. Finally,the simulation results are enumerated to state the superiority and effectiveness of the designed control method.

关键词： quadrotor unmanned aerial vehicles unknown deception attack backstepping Nussbaum function

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