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47th IEEE-Computer-Society Annual International Conference on Computers, Software, and Applications (COMPSAC)

作者： Aung, Han Nay Oto, Ketta G. Ohsaki, Hiroyuki Kwansei Gakuin Univ Grad Sch Sci & Technol Sanda Hyogo 6691330 Japan

ISBN: (纸本)9798350326970

MultiPath TCP (MP-TCP) allows the use of multiple paths between two end hosts for a single data transmission, extending the capabilities of SinglePath Transmission control Protocol (SP-TCP). AIMD congestion control algorithm can be implemented on each subpath of the MP-TCP sender. However, the characteristics of the AIMD-type window flow control depend on the control parameters (alpha, beta). There have been some guidelines proposed to select proper control parameters (alpha, beta) that can achieve fair bandwidth sharing between SP-TCP and MP-TCP senders using existing MP-TCP congestion algorithms. However, current guidelines do not offer a solution that can simultaneously increase the throughput of an MP-TCP sender and ensure fairness between MP-TCP and SP-TCP senders. To address this issue, this study proposes a control parameter setting for an MP-TCP sender that can maximize the sender's throughput and ensure fairness between SP-TCP and MP-TCP senders. We derive a fluid model of a network that includes SP-TCP and MP-TCP senders, describing the relationship between control parameters, the aggregate throughput of an MP-TCP sender, and the packet loss rate of a router.

关键词： MultiPath TCP MultiPath AIMD-based Window Flow control control parameter tuning Fluid Model Fairness Throughput

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Comparing Parallel and Sequential control parameter tuning for a Powered Knee Prosthesis

Comparing Parallel and Sequential Control Parameter Tuning f...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wen, Yue Brandt, Andrea Liu, Ming Huang, He (Helen) Si, Jennie NCSU UNC Joint Dept Biomed Engn Raleigh NC 27695 USA ASU Dept Elect Comp & Energy Engn Tempe AZ USA

ISBN: (纸本)9781538616451

Powered knee prostheses, compared to traditional energetically-passive knee prostheses, greatly enhance the mobility of transfemoral amputees. However, powered prostheses have a large number of control parameters that must be adjusted for individual amputee users, which presents a gnat challenge for clinical use. To address this challenge, we proposed and compared 2 automatic tuning strategies (i.e. parallel and sequential) using our newly developed optimal adaptive dynamic programming (ADP) tuner that objectively tuned the control parameters of an experimental powered knee prosthesis to mimic the knee profile of an able-bodied person (i.e. reference profile). With the parallel tuning strategy, we tuned all control parameters during the stance and the swing phases simultaneously. With the sequential tuning strategy, we alternately tuned stance or swing phase control parameters while fixing the remaining parameters. One able-bodied subject with a prosthesis adapter and one transfemoral amputee subject walked with the experimental powered knee prosthesis under both tuning strategies. Results show that with both tuning strategies, the ADP tuner successfully tuned the impedance parameters to match the prosthetic knee profile to the reference profile. Additionally, the parallel strategy outperformed the sequential strategy with better convergence to the reference profile. Interestingly, with the sequential tuning strategy, tuning during the swing phase greatly impacted the subsequent stance phase profile, but the impact was not as great when the order of tuning was switched. The ability to simultaneously adjust all control parameters with ADP using a parallel strategy may be a preferred solution for the current high-dimension control challenge, which may lead to more advanced, adaptive powered knee prostheses.

关键词： Machine Learning Reinforcement Learning Finite State Machine Impedance control Transfemoral Amputee Gait control parameter tuning

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A New parameter tuning Method of Flocking control based on A Dual-Loop Model Predictive control Structure 4

A New Parameter Tuning Method of Flocking Control based on A...

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4th Modeling, Estimation, and control Conference (MECC)

作者： Wang, Gang Chen, Yan Arizona State Univ Mesa AZ 85212 USA

Although flocking control of multi-agent mobile systems is extensively studied in the literature, the control performance is often limited or varied by the tuning of control parameters. Inappropriate parameter selection can greatly impact the convergence speed of flocking and may even result in flocking failure. Because of the conflicting features of flocking control parameters, such as those for attractive and repulsive forces, and the increased control parameters with the number of agents, simultaneous control parameter tuning toward an optimized flocking behavior of a large group of multi-agent systems faces real challenges. A dual-loop model predictive control (MPC) structure is proposed to address this challenge in a systematic manner. Unlike common MPC that directly gives control design, the proposed approach optimizes the flocking control parameters within the MPC framework and then feeds these optimized parameters into the flocking controller to generate control inputs. This novel method enhances the performance of flocking controllers through a dual-loop MPC structure. Simulation results demonstrate that the proposed approach maintains the inherent properties of the designed flocking controllers while leveraging the adaptability and optimality of MPC for parameter tuning. Copyright (C) 2024 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： Model predictive control flocking control control parameter tuning optimization

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A New parameter tuning Method of Flocking control based on A Dual-Loop Model Predictive control Structure

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IFAC-PapersOnLine 2024年第28期58卷 390-395页

作者： Gang Wang Yan Chen Arizona State University Mesa AZ 85212 USA

关键词： Model predictive control flocking control control parameter tuning optimization

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Modeling, control and Guidance of an Autonomous Wheeled Mobile Robot AWMR: A Comparative Study between Three Yaw Moment control Techniques for Autopilot Design with Experimental Results

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UNMANNED SYSTEMS 2025年

作者： Abrougui, Helmi Nejim, Samir Naval Acad Res Lab Marine Technol & Naval Syst Menzel Bourguiba 7050 Tunisia

This paper presents the development of an autopilot system for self-driving an autonomous wheeled vehicle. A mathematical model, including the power allocation system, has been designed for a vehicle with three degrees of freedom. All model parameters have been identified through experimental trials. Heading and speed controllers were designed based on Lyapunov theory. These controllers have been further fine-tuned and tested through simulations to verify their robustness against external disturbances in the system dynamics. Moreover, this work proposes guidance approaches that allow the vehicle to track desired waypoints (line of sight (LOS)), and follow a given path (cross-track error) and a predefined trajectory with obstacle avoidance. A comparative study was also proposed in this paper, wherein we evaluate the paths followed by the vehicle using distinct yaw moment control techniques which are;differential thrust controller, solely relying on a steering controller, and a combination of both. To validate the effectiveness of the proposed autopilot system, we have conducted experimental tests, specifically focusing on waypoint tracking control (LOS method). The results underscore the system's capabilities and its potential in real-world applications.

关键词： Autonomous wheeled mobile robot modeling power allocation Lyapunov theory heading and speed controllers control parameter tuning yaw moment control techniques waypoint tracking control path following control

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Trajectory Tracking Active Disturbance Rejection control of the Unmanned Helicopter and Its parameters tuning

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IEEE ACCESS 2021年 9卷 56773-56785页

作者： Shen, Suiyuan Xu, Jinfa Nanjing Univ Aeronaut & Astronaut Natl Key Lab Rotorcraft Aeromech Nanjing 210016 Peoples R China

To solve the problem that the trajectory tracking control system of the unmanned helicopter is limited by the accuracy of the physical model (parameters and dynamic characteristics) and external disturbance of the unmanned helicopter, this paper designs a trajectory tracking control system of the unmanned helicopter based on the linear/nonlinear hybrid Active Disturbance Rejection control (ADRC). At the same time, this paper proposes a method for tuning the controller parameters based on the Bacterial Foraging Optimization-Flower Pollination Algorithm (BFO-FPA). Finally, the simulation test of spiral climb and "8"-figure climb is applied to verify the controller's performance. The results show that the controller proposed in this paper can effectively overcome the influence of the unmanned helicopter's internal and external disturbance, and the controller has the advantages of strong anti- disturbance ability and strong robustness. Simultaneously, the optimization algorithm can obtain the optimal global solution, and can improve the controller's performance.

关键词： Helicopters control systems Trajectory tracking Uncertainty Observers Robust control Mathematical model Unmanned helicopter linear nonlinear hybrid control active disturbance rejection control control parameter tuning

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Comprehensive parameter Optimization Using an Empowered and Lightweight Surrogate Model

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IEEE TRANSACTIONS ON POWER ELECTRONICS 2024年第10期39卷 12124-12129页

作者： Yang, Qifan Huang, Dihong Chen, Yong Dai, Ningyi Univ Macau State Key Lab Internet Things Smart City Macau 999078 Peoples R China Univ Macau Dept Elect & Comp Engn Macau 999078 Peoples R China Guangdong Power Grid Corp Zhuhai Power Supply Bur DC Power Distribut & Consumpt Technol Guangzhou 200235 Peoples R China Univ Macau Zhuhai UM Sci & Technol Res Inst Macau 999078 Peoples R China

Fine tuning the parameters is crucial for achieving high-performance power electronics converters. Traditionally, iterative testing using professional simulation tools has been a common approach. However, running the simulation model is time consuming, and online parameter optimization generates parameters specific to each operating condition. In this article, we propose a novel approach that combines artificial intelligence (AI)-aided parameter tuning with simulation using a data-driven empowered surrogate model. The surrogate model is trained using a dataset derived from 3000 simulation tests, enabling rapid parameter tuning with feedback on system performance within a time frame of less than 0.1 ms, even on devices with restricted computational capabilities. Moreover, comprehensive parameter optimization for multiscenarios can be achieved using the surrogate model. A case study focusing on the parameter tuning of the soft-open-point is provided, including a comparison with AI-aided autonomous online parameter tuning methods. The results demonstrate the effectiveness and efficiency of the proposed approach.

关键词： Optimization tuning Phase locked loops Power electronics Genetic algorithms Real-time systems Computational modeling control parameter tuning simulation soft open point (SOP) surrogate model

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Digital signal processor based intelligent fractional-order sliding-mode control for a linear voice coil actuator

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IET control THEORY AND APPLICATIONS 2017年第8期11卷 1282-1292页

作者： Chen, Syuan-Yi Lee, Cheng-Yan Natl Taiwan Normal Univ Dept Elect Engn Taipei 106 Taiwan

In this study, a digital signal processor (DSP) based intelligent fractional-order sliding-mode control (IFOSMC) is proposed to control a linear voice coil actuator (VCA) for the tracking of reference trajectory. First, a dynamic model of the VCA is analysed considering the system uncertainties. Subsequently, a sliding-mode control (SMC) and a fractional-order SMC (FOSMC) are developed for the VCA control system. With increased degrees of freedom for the control parameters, the FOSMC can improve the control performance of the SMC. However, because the uncertainties of the VCA are unclear, designing hitting control laws for the SMC and FOSMC is difficult. Thus, the IFOSMC is proposed for improving the adaptability and robustness of the control system. For the IFOSMC, a compensatory fuzzy neural network observer is designed to replace the hitting control directly while a switching compensator is developed to compensate for the observation error smoothly. A Lyapunov method is used to derive the adaptive laws for tuning the control parameters of the IFOSMC online. Experimentations regarding nominal and parameter variation cases were undertaken through the DSP. Experimental results show that the proposed IFOSMC significantly improves the control performances of the SMC and FOSMC with regard to the VCA control system.

关键词： variable structure systems actuators digital signal processing chips trajectory control uncertain systems control system synthesis robust control fuzzy control fuzzy neural nets observers Lyapunov methods adaptive control neurocontrollers error compensation digital signal processor based intelligent fractional-order sliding-mode control linear voice coil actuator DSP-based intelligent fractional-order sliding-mode control reference trajectory tracking dynamic model system uncertainties control performance improvement IFOSMC control system robustness improvement control system adaptability improvement compensatory fuzzy neural network observer design switching compensator observation error compensation Lyapunov method control parameter tuning nominal variation parameter variation VCA control system

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Non-linear fuzzy predictive control of hydroelectric system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第8期11卷 1966-1975页

作者： Zhang, Runfan Chen, Diyi Yao, Wei Ba, Duoduo Ma, Xiaoyi Northwest A&F Univ Inst Water Resources & Hydropower Res Yangling 712100 Shaanxi Peoples R China Curtin Univ Dept Math & Stat Fac Sci & Engn Perth WA 6845 Australia

This study brings out a brand new non-linear predictive control method, a fuzzy generalised predictive control, for hydroelectric plant systems. Takagi-Sugeno (T-S) fuzzy method and generalised predictive control are implemented in the non-linear control method. The basic concept of the method is generalised predictive control, which is a linear system based method. However, the authors novelty adopt fuzzy model, especially the T-S model, as the prediction model to broaden the applications of the predictive control to non-linear range. Therefore, the proposed method inherits the advantages of generalised predictive control, but it can also effectively control a complex non-linear system. Its effectiveness and efficiency are verified by Lyapunov stability theory. Applying the control method on the non-linear hydroelectric system, the different experiments show that this control method succeeds in maintaining the hydroelectric system stable. Those experimental results accord with theoretic analysis. Thus, the proposed control method enjoys the capacity to govern a non-linear system. A comparison experiment between the proposed method and PID method illustrates the advantages of the authors' method, which are its smooth treatment, less overshoot and fast to be stable of response. In addition, discussions about the tuning control parameters facilitate the usages of the proposed method.

关键词： nonlinear control systems fuzzy control predictive control hydroelectric power stations linear systems Lyapunov methods power generation control three-term control hydroelectric system nonlinear fuzzy predictive control fuzzy generalised predictive control hydroelectric plant system Takagi-Sugeno fuzzy method T-S fuzzy method linear system based method complex nonlinear system Lyapunov stability theory PID method control parameter tuning

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tuning Optimization Algorithms Under Multiple Objective Function Evaluation Budgets

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2015年第3期19卷 341-358页

作者： Dymond, Antoine S. Engelbrecht, Andries P. Kok, Schalk Heyns, P. Stephan Univ Pretoria Dept Mech & Aeronaut Engn ZA-0043 Pretoria South Africa Univ Pretoria Dept Comp Sci ZA-0043 Pretoria South Africa

Most sensitivity analysis studies of optimization algorithm control parameters are restricted to a single objective function evaluation (OFE) budget. This restriction is problematic because the optimality of control parameter values (CPVs) is dependent not only on the problem's fitness landscape, but also on the OFE budget available to explore that landscape. Therefore, the OFE budget needs to be taken into consideration when performing control parameter tuning. This paper presents a new algorithm tuning multiobjective particle swarm optimization (tMOPSO) for tuning the CPVs of stochastic optimization algorithms under a range of OFE budget constraints. Specifically, for a given problem tMOPSO aims to determine multiple groups of CPVs, each of which results in optimal performance at a different OFE budget. To achieve this, the control parameter tuning problem is formulated as a multiobjective optimization problem. Additionally, tMOPSO uses a noise-handling strategy and CPV assessment procedure, which are specialized for tuning stochastic optimization algorithms. Conducted numerical experiments provide evidence that tMOPSO is effective at tuning under multiple OFE budget constraints.

关键词： control parameter tuning multiobjective optimization objective function evaluation (OFE) budget

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