检索结果-内蒙古大学图书馆

AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Arabi, Ehsan Panagou, Dimitra Yucelen, Tansel Nguyen, Nhan T. Univ Michigan Aerosp Engn Dept Ann Arbor MI 48109 USA Univ Michigan Distributed Aerosp Syst & Control Lab Ann Arbor MI 48109 USA Univ S Florida Mech Engn Dept Tampa FL 33620 USA Univ S Florida Lab Auton Control Informat & Syst Tampa FL 33620 USA NASA Adv Control & Evolvable Syst Grp Washington DC USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

The bandwidth of the actuator dynamics plays a crucial role in the closed-loop system stability as well as in achieving a desired level of system performance. Altering the ideal reference system, known as the pseudo-control hedging method, can partially overcome this limitation by separating the adaptation process from the actuator dynamics. However, the calculated system error between the uncertain dynamical system trajectories and the ideal reference model trajectories can be conservative and depend on the bounds on the system uncertainties. In this paper, we consider uncertain dynamical systems with high-order actuator dynamics that captures a wide range of applications. To address the aforementioned problem, we utilize a set-theoretic model reference adaptive control architecture to impose a user-defined performance bound on the system error vector. This user-defined bound can then characterize the system error between the uncertain dynamical system trajectories and the ideal reference model trajectories. To validate the efficacy of the results, we illustrate an application of the proposed control scheme on a generic transport model developed by NASA for both longitudinal and lateral-directional dynamics.

关键词： Actuators model reference adaptive control Angle of Attack Sideslip Angle Pitching Moment Feedback control Damping Ratio Elevator Input to State Stability Convex Optimization

来源：评论

学校读者我要写书评

暂无评论

model reference adaptive control of Wheel Mobile Robot 4

Model Reference Adaptive Control of Wheel Mobile Robot

引用

Michael Faraday IET International Summit 2020, MFIIS 2020

作者： Pal, Mita Sarkar, Gautam Barai, Ranjit Kumar Roy, Tamal Electrical Engineering Department Jadavpur University Kolkata India Electrical Engineering Department MCKV Institute of Engineering Howrah India

ISBN: (纸本)9781839535635

This paper presents the design of model reference adaptive control (MRAC) as tracking control system for unstable two input two output (TITO) Wheel Mobile Robot(WMR) system. MRAC technique is very much useful to track the desired trajectory for different SISO system. The novelty of this proposed controller is that it has been taken one TITO cross coupled WMR system, whose output responses are very much *** uniqueness of this suggested control scheme is that its output responses can exactly track the reference input trajectory by comparing with the output response of the reference model which is an important part of MRAC structure and there is a scope for control designer to choose the appropriate reference model required for the WMR system. adaptive control parameters needed for the control input in MRAC structure have been derived by Lyapunov theory and finally the dynamic characteristics of MRAC controlled TITO WMR have been compared with the State feedback and PID compensation to prove its efficiency as tracking control system in MATLAB simulink environment. © 2020 Institution of Engineering and Technology. All rights reserved.

关键词： model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

Dynamic Performance Improvement Using model reference adaptive control of Photovoltaic Systems under Fast-Changing Atmospheric Conditions

引用

INTERNATIONAL JOURNAL OF PHOTOENERGY 2023年第12023期2023卷 5703727-5703727页

作者： Mindzie, Yves Abessolo Kenfack, Joseph Joseph, Voufo Nzotcha, Urbain Djanssou, Dieudonne Marcel Mbounguen, Raphael Univ Yaounde I Natl Adv Sch Engn Yaounde Lab Energy Water & Environm Yaounde Cameroon Univ Maroua Natl Adv Sch Engn Maroua Dept Renewable Energy Maroua Cameroon

The effectiveness of a photovoltaic (PV) system can be increased by using maximum power point tracking (MPPT). The literature has suggested a number of methods for tracking the maximum power point (MPP). However, this number of methods most often presents a high convergence speed in reaching the MPP, complexity under their implementation, power fluctuations, overshoots, and great difficulty in reaching the MPP under fast-changing atmospheric conditions, thus influencing the efficiency of PV systems. Intending to improve the performance of PV systems under rapid changes in the atmosphere, this paper proposes model reference adaptive control (MRAC) as a technique for tracking the MPP based on the employ of reference models such as optimal voltage and current at the MPP (V-MPP and I-MPP). The MATLAB/Simulink environment is used to produce the simulation results;the Kyocera Solar KC 130 GT module is used here as a photovoltaic power plant, connected to a boost converter, supplying a resistive load. The Lyapunov theory was used to demonstrate the stability of the system. The simulation outcomes obtained using the suggested method are compared with those obtained by techniques such as perturb and observe (P & O), incremental conductance (INC), variable step incremental conductance (VSINC), particle swarm optimization (PSO), and grey wolf optimization (GWO), thus showing a very large improvement under standard test and fast-changing atmospheric conditions of the technique proposed on the other techniques in terms of convergence speed and tracking efficiency. The simulation results prove that the suggested method has great tracking effectiveness (>99.88%), less time for convergence (<0.01 s), and simple implementation complexity under fast-changing atmospheric conditions without both transient and steady-state power oscillations, overshoots, and chattering effects, thus causing a great minimization of energy losses, and the proposed technique reaches exactly the MPP under f

关键词： model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

Bridging model reference adaptive control and Data Informativity

arXiv

引用

arXiv 2025年

作者： Wang, Jiwei Baldi, Simone van Waarde, Henk J. School of Cyber Science and Engineering Southeast University China Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Netherlands School of Mathematics Southeast University China

The goal of model reference adaptive control (MRAC) is to ensure that the trajectories of an unknown dynamical system track those of a given reference model. This is done by means of a feedback controller that adaptively changes its gains using data collected online from the closed-loop system. One of the approaches to solve the MRAC problem is to impose conditions on the data that guarantee convergence of the gains to a solution of the so-called matching equations. In the literature, various extensions of the concept of persistent excitation have been proposed in an effort to weaken the conditions on the data required for this convergence. Despite these efforts, it is not well-understood what are the weakest possible data requirements ensuring convergence of MRAC. In this paper, we propose a new framework to study the MRAC problem, using the concept of data informativity. Our main contribution is to provide necessary and sufficient conditions for the convergence of the adaptive gains to a solution of the matching equations. These necessary and sufficient conditions can be readily checked online as new data are generated by the closed-loop system. Our results reveal that existing excitation conditions impose stronger requirements on the collected data than required. Notably, the necessary and sufficient conditions provided in this paper are weaker than those for unique system identification. Copyright © 2025, The Authors. All rights reserved.

关键词： model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

Efficient Uncertainty Mitigation in Automated Vehicles: Combining MPC with model reference adaptive control

引用

IFAC-PapersOnLine 2023年第3期56卷 259-264页

作者： Mugdha Basu Thakur Matthias Schmid Department of Automotive Engineering Clemson University Greenville SC 29607 USA

Automated vehicles must be equipped with the ability to plan and execute trajectories in the presence of uncertainties for safe and effective navigation in both on-and of-road environments. model predictive control (MPC) is a powerful and popular optimal control solution for both planning and control tasks, but even robust MPC solutions still suffer from decreased performance and the curse of dimensionality in the optimization problem when subject to model errors. We propose an adaptive control scheme that combines a fast, nominal MPC with our previously developed model reference adaptive control strategy in a cascaded architecture. The effectiveness of the resulting framework is demonstrated via robust and computationally efficient trajectory tracking under severe uncertainties. This study includes detailed tuning considerations and numerical evaluation with different fidelity vehicle models.

关键词： Automated vehicle stability adaptive control of automotive systems model reference adaptive control model predictive control

来源：评论

学校读者我要写书评

暂无评论

model reference adaptive control with Unknown Gain Sign

Model Reference Adaptive Control with Unknown Gain Sign

引用

2019 Chinese Intelligent Systems Conference

作者： Liu, Heqing Zhang, Tianping Wu, Ziwen Hua, Yu Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China

ISBN: (纸本)9789813296848;9789813296824;9789813296817

In this paper, a model reference adaptive control scheme is proposed by using Nussbaum function for first order linear system with unknown gain sign. Only one self-tuning parameter is designed. Based on Lyapunov method, the Nussbaum parameter is determined. Using Nussbaum function, unknown gain sign is handled. According to Babalat's lemma, the tracking error is proved to converge to zero. A numerical example is provided to demonstrate the effectiveness of the proposed method.

关键词： model reference adaptive control Unknown gain sign Nussbaum function

来源：评论

学校读者我要写书评

暂无评论

An Application of model reference adaptive control for Multi-Agent Synchronization in Drone Networks

arXiv

引用

arXiv 2024年

作者： Arevalo-Castiblanco, Miguel F. Wi, Yejin Cescon, Marzia Uribe, César A. Rice University HoustonTX77005 United States the University of Houston HoustonTX77204 United States the Departamento de Ingeniería Eléctrica y Electrónica Universidad Nacional de Colombia Bogotá111321 Colombia

This paper presents the application of a Distributed model reference adaptive control (DMRAC) strategy for robust multi-agent synchronization of a network of drones. The proposed approach enables the development of controllers capable of accommodating differences in real-life model parameters between agents, thereby enhancing overall network performance. We compare the performance of the adaptive control laws with classical PID controllers for the reference tracking task. Each follower drone has a model reference adaptive controller that continuously updates its parameters based on real-time feedback and reference model information. This adaptability ensures an adequate performance that, compared to conventional non-adaptive techniques, can reduce the amount of energy required and consequently increase the operating duration of the drones. The experimental results, particularly in vertical velocity control, underscore the effectiveness of the proposed approach in achieving synchronized behavior. © 2024, CC BY.

关键词： model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

Direct model reference adaptive control of Periodic Slow Time-Varying Systems with Disturbance Input

Direct Model Reference Adaptive Control of Periodic Slow Tim...

引用

第43届中国控制会议

作者： Zhuolin Tan Xiaochen Xie Jiantai Huang Department of Automation Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper investigates the direct model reference adaptive control(MRAC) of periodic systems with slow timevarying dynamics and disturbance input. It is necessary to approximate the periodic slow time-varying system as a periodic piecewise linear system for MRAC. Based on the stability criteria of the periodic piecewise linear reference system, a new sufficient condition in terms of linear matrix inequalities(LMIs) is established to obtain the Lyapunov matrices and design the adaptive update law for the state feedback controller gains. Different from previous studies, where asymptotic tracking was possible only in the presence of a common Lyapunov function for the reference models, the asymptotic tracking in this work is achieved through a periodic time-varying Lyapunov matrix function. A numerical example validates the proposed MRAC scheme, and the effectiveness of the modified approach is discussed.

关键词： model reference adaptive control Periodic Slow Time-Varying System Periodic Piecewise Linear System

来源：评论

学校读者我要写书评

暂无评论

Approximation with Random Shallow ReLU Networks with Applications to model reference adaptive control

arXiv

引用

arXiv 2024年

作者： Lamperski, Andrew Lekang, Tyler department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States Honeywell Aerospace PlymouthMN55441 United States

Neural networks are regularly employed in adaptive control of nonlinear systems and related methods of reinforcement learning. A common architecture uses a neural network with a single hidden layer (i.e. a shallow network), in which the weights and biases are fixed in advance and only the output layer is trained. While classical results show that there exist neural networks of this type that can approximate arbitrary continuous functions over bounded regions, they are non-constructive, and the networks used in practice have no approximation guarantees. Thus, the approximation properties required for control with neural networks are assumed, rather than proved. In this paper, we aim to fill this gap by showing that for sufficiently smooth functions, ReLU networks with randomly generated weights and biases achieve L8 error of Opm´1{2q with high probability, where m is the number of neurons. It suffices to generate the weights uniformly over a sphere and the biases uniformly over an interval. We show how the result can be used to get approximations of required accuracy in a model reference adaptive control application. Copyright © 2024, The Authors. All rights reserved.

关键词： model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

A Sliding Mode-Based model reference adaptive control Algorithm for Target Tracking Using Recursive Least Squares

引用

TRANSACTIONS OF THE KOREAN SOCIETY OF MECHANICAL ENGINEERS A 2023年第8期47卷 657-666页

作者： La, Han Byeol Lee, Ji Ung Oh, Kwang Seok Hankyong Natl Univ Sch ICT Robot & Mech Engn Anseong South Korea

This paper proposes a sliding mode-based model reference adaptive control algorithm for target tracking using recursive least squares with forgetting. The reference model for target was designed based on the first order differential equation, and the model reference adaptive control algorithm was designed using a sliding mode approach. Using the virtual first order differential equation, the Lyapunov direct method based sliding mode control input and adaptation rule were derived. The system parameter of the virtual function was estimated using recursive least squares with forgetting factor. The adaptive control algorithm proposed in this study was designed on Matlab/Simulink environment, and performance was evaluated based on the simulation technique and actual test platform. The performance evaluation results showed that the sliding mode-based model reference adaptive control algorithm proposed in this study could enable the system to track the target reasonably without using any system information.

关键词： Sliding model model reference adaptive control Target Tracking( Recursive Least Squares Forgetting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：