2022 Australian & New Zealand Control Conference (ANZCC)最新文献_第3页

Comparison of Constant PID Controller and Adaptive PID Controller via Reinforcement Learning for a Rehabilitation Robot 基于强化学习的康复机器人恒PID控制器与自适应PID控制器的比较

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966949

Bradley R.G. Beck, J. Tipper, S. Su

{"title":"Comparison of Constant PID Controller and Adaptive PID Controller via Reinforcement Learning for a Rehabilitation Robot","authors":"Bradley R.G. Beck, J. Tipper, S. Su","doi":"10.1109/ANZCC56036.2022.9966949","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966949","url":null,"abstract":"Effectively tuning a PID controller can be difficult without prior experience or knowledge of the system being controlled. Reinforcement learning is a tool that allows automatic PID tuning with adaptability to environmental change. This technique was utilised for a single degree-of-freedom robot designed for human interaction, proving the validity of the TD3PG algorithm for reference tracking and rehabilitation exercises. These results were measured by the root mean square error of the system and compared to a classical PID controller to determine whether the adaptability improved the system tracking ability. Results showed the classical PID controller resulted in smaller RMSE measurements for a multitude of input signals including sine waves and multi-step functions when the environment remained constant. The adaptive PID controller resulted in smaller RMSE measurements for all input signals when the environment changed to reduce the amount of torque applied to the plant, representing a motor power failure. It is believed that a classic PID controller is better suited for systems with low input frequency and low system uncertainty while adaptive PID controllers are better for systems with changing environments or input signals.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121406280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Control of Adaptive Model Predictive Control using Online Model Estimation 基于在线模型估计的自适应模型预测控制鲁棒控制

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966969

M. R. Mariya, V. ShashankS., M. Mamta

引用次数: 0

A Time-domain Approach to the 3-omega Heat Conductivity Measurement Method 3-omega热导率测量方法的时域方法

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966983

J. Bendtsen, J. Leth, C. Kallesøe

{"title":"A Time-domain Approach to the 3-omega Heat Conductivity Measurement Method","authors":"J. Bendtsen, J. Leth, C. Kallesøe","doi":"10.1109/ANZCC56036.2022.9966983","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966983","url":null,"abstract":"The so-called \"3ω method\" is a well established method to measure heat conductivity of solids. It is a frequency-based method, in which the ratio between the first and third harmonics of an induced voltage in an electric heater element can be shown to be (inversely) proportional to the thermal conductivity of a solid that the heater is in direct contact with. Commonly, the method utilizes Discrete Fourier analysis in an off-line setting, which is of course perfectly valid when measuring material properties in a static setting.In this paper we propose to make use of the measurement principle in a dynamic setting. We propose a novel timedomain approach to 3ω measurement, which can easily be implemented in a cheap micro-controller due to its modest memory and sampling rate requirements, and therefore likely to be useful for feedback in control loops or similar applications.The approach comprises two main elements, a discrete-time signal generator, which provides a steady-state sinusoidal current output, and a standard Luenberger-style state observer designed to estimate the associated third harmonic in the presence of noisy voltage measurements. We prove that the signal generator is robust to numerical inaccuracies. The approach is tested in simulation and on actual laboratory data, showing good agreement with traditional off-line analysis.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124310360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Discrete Fruit Fly Optimization Algorithm for Disassembly Line Balancing Problems by Considering Human Worker’s Learning Effect 考虑工人学习效应的离散果蝇拆解平衡问题优化算法

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966961

Jianping Wang, Xiwang Guo, Mengchu Zhou, Jiacun Wang, Shujin Qin, Liang Qi

引用次数: 1

Remarks on a Commutative Quaternion Neural Network–based Controller and Its Application in Controlling a Robot Manipulator 基于交换四元数神经网络的控制器及其在机器人机械臂控制中的应用

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966947

Kazuhiko Takahashi, Daiki Kawamoto, Tomoaki Naba, Hirotaka Okamoto, Tomoki Onodera, M. Hashimoto

{"title":"Remarks on a Commutative Quaternion Neural Network–based Controller and Its Application in Controlling a Robot Manipulator","authors":"Kazuhiko Takahashi, Daiki Kawamoto, Tomoaki Naba, Hirotaka Okamoto, Tomoki Onodera, M. Hashimoto","doi":"10.1109/ANZCC56036.2022.9966947","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966947","url":null,"abstract":"This study examines the possibility of using a commutative quaternion neural network in control system applications. A multi-layer commutative quaternion neural network and its training algorithm are derived and the network is applied to develop a feedforward-feedback controller, with the network input consisting of a reference output and some tapped-delay input-output sets of the controlled plant while the network output is employed to synthesise the control input. Training of the commutative quaternion neural network in the control system is conducted in real-time by integrating feedback error learning. To evaluate the effectiveness of a commutative quaternion neural network-based controller, computational experiments on trajectory tracking control of a three-link robot manipulator are conducted. Simulation results show the suitability of the commutative quaternion neural network for controlling the robot manipulator and the characteristics of the commutative quaternion neural network-based controller are clarified when compared with those of a quaternion neural network-based controller.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122649169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

End Effector Position Control of Pantograph Type Robot Using Sliding Mode Controller 基于滑模控制器的受电弓机器人末端执行器位置控制

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966971

Muhammad Khairul Ali Hassan, Z. Cao, Z. Man

引用次数: 0

Test Case Study of High-Speed Railway Train Control System for Typical Operation Scenarios* 高速铁路列控系统典型运行场景测试案例研究*

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966951

F. Zhu, Shixiang Li, Fei Li, Yuan Cheng

引用次数: 0

H2 Controller Design for Multi-Agent Systems with Markovian Switching Topologies 具有马尔可夫交换拓扑的多智能体系统H2控制器设计

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966964

Robert C. Ballam, A. Mcfadyen, D. Quevedo

引用次数: 0

Siamese Multi-Scale Aggregation Network for UAV Tracking* 用于无人机跟踪的Siamese多尺度聚合网络*

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966962

Meiyu Yao, Na Wu, Shuo Hu, Hui Yu

引用次数: 0

Interpretable Learning for Travel Behaviours in Cyber-Physical-Social-Systems 网络-物理-社会系统中旅行行为的可解释学习

2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966975

Hao Qi, Peijun Ye

引用次数: 0