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Attitude Control of UAV with Manipulator Using Adaptive PID 基于自适应PID的无人机机械手姿态控制
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966861
Kazuya Uchida, K. Uchiyama, Kai Masuda
{"title":"Attitude Control of UAV with Manipulator Using Adaptive PID","authors":"Kazuya Uchida, K. Uchiyama, Kai Masuda","doi":"10.1109/ANZCC56036.2022.9966861","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966861","url":null,"abstract":"This paper describes the attitude control of Unmanned Aerial Vehicles (UAVs) equipped with a manipulator referred to as Unmanned Aerial Manipulators (UAMs). UAVs equipped with manipulators are expected to work on behalf of humans and to observe and investigate, but the effect of the motion reaction on control performance cannot be ignored when using the manipulator in a mission. Therefore, the control system must be designed to operate UAM so that these effects can be reduced or tolerated. The adaptive PID controller, which can deal with the parameter variations, is applied to the UAM in this study. We confirm the effectiveness of the controller through numerical simulation.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"31 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":"114321070","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
Unified Approach for Weighted Sensitivity Design of PID Controllers with Smith Predictors 带Smith预测器的PID控制器加权灵敏度统一设计方法
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966952
Abdulkarim Alrishan, J. Watkins, T. Emami
{"title":"Unified Approach for Weighted Sensitivity Design of PID Controllers with Smith Predictors","authors":"Abdulkarim Alrishan, J. Watkins, T. Emami","doi":"10.1109/ANZCC56036.2022.9966952","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966952","url":null,"abstract":"This paper combines a Smith Predictor with an approach for graphically determining all Proportional-Integral-Derivative (PID) controllers in either continuous-time (CT) or discrete-time (DT) domains that meet performance specifications expressed in a form of a weight on the sensitivity transfer function using Hardy-Space (H∞) control. A Smith Predictor (SP) is often used when designing a controller for a system that exhibits a \"relatively\" large delay that may cause the system’s relative stability and/or performance to deteriorate. The PID controller gains, namely Proportional gain Kp, Integral gain Ki and Derivative gain Kd, will be determined graphically using only the frequency response of the systems’ components, i.e., plant with delay and SP structure. The inclusion of a SP along with a PID controller can significantly improve stability margins and/or performance when compared to relying solely on a PID controller. The improvement can be observed even if there is a mismatch between the actual process and its corresponding SP model. By using the delta operator, the same procedure can be applied to either continuous or discrete time systems, hence a unified approach. The stability boundaries of the PID controller will be determent graphically where within the boundaries, nominal stability is guaranteed and weighted sensitivity requirements are met.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"1 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":"130190346","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
Physics Informed Intrinsic Rewards in Reinforcement Learning 强化学习中的物理内在奖励
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966956
Jiazhou Jiang, M. Fu, Zhiyong Chen
{"title":"Physics Informed Intrinsic Rewards in Reinforcement Learning","authors":"Jiazhou Jiang, M. Fu, Zhiyong Chen","doi":"10.1109/ANZCC56036.2022.9966956","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966956","url":null,"abstract":"Model-free algorithms in Reinforcement Learning (RL) are known to be a powerful learning tool and have performed well in solving complex issues. However, RL training results are often poor when the reward function is sparse or misleading in short term. In this paper, we propose a physics informed intrinsic reward function to assist the agent to overcome this difficulty. We evaluate the proposed intrinsic reward method on different types of actor-critic (AC) algorithms. The experimental results show noticeable improvement.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"1 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":"128902418","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: 2
Predefined-Time Leader-Following Consensus for Multi-Agent Systems With Collision Avoidance 具有避碰的多智能体系统的预定义时间领导-跟随一致性
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966976
Boda Ning, Qing‐Long Han, Derui Ding
{"title":"Predefined-Time Leader-Following Consensus for Multi-Agent Systems With Collision Avoidance","authors":"Boda Ning, Qing‐Long Han, Derui Ding","doi":"10.1109/ANZCC56036.2022.9966976","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966976","url":null,"abstract":"In this paper, predefined-time leader-following (LF) consensus is investigated for multi-agent systems (MASs) with collision avoidance. A monotone system-based controller is proposed to maintain the order of a MAS. Particularly, two sufficient conditions are derived to guarantee collision-free coordination of the MAS, while realizing LF consensus in predefined-time. Numerical examples including comparison studies are provided to verify the effectiveness of the proposed controller.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"25 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":"117076613","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: 1
Data-driven robust optimization with multiple kernel learning for refinery planning under price uncertainty 价格不确定条件下炼油厂规划的多核学习数据驱动鲁棒优化
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966966
Yuhao Liu, Wangli He, Liang Zhao
{"title":"Data-driven robust optimization with multiple kernel learning for refinery planning under price uncertainty","authors":"Yuhao Liu, Wangli He, Liang Zhao","doi":"10.1109/ANZCC56036.2022.9966966","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966966","url":null,"abstract":"Refinery planning is crucial for increased profitability for refineries. However, the markets associated with refinery operations are volatile, resulting in fluctuations in the product price, which can heavily affect the total profit of refineries. This paper is intended to develop a data-driven robust optimization (DDRO) framework for refinery planning under price uncertainty. Firstly, historical data of the product prices is collected and a multiple kernel learning (MKL) algorithm is proposed to construct the uncertainty set to capture the price uncertainty. Then, based on the derived uncertainty set, a DDRO model of refinery planning is developed and a tractable robust counterpart is reformulated by using the dual transformation, which is directly solved by using the solver. Finally, an industrial case of refinery planning is researched to illustrate the applicability of the proposed approach, which demonstrates that the proposed approach has a better balance between the total profit and robustness for refinery planning than the deterministic method.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"132 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":"116936664","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
Optimal control of plant growth in a plant factory using a plant model * 植物模型在植物工厂植物生长的最优控制*
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966978
X. J. Wang, M. Kang, U. Lewlomphaisarl, Jing Hua, H. Y. Wang
{"title":"Optimal control of plant growth in a plant factory using a plant model *","authors":"X. J. Wang, M. Kang, U. Lewlomphaisarl, Jing Hua, H. Y. Wang","doi":"10.1109/ANZCC56036.2022.9966978","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966978","url":null,"abstract":"A plant factory (PF) is an environmentally controlled facility that can maintain stable crop cultivation while ensuring rapid production and good crop quality by controlling temperature, humidity, light and other growing factors. Although it has many advantages, PFs have high initial and operating costs, especially the energy for lightening. In this work, we aim to improve the light use efficiency by optimizing the combination of environmental conditions (case of light intensity and CO2 level) in order to fit the best plant’s demand. To reach this goal, the effect of environmental conditions on daily assimilation rate is simulated using a process-based plant model ‘TomSim’. The daily profit of a PF under different light intensities and CO2 concentrations is evaluated. As a result, the optimal light and CO2 conditions can be obtained for a given leaf area index (LAI) of the lettuce plant. Smaller LAI, which means the early growth stage, requires lower light intensity. A dynamic light control strategy for a PF is expected that can fit plant demand during its growth period while keeping relatively low cost.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"42 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":"129209407","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
Model Predictive Valve Control of Lung Pressure Profile Tracking 肺压力曲线跟踪的模型预测阀控
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966865
M. C. Thompson, C. Freeman, N. O'Brien, A. Hughes, T. Birch, R. Marchbanks
{"title":"Model Predictive Valve Control of Lung Pressure Profile Tracking","authors":"M. C. Thompson, C. Freeman, N. O'Brien, A. Hughes, T. Birch, R. Marchbanks","doi":"10.1109/ANZCC56036.2022.9966865","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966865","url":null,"abstract":"Measuring changes in intracranial pressure (ICP) is critical for diagnosing many cerebral pathologies. However noninvasive methods require airway pressure to be precisely controlled. In clinical practice, this is currently performed by the subject breathing into a tube, attempting to follow a target pressure profile. They are assisted by an operator manually releasing airway pressure via a cap, however tracking is poor. This paper develops the first automatic solution, taking the form of model predictive control (MPC) of a variable release valve to assist the subject in tracking the target trajectory. This differs from conventional MPC since the controlled variable is a system parameter rather than an input signal. A novel identification approach for the combined lung model, muscle dynamics and voluntary respiration time-varying system is also proposed. Numerical results validate the approach and show a 44% reduction in tracking error compared with manual assistance.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"24 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":"127866310","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
Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic 2019冠状病毒病疫情下2020年国庆人员流动模式分析
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966981
Yuan-yuan Chen, Yisheng Lv
{"title":"Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic","authors":"Yuan-yuan Chen, Yisheng Lv","doi":"10.1109/ANZCC56036.2022.9966981","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966981","url":null,"abstract":"With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation Systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., \"Social Transportation\", is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"40 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":"127615533","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
Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Problems 多目标拆解线平衡问题的近端策略优化算法
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966864
ZhaoKai Zhong, Xiwang Guo, Mengchu Zhou, Jiacun Wang, Shujin Qin, Liang Qi
{"title":"Proximal Policy Optimization Algorithm for Multi-objective Disassembly Line Balancing Problems","authors":"ZhaoKai Zhong, Xiwang Guo, Mengchu Zhou, Jiacun Wang, Shujin Qin, Liang Qi","doi":"10.1109/ANZCC56036.2022.9966864","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966864","url":null,"abstract":"As more and more end-of-life products are accumulated over time, there is an urgent need for their recycling. Disassembly is a key step to do so. In order to improve the operational efficiency of disassembly lines, a disassembly line balance problem (DLBP) has drawn many researchers’ attention. There are multiple factors that affect disassembly quality and efficiency, e.g., workstation allocation and disassembly revenue. This work addresses a multi-objective DLBP. We consider three objectives: maximizing the net profit of disassembly, minimizing the maximal gap of working time among workstations, and minimizing the risk of performing dangerous disassembly tasks. An improved proximal policy optimization is proposed for the multi-objective DLBP. Five real-world products are used to test its effectiveness and feasibility. Experimental results verify the strength of the algorithm by comparing it with an Actor-Critic algorithm.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"65 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":"114226238","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
Rendezvous Control of UAVs with Disturbance Compensation 基于扰动补偿的无人机交会控制
2022 Australian & New Zealand Control Conference (ANZCC) Pub Date : 2022-11-24 DOI: 10.1109/ANZCC56036.2022.9966958
S. Ura, K. Uchiyama, Kai Masuda
{"title":"Rendezvous Control of UAVs with Disturbance Compensation","authors":"S. Ura, K. Uchiyama, Kai Masuda","doi":"10.1109/ANZCC56036.2022.9966958","DOIUrl":"https://doi.org/10.1109/ANZCC56036.2022.9966958","url":null,"abstract":"This paper describes the method of rendezvous control of UAVs under disturbance conditions. The quadrotor, which is used globally, has restrictions on the cruise distance, the cruise speed, and the flight time due to its structure and battery life. To overcome these problems, the coordination between the quadrotor and the fixed-wing UAV that is superior to the cruise speed and the long-range flight has been proposed by some researchers. If the quadrotor realizes the rendezvous docking to the fixed-wing UAV, the use range of the quadrotor expands extremely. However, these controllers are insufficient to have robustness against disturbances, although wind disturbance greatly influences these UAVs. Thus, this paper proposes the control method with disturbance compensation for the rendezvous problem for those UAVs. The artificial potential field (APF) is applied to the guidance of UAVs for the rendezvous. The effectiveness of the proposed method is confirmed numerically under the flight condition with the existence of the disturbance.","PeriodicalId":190548,"journal":{"name":"2022 Australian & New Zealand Control Conference (ANZCC)","volume":"150 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":"117092080","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
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