Biomass energy plays an essential role in renewable energy for many reasons, such as reducing the dependence on fossil fuels and lowering greenhouse gas emissions, providing heat, electricity, and biofuels for various applications, and utilizing waste materials for helpful energy products. Besides, it can create employment opportunities and promote rural development, especially in developing countries where biomass resources are abundant and accessible. In the context of renewable energy research and application, this paper aims to develop a multi-objective mixed integer linear programming for designing multiple echelon biomass supply chain networks. The model is formulated to consider the economic costs and environmental impact of biomass distribution from the suppliers to the biomass plants. In this research, the Epsilon constraint method is adopted to generate Pareto fonts, which provides the trade-offs between two objectives. Moreover, sensitivity analysis is implemented to provide decision-makers with information about a network with changed parameters such as demand. Our model allows the decision maker to determine the capacity of warehouses and biomass power plants, inventory levels, type of trucks, etc. The proposed model is verified and evaluated using a practical dataset from Can Tho province, Central Mekong River Delta in Vietnam, generating several benefits for energy security and sustainability. Such a network includes 3 types of power plants, 3 scales of warehouses, 13 potential locations, and 41 suppliers. From the generated solutions, with the proportion of biomass electricity satisfaction varying from 5% to 30%, Hung Phu, O Mon, and Cai Rang industrial parks are the most suitable for power plants.
{"title":"Bi-objective optimization modeling for biomass supply chain planning","authors":"Chia-Nan Wang, Thi-Be-Oanh Cao, Duc Duy Nguyen, Thanh-Tuan Dang","doi":"10.1177/00202940241226603","DOIUrl":"https://doi.org/10.1177/00202940241226603","url":null,"abstract":"Biomass energy plays an essential role in renewable energy for many reasons, such as reducing the dependence on fossil fuels and lowering greenhouse gas emissions, providing heat, electricity, and biofuels for various applications, and utilizing waste materials for helpful energy products. Besides, it can create employment opportunities and promote rural development, especially in developing countries where biomass resources are abundant and accessible. In the context of renewable energy research and application, this paper aims to develop a multi-objective mixed integer linear programming for designing multiple echelon biomass supply chain networks. The model is formulated to consider the economic costs and environmental impact of biomass distribution from the suppliers to the biomass plants. In this research, the Epsilon constraint method is adopted to generate Pareto fonts, which provides the trade-offs between two objectives. Moreover, sensitivity analysis is implemented to provide decision-makers with information about a network with changed parameters such as demand. Our model allows the decision maker to determine the capacity of warehouses and biomass power plants, inventory levels, type of trucks, etc. The proposed model is verified and evaluated using a practical dataset from Can Tho province, Central Mekong River Delta in Vietnam, generating several benefits for energy security and sustainability. Such a network includes 3 types of power plants, 3 scales of warehouses, 13 potential locations, and 41 suppliers. From the generated solutions, with the proportion of biomass electricity satisfaction varying from 5% to 30%, Hung Phu, O Mon, and Cai Rang industrial parks are the most suitable for power plants.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"43 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432575","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}
Pub Date : 2024-02-24DOI: 10.1177/00202940241236288
Mourad Yessef, M. Taoussi, H. Benbouhenni, A. Lagrioui, I. Colak, Hatim Ameziane, Btissam Majout, B. Bossoufi
The direct power command (DPC) of the doubly-fed induction generators (DFIG) has garnered increasing attention due to its simplicity, ease of implementation, and fast dynamic response that distinguished it from other controls. Recently, nonlinear controllers have been suggested to improve the DPC robustness, in particular the minimization of the DFIG power fluctuations. In the present research work, two different controls are used to regulate the DFIG power, where the first is a traditional DPC and the second is the DPC based on neural super-twisting algorithm (NSTA) controllers. The NSTA controllers were used instead of hysteresis comparators in the DPC technique, and the pulse width modulation (PWM) technique was used as a suitable solution instead of the switching table to better manage the state of switches and simplify the control system. The proposed controls were investigated and implemented using Matlab software and Dspace 1104 card with different tests to determine the best control. Experimental and simulation results show the good efficiency of the NSTA controller in improving energy and current quality. Also, the comparison is made between both techniques and the other controls in terms of reducing current harmonic distortions and ratios of the DFIG power fluctuations.
{"title":"Two different controllers-based DPC of the doubly-fed induction generator with real-time implementation on dSPACE 1104 controller board","authors":"Mourad Yessef, M. Taoussi, H. Benbouhenni, A. Lagrioui, I. Colak, Hatim Ameziane, Btissam Majout, B. Bossoufi","doi":"10.1177/00202940241236288","DOIUrl":"https://doi.org/10.1177/00202940241236288","url":null,"abstract":"The direct power command (DPC) of the doubly-fed induction generators (DFIG) has garnered increasing attention due to its simplicity, ease of implementation, and fast dynamic response that distinguished it from other controls. Recently, nonlinear controllers have been suggested to improve the DPC robustness, in particular the minimization of the DFIG power fluctuations. In the present research work, two different controls are used to regulate the DFIG power, where the first is a traditional DPC and the second is the DPC based on neural super-twisting algorithm (NSTA) controllers. The NSTA controllers were used instead of hysteresis comparators in the DPC technique, and the pulse width modulation (PWM) technique was used as a suitable solution instead of the switching table to better manage the state of switches and simplify the control system. The proposed controls were investigated and implemented using Matlab software and Dspace 1104 card with different tests to determine the best control. Experimental and simulation results show the good efficiency of the NSTA controller in improving energy and current quality. Also, the comparison is made between both techniques and the other controls in terms of reducing current harmonic distortions and ratios of the DFIG power fluctuations.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434789","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}
Pub Date : 2024-02-19DOI: 10.1177/00202940241228996
Minghui Wu, Bo Gao, Heping Hu, Konglin Hong
Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.
{"title":"Research on path planning of tea picking robot based on ant colony algorithm","authors":"Minghui Wu, Bo Gao, Heping Hu, Konglin Hong","doi":"10.1177/00202940241228996","DOIUrl":"https://doi.org/10.1177/00202940241228996","url":null,"abstract":"Robot tea picking is an inevitable trend to solve the problem of tea picking, and the picking path planning is directly related to the robot picking efficiency. An Improved Ant Colony Algorithm (IACA) is proposed, which firstly introduces the adaptive adjustment mechanism into the pheromone volatilization factor of the ant colony algorithm, and then sets the pheromone volatilization factor with a high initial value to improve the searching speed, and then adjusts the size of its value within a certain range in real time according to the iterative results, and finally solves the problem that the searching of the ant colony algorithm is prone to fall into the local optimal solution. On the basis of visual recognition of tea leaves and obtaining coordinate information, the improved ant colony algorithm is used to enter the path planning simulation experiments, and the planning results of the other six algorithms are compared with the similar algorithms and dissimilar algorithms, and the experimental results indicate that the IACA method has improved the shortest path index by 5% compared to the basic ant colony algorithm, and by an average of 4% compared to similar improved ant colony algorithms. In comparison to different optimization algorithms, the enhancement has an average increase of 6%; Furthermore, the convergence speed has been improved by 60% compared to six other methods. The standard deviation of repeated experimental results is 50% lower than the other six methods. The gap between the results of multiple repeated experiments is small, the degree of fluctuation is low, and the calculation results are more stable, which verifies the superiority of IACA method. Therefore, the improvement of the ant colony algorithm makes the pheromone concentration value with adaptive adjustment ability, which reflects good effects in path optimization, convergence speed improvement, stability of results, etc., and has good application value for the path planning problems such as tea picking, which has complex paths and large computational volume.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"23 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140450179","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}
In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.
为了降低焊接机器人的能耗，确保机器人关节的协同运动，提出了一种基于改进的麻雀搜索算法的最优能耗轨迹规划方法。首先，根据机器人的关节力矩和角速度建立能耗最优的轨迹规划模型。为使机器人各关节的速度、加速度和颠簸有界且连续，用七度 B 样条曲线构建关节空间轨迹。结合运动学和动力学参数，计算出机器人的总能耗。在改进的麻雀搜索算法基础上，利用精英反向学习、非支配排序和高斯-考奇变异策略求解最优能耗对应的时间序列，进而规划出最优能耗的连续运动轨迹。仿真结果表明，所提方法不仅能实现连续平滑控制目标，还能有效降低能耗。
{"title":"Optimal trajectory planning of robot energy consumption based on improved sparrow search algorithm","authors":"Yaosheng Zhou, Guirong Han, Ziang Wei, Zixin Huang, Xubing Chen, Jianjun Wu","doi":"10.1177/00202940231220080","DOIUrl":"https://doi.org/10.1177/00202940231220080","url":null,"abstract":"In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"59 46","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139777732","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}
Pub Date : 2024-02-14DOI: 10.1177/00202940241226598
Zhou Lan, Jingsong Chen, Cheng Xue, Jun Lan, Bing Wang, Yupu Wang, Yong Yang
Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.
温度稳定性是影响光刻机系统中大多数子系统性能的关键因素，因为系统组件精度高，对温度变化敏感。光刻机的温度控制系统具有惯性常数大、时间延迟特性以及易受多种干扰的特点。光刻机的温度控制系统主要要求响应速度快、精度高、温度控制稳定恒定。本研究的贡献不仅在于避免了基于实时参数估计和神经网络自调整的复杂精密建模过程，还在于提高了外部干扰下的实时温度控制性能。成功地提出了一种基于广义预测控制（GPC）和反向传播（BP）神经网络比例积分（PI）控制的级联结构的新型自适应算法，用于具有大惯性常数、时间延迟和多重干扰的光刻机的高精度温度控制。本研究首先开发了基于热交换器和加热器的液体循环温度控制系统。其次，成功地提出了一种由 GPC 和 BP 神经网络 PI 控制组成的自适应控制器。利用 BP 神经网络实现了 PI 控制器参数的实时调节，并通过最小二乘法实时确定了控制系统的数学模型参数。此外，还从鲁棒性和误差分析的定量研究方面，评估了拟议控制器与传统 PI 控制器和 GPC 控制器的性能比较。最后，通过在不同工作场景下的仿真结果，研究了采用所提出的自适应 GPC-PI 算法控制的温度控制系统的温度稳定性和鲁棒性。仿真结果表明，在干扰输入的作用下，所提出算法的稳态误差小于 0.01°C。它能有效抵消环境干扰和系统参数时变的影响。仿真实验结果表明，与传统控制方法相比，所提出的自适应 GPC 和 PI 控制算法在控制精度、抗干扰能力和鲁棒性等方面具有显著优势。
{"title":"A temperature control algorithm for lithography machine based on generalized predictive control and BP neural network PI control","authors":"Zhou Lan, Jingsong Chen, Cheng Xue, Jun Lan, Bing Wang, Yupu Wang, Yong Yang","doi":"10.1177/00202940241226598","DOIUrl":"https://doi.org/10.1177/00202940241226598","url":null,"abstract":"Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"392 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839479","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}
Fieldbus transmitters are commonly used in modern industrial productions, particularly in Safety Instrumented Systems (SIS). Safety and security are critical considerations in the design and operation of these transmitters. Previous research has tended to address safety issues and security issues separately, but with the increasing complexity of network technology, it is important to analyze them simultaneously. In this paper, a systematic framework for comprehensively analyzing random failures and cyber-attack failures is proposed. The framework adopts the FMEA-IMEA method, which combines Failure Modes and Effects Analysis (FMEA) and Intrusion Modes and Effects Analysis (IMEA), to analyze failure modes and effects of fieldbus transmitters. In addition, by extending Reliability Block Diagrams (RBD), the impact of random failures and cyber-attack failures on fieldbus transmitters is quantitatively determined. At the same time, calculation approach of the residual error rate (RER), Component counting method, and Monte Carlo are used to determine random failure rate and cyber-attack failure rate. Using fieldbus pressure transmitter and fieldbus temperature transmitter as examples, the results demonstrate that security issues can significantly impact the safety integrity level. In fact, the safety integrity level is reduced from SIL3 to SIL1 when cyber-attacks are considered. Compared to existing FMEA, the proposed approach offers a more comprehensive analysis of random failures and cyber-attack failures in fieldbus transmitters.
{"title":"Research on the integrated failure analysis method of safety and security of fieldbus transmitters","authors":"Xiufang Zhou, Aidong Xu, Bingjun Yan, Yue Sun, Wenbo Chen, Jiao Yang","doi":"10.1177/00202940231222811","DOIUrl":"https://doi.org/10.1177/00202940231222811","url":null,"abstract":"Fieldbus transmitters are commonly used in modern industrial productions, particularly in Safety Instrumented Systems (SIS). Safety and security are critical considerations in the design and operation of these transmitters. Previous research has tended to address safety issues and security issues separately, but with the increasing complexity of network technology, it is important to analyze them simultaneously. In this paper, a systematic framework for comprehensively analyzing random failures and cyber-attack failures is proposed. The framework adopts the FMEA-IMEA method, which combines Failure Modes and Effects Analysis (FMEA) and Intrusion Modes and Effects Analysis (IMEA), to analyze failure modes and effects of fieldbus transmitters. In addition, by extending Reliability Block Diagrams (RBD), the impact of random failures and cyber-attack failures on fieldbus transmitters is quantitatively determined. At the same time, calculation approach of the residual error rate (RER), Component counting method, and Monte Carlo are used to determine random failure rate and cyber-attack failure rate. Using fieldbus pressure transmitter and fieldbus temperature transmitter as examples, the results demonstrate that security issues can significantly impact the safety integrity level. In fact, the safety integrity level is reduced from SIL3 to SIL1 when cyber-attacks are considered. Compared to existing FMEA, the proposed approach offers a more comprehensive analysis of random failures and cyber-attack failures in fieldbus transmitters.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"10 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139838149","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}
Fieldbus transmitters are commonly used in modern industrial productions, particularly in Safety Instrumented Systems (SIS). Safety and security are critical considerations in the design and operation of these transmitters. Previous research has tended to address safety issues and security issues separately, but with the increasing complexity of network technology, it is important to analyze them simultaneously. In this paper, a systematic framework for comprehensively analyzing random failures and cyber-attack failures is proposed. The framework adopts the FMEA-IMEA method, which combines Failure Modes and Effects Analysis (FMEA) and Intrusion Modes and Effects Analysis (IMEA), to analyze failure modes and effects of fieldbus transmitters. In addition, by extending Reliability Block Diagrams (RBD), the impact of random failures and cyber-attack failures on fieldbus transmitters is quantitatively determined. At the same time, calculation approach of the residual error rate (RER), Component counting method, and Monte Carlo are used to determine random failure rate and cyber-attack failure rate. Using fieldbus pressure transmitter and fieldbus temperature transmitter as examples, the results demonstrate that security issues can significantly impact the safety integrity level. In fact, the safety integrity level is reduced from SIL3 to SIL1 when cyber-attacks are considered. Compared to existing FMEA, the proposed approach offers a more comprehensive analysis of random failures and cyber-attack failures in fieldbus transmitters.
{"title":"Research on the integrated failure analysis method of safety and security of fieldbus transmitters","authors":"Xiufang Zhou, Aidong Xu, Bingjun Yan, Yue Sun, Wenbo Chen, Jiao Yang","doi":"10.1177/00202940231222811","DOIUrl":"https://doi.org/10.1177/00202940231222811","url":null,"abstract":"Fieldbus transmitters are commonly used in modern industrial productions, particularly in Safety Instrumented Systems (SIS). Safety and security are critical considerations in the design and operation of these transmitters. Previous research has tended to address safety issues and security issues separately, but with the increasing complexity of network technology, it is important to analyze them simultaneously. In this paper, a systematic framework for comprehensively analyzing random failures and cyber-attack failures is proposed. The framework adopts the FMEA-IMEA method, which combines Failure Modes and Effects Analysis (FMEA) and Intrusion Modes and Effects Analysis (IMEA), to analyze failure modes and effects of fieldbus transmitters. In addition, by extending Reliability Block Diagrams (RBD), the impact of random failures and cyber-attack failures on fieldbus transmitters is quantitatively determined. At the same time, calculation approach of the residual error rate (RER), Component counting method, and Monte Carlo are used to determine random failure rate and cyber-attack failure rate. Using fieldbus pressure transmitter and fieldbus temperature transmitter as examples, the results demonstrate that security issues can significantly impact the safety integrity level. In fact, the safety integrity level is reduced from SIL3 to SIL1 when cyber-attacks are considered. Compared to existing FMEA, the proposed approach offers a more comprehensive analysis of random failures and cyber-attack failures in fieldbus transmitters.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"39 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139778353","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}
Pub Date : 2024-02-14DOI: 10.1177/00202940241226598
Zhou Lan, Jingsong Chen, Cheng Xue, Jun Lan, Bing Wang, Yupu Wang, Yong Yang
Temperature stability is a critical factor affecting the performance of the most subsystems in the lithography system, due to the high precision and sensitivity of system components to temperature variations. The temperature control system of the lithography machine is characterized by its large inertial constant, time delay characteristics, as well as susceptibility to multiple disturbances. The temperature control system of the lithography machine chiefly requires response speed, high accuracy, and stable and constant temperature control. The contribution of this study is not only avoiding complex precision modeling processes based on real-time parameter estimation and neural network self-tuning but also improving the performance of temperature control in real time under external disturbances. A novel adaptive algorithm with a cascade structure based on generalized predictive control (GPC) and backpropagation (BP) neural network proportional-integral (PI) control is successfully proposed for high accuracy temperature control of lithography machine with a large inertial constant, time delay, and multiple disturbances. In this study, firstly, the liquid circulating temperature control system is developed based on heat exchanger and heater. Secondly, an adaptive controller composed of GPC and BP neural network PI control is successfully proposed. A BP neural network is employed to enable the parameters of the PI controller to adjust in real time, and the mathematical model parameters of the control system are identified in real time by the least square method. Also, the performance of the proposed controller is evaluated comparing with conventional PI controller and GPC controller in terms of robustness and quantitative study of error analysis. Finally, the temperature stability and robustness of the temperature control system controlled with the proposed adaptive GPC-PI algorithm has been investigated by the simulation results carried out in different working scenarios. The simulation results show that the steady-state error from the proposed algorithm is less than 0.01°C under the action of disturbance input. It can effectively counteract the influence of environmental interference and time-varying system parameters. The results of the simulation experiment indicate that the proposed adaptive GPC and PI control algorithm exhibits significant advantages in terms of control accuracy, anti-interference ability, and robustness compared to the conventional control method.
温度稳定性是影响光刻系统中大多数子系统性能的关键因素，这是因为系统组件精度高，对温度变化敏感。光刻机的温度控制系统具有惯性常数大、时间延迟特性以及易受多种干扰的特点。光刻机的温度控制系统主要要求响应速度快、精度高、温度控制稳定恒定。本研究的贡献不仅在于避免了基于实时参数估计和神经网络自调整的复杂精密建模过程，还在于提高了外部干扰下的实时温度控制性能。成功地提出了一种基于广义预测控制（GPC）和反向传播（BP）神经网络比例积分（PI）控制的级联结构的新型自适应算法，用于具有大惯性常数、时间延迟和多重干扰的光刻机的高精度温度控制。本研究首先开发了基于热交换器和加热器的液体循环温度控制系统。其次，成功地提出了一种由 GPC 和 BP 神经网络 PI 控制组成的自适应控制器。利用 BP 神经网络实现了 PI 控制器参数的实时调节，并通过最小二乘法实时确定了控制系统的数学模型参数。此外，还从鲁棒性和误差分析的定量研究方面，评估了拟议控制器与传统 PI 控制器和 GPC 控制器的性能比较。最后，通过在不同工作场景下的仿真结果，研究了采用所提出的自适应 GPC-PI 算法控制的温度控制系统的温度稳定性和鲁棒性。仿真结果表明，在干扰输入的作用下，所提出算法的稳态误差小于 0.01°C。它能有效抵消环境干扰和系统参数时变的影响。仿真实验结果表明，与传统控制方法相比，所提出的自适应 GPC 和 PI 控制算法在控制精度、抗干扰能力和鲁棒性等方面具有显著优势。
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In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.
为了降低焊接机器人的能耗，确保机器人关节的协同运动，提出了一种基于改进的麻雀搜索算法的最优能耗轨迹规划方法。首先，根据机器人的关节力矩和角速度建立能耗最优的轨迹规划模型。为使机器人各关节的速度、加速度和颠簸有界且连续，用七度 B 样条曲线构建关节空间轨迹。结合运动学和动力学参数，计算出机器人的总能耗。在改进的麻雀搜索算法基础上，利用精英反向学习、非支配排序和高斯-考奇变异策略求解最优能耗对应的时间序列，进而规划出最优能耗的连续运动轨迹。仿真结果表明，所提方法不仅能实现连续平滑控制目标，还能有效降低能耗。
{"title":"Optimal trajectory planning of robot energy consumption based on improved sparrow search algorithm","authors":"Yaosheng Zhou, Guirong Han, Ziang Wei, Zixin Huang, Xubing Chen, Jianjun Wu","doi":"10.1177/00202940231220080","DOIUrl":"https://doi.org/10.1177/00202940231220080","url":null,"abstract":"In order to reduce the energy consumption of the welding robot and ensure the cooperative movement of the robot joints, a trajectory planning method with optimal energy consumption based on improved sparrow search algorithm is proposed. Firstly, the trajectory planning model with optimal energy consumption is established based on the joint torque and angular velocity of the robot. To make the velocity, acceleration and jerk of each joint of the robot be bounded and continuous, the joint space trajectory is constructed with seventh degree B-spline curve. The total energy consumption of the robot is calculated by combining kinematic and dynamic parameters. On the basis of improved sparrow search algorithm, the time series corresponding to the optimal energy consumption is solved by using elite reverse learning, non-dominated sorting and Gaussian-Cauchy variation strategy, and then the optimal continuous motion trajectory of energy consumption is planned. The simulation results show that the proposed method can not only achieve continuous smooth control objective, but also effectively reduce energy consumption.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837677","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}
Pub Date : 2024-02-14DOI: 10.1177/00202940241228725
Boyang Xu
Path planning and trajectory tracking are very meaningful for the field of autonomous driving, but currently path planning still has problems such as non-optimal paths and insufficiently accurate paths. This paper addresses the issue of path planning by proposing a improved A-star algorithm and locally zooming on the map technique to achieve precise path planning. Compared with the conventional method, this method reduces the time by 23% and the path length by 21% in the scenarios shown in the paper, respectively, and provides a reference for related research. Moreover, trajectory tracking was achieved using the improved LQR control. Compared with the conventional method, the improved LQR control algorithm reduces the average error by 80% in the scenario shown in the paper. Firstly, the A-star algorithm is enhanced by incorporating an unknown path cost estimation function, thereby improving the effect of its path planning in complex environments. Additionally, the method of locally zooming on the map is incorporated, effectively enhancing the accuracy and safety of path planning. Building upon the path planning, further improvements are made to the LQR control algorithm, enabling autonomous deceleration in complex sections, which facilitates better trajectory tracking and enhances the motion control performance of the robot during practical operations.
{"title":"Precise path planning and trajectory tracking based on improved A-star algorithm","authors":"Boyang Xu","doi":"10.1177/00202940241228725","DOIUrl":"https://doi.org/10.1177/00202940241228725","url":null,"abstract":"Path planning and trajectory tracking are very meaningful for the field of autonomous driving, but currently path planning still has problems such as non-optimal paths and insufficiently accurate paths. This paper addresses the issue of path planning by proposing a improved A-star algorithm and locally zooming on the map technique to achieve precise path planning. Compared with the conventional method, this method reduces the time by 23% and the path length by 21% in the scenarios shown in the paper, respectively, and provides a reference for related research. Moreover, trajectory tracking was achieved using the improved LQR control. Compared with the conventional method, the improved LQR control algorithm reduces the average error by 80% in the scenario shown in the paper. Firstly, the A-star algorithm is enhanced by incorporating an unknown path cost estimation function, thereby improving the effect of its path planning in complex environments. Additionally, the method of locally zooming on the map is incorporated, effectively enhancing the accuracy and safety of path planning. Building upon the path planning, further improvements are made to the LQR control algorithm, enabling autonomous deceleration in complex sections, which facilitates better trajectory tracking and enhances the motion control performance of the robot during practical operations.","PeriodicalId":510299,"journal":{"name":"Measurement and Control","volume":"31 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139779102","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}
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