This paper proposes an adaptive integral sliding mode tracking control for robotic manipulators. Our proposed control method is developed based on the benefits of both integral sliding mode control and adaptive control, such as high robustness, high accuracy, and estimation ability. In this paper, an integral sliding mode controller is designed with the elimination of the reaching stage to provide better trajectory tracking accuracy and to stabilize the closed-loop system. To reduce the computation complexity, an adaptive controller with only one simple adaptive law is used to estimate the upper-bound values of the lumped model uncertainties. As a result, the requirement of their prior knowledge is eliminated and then decrease the computation cost. Consequently, this controller provides better tracking accuracy and handles the dynamic uncertainties and external disturbances more strongly. The system global stability of the controller is guaranteed by using Lyapunov criteria. Finally, the effectiveness of the proposed control method is tested by computer simulation for a PUMA560 robotic manipulator.
{"title":"An Adaptive Integral Sliding Mode Tracking Control for Robotic Manipulators","authors":"A. Vo, Hee-Jun Kang, T. Le","doi":"10.1145/3386164.3387260","DOIUrl":"https://doi.org/10.1145/3386164.3387260","url":null,"abstract":"This paper proposes an adaptive integral sliding mode tracking control for robotic manipulators. Our proposed control method is developed based on the benefits of both integral sliding mode control and adaptive control, such as high robustness, high accuracy, and estimation ability. In this paper, an integral sliding mode controller is designed with the elimination of the reaching stage to provide better trajectory tracking accuracy and to stabilize the closed-loop system. To reduce the computation complexity, an adaptive controller with only one simple adaptive law is used to estimate the upper-bound values of the lumped model uncertainties. As a result, the requirement of their prior knowledge is eliminated and then decrease the computation cost. Consequently, this controller provides better tracking accuracy and handles the dynamic uncertainties and external disturbances more strongly. The system global stability of the controller is guaranteed by using Lyapunov criteria. Finally, the effectiveness of the proposed control method is tested by computer simulation for a PUMA560 robotic manipulator.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129847616","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}
Mahmood Al-Tekreeti, S. Özyer, Can Özdemir, Altuğ Karadağ, Saad Al-Dakheel
In this paper, Unmanned Surface Vehicles (USV) prototype and system design have been presented for the rescue of human life at sea. The USV will be protected from obstacles that may cause a crash to the USV by avoiding the obstacles using many kinds of detection sensors. All of these sensors send results to Crash Avoidance System (CAS) and to the main computer to control the USV direction depends on the obstacle shape, size or if it is a moving obstacle or not. The sensors that will be used for this purpose are Light Detection and Ranging (LIDAR) sensor, LIDAR-Lite sensors and ultrasonic sensors. All the information that will be collected from all these types of sensors will be used to direct the USV to the safe path. This work is a part of the research and development project which is accepted in Turkey Government with the collaboration of the University and Industry.
{"title":"Unmanned Surface Vehicle Prototype with Obstacle Avoidance System Area: Applications and Evaluation of Real-Time Big Data Systems","authors":"Mahmood Al-Tekreeti, S. Özyer, Can Özdemir, Altuğ Karadağ, Saad Al-Dakheel","doi":"10.1145/3386164.3390514","DOIUrl":"https://doi.org/10.1145/3386164.3390514","url":null,"abstract":"In this paper, Unmanned Surface Vehicles (USV) prototype and system design have been presented for the rescue of human life at sea. The USV will be protected from obstacles that may cause a crash to the USV by avoiding the obstacles using many kinds of detection sensors. All of these sensors send results to Crash Avoidance System (CAS) and to the main computer to control the USV direction depends on the obstacle shape, size or if it is a moving obstacle or not. The sensors that will be used for this purpose are Light Detection and Ranging (LIDAR) sensor, LIDAR-Lite sensors and ultrasonic sensors. All the information that will be collected from all these types of sensors will be used to direct the USV to the safe path. This work is a part of the research and development project which is accepted in Turkey Government with the collaboration of the University and Industry.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128447547","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}
Tao He, Wenting Sheng, Shuaifang Wen, Xing Wang, Wenchao Liu, Liangen Yang
In the industrial production, both ceiling lighting and sunlight through the ceiling in the workshop can cause noise spots in the image acquired in the CCD laser online positioning system. Moreover, these ambient noise spots are interlaced with the positioning laser spots in the image, which makes it difficult to obtain the position information of the positioning laser spots accurately. Aiming at this problem, in this paper, according to the geometric characteristics of both positioning laser spots and ambient noise spot, a denoising localization algorithm for positioning laser spots and noise spots interlaced image is proposed, and the cylindrical bar positioning system is used as the experimental object to verify the algorithm. The algorithm steps are as follows, firstly the noise spot is located according to the geometric characteristics of the two kinds of spots, and the non-overlapping region of the two kinds of spots are identified at the same time. Secondly, the noise spots in the non-overlapping area are eliminated. Finally, the existing Blob positioning algorithm is slightly modified to complete the accurate extraction of the position coordinates of the positioning laser spots pixel. The algorithm can effectively eliminate the ambient noise spots on the premise of ensuring that the positioning laser spots are not damaged, and accurately realize the measurement of the position coordinates of the positioning laser spots pixel.
{"title":"Research on Denoising Localization Algorithm for Positioning Laser Spots and Noise Spots Interlaced Image","authors":"Tao He, Wenting Sheng, Shuaifang Wen, Xing Wang, Wenchao Liu, Liangen Yang","doi":"10.1145/3386164.3387258","DOIUrl":"https://doi.org/10.1145/3386164.3387258","url":null,"abstract":"In the industrial production, both ceiling lighting and sunlight through the ceiling in the workshop can cause noise spots in the image acquired in the CCD laser online positioning system. Moreover, these ambient noise spots are interlaced with the positioning laser spots in the image, which makes it difficult to obtain the position information of the positioning laser spots accurately. Aiming at this problem, in this paper, according to the geometric characteristics of both positioning laser spots and ambient noise spot, a denoising localization algorithm for positioning laser spots and noise spots interlaced image is proposed, and the cylindrical bar positioning system is used as the experimental object to verify the algorithm. The algorithm steps are as follows, firstly the noise spot is located according to the geometric characteristics of the two kinds of spots, and the non-overlapping region of the two kinds of spots are identified at the same time. Secondly, the noise spots in the non-overlapping area are eliminated. Finally, the existing Blob positioning algorithm is slightly modified to complete the accurate extraction of the position coordinates of the positioning laser spots pixel. The algorithm can effectively eliminate the ambient noise spots on the premise of ensuring that the positioning laser spots are not damaged, and accurately realize the measurement of the position coordinates of the positioning laser spots pixel.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227830","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}
This paper aims to evaluate singing performance based on deep metric learning. As the vocal sound will be the input, we will first need to separate that from a soundtrack. After the separation, the vocal audio will be represented by Mel-spectrogram as an input in our proposed model. The process to build up our model splits into pre-training and training steps. Meta learning is adopted for pre-training while deep metric learning is adopted for training. The output of the model is a Euclidean distance reflecting the singers' performance, which is determined by comparing their sounds to the originals. Experimental results show a stable and reliable singing evaluation.
{"title":"Singing Evaluation based on Deep Metric Learning","authors":"Terry Tan","doi":"10.1145/3386164.3389096","DOIUrl":"https://doi.org/10.1145/3386164.3389096","url":null,"abstract":"This paper aims to evaluate singing performance based on deep metric learning. As the vocal sound will be the input, we will first need to separate that from a soundtrack. After the separation, the vocal audio will be represented by Mel-spectrogram as an input in our proposed model. The process to build up our model splits into pre-training and training steps. Meta learning is adopted for pre-training while deep metric learning is adopted for training. The output of the model is a Euclidean distance reflecting the singers' performance, which is determined by comparing their sounds to the originals. Experimental results show a stable and reliable singing evaluation.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127764227","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}
With the fast development of Information Technology, program software and mobile applications have been widely used in the world, and are playing important roles in human's daily life. Thus, writing programming codes has been important work in many fields. however, it is a hard and time-cost task which presents a great amount of workload to programmers. To make programmers' work easier, intelligent code completion models have been a popular research topic in recent years. This paper designs Deep Learning based models to automatically complete programming codes, which are LSTM-based neural networks, and are combined with several techniques such as Word Embedding models in NLP (Natural Language Processing), and Multihead Attention Mechanism. Moreover, in the models, this paper raises a new algorithm of generating input sequences from partial AST (Abstract Syntax Tree) that have most relevance with nodes to be predicted which is named as RZT (Reverse Zig-zag Traverse) Algorithm, and is the first work of applying Multihead Attention Block into this task. This paper makes insight into codes of several different programming languages, and the models this paper presents show good performances in accuracy comparing with the state-of-art models.
随着信息技术的快速发展，程序软件和移动应用程序在世界范围内得到了广泛的应用，在人们的日常生活中发挥着重要的作用。因此，编写程序代码在许多领域都是很重要的工作。然而，这是一项困难且耗时的任务，给程序员带来了大量的工作量。为了使程序员的工作更容易，智能代码完成模型是近年来的一个热门研究课题。本文设计了基于深度学习的自动完成编程代码的模型，该模型是基于lstm的神经网络，并结合了NLP(自然语言处理)中的词嵌入模型和多头注意机制等技术。此外，在模型中，本文提出了一种从部分AST (Abstract Syntax Tree)生成与待预测节点最相关的输入序列的新算法，称为RZT (Reverse z -zag Traverse)算法，这是首个将多头注意力块应用于该任务的算法。本文深入研究了几种不同编程语言的代码，与目前的模型相比，本文提出的模型在精度上有良好的表现。
{"title":"Deep Learning Based Code Completion Models for Programming Codes","authors":"Shuai Wang, Jinyang Liu, Ye Qiu, Zhiyi Ma, Junfei Liu, Zhonghai Wu","doi":"10.1145/3386164.3389083","DOIUrl":"https://doi.org/10.1145/3386164.3389083","url":null,"abstract":"With the fast development of Information Technology, program software and mobile applications have been widely used in the world, and are playing important roles in human's daily life. Thus, writing programming codes has been important work in many fields. however, it is a hard and time-cost task which presents a great amount of workload to programmers. To make programmers' work easier, intelligent code completion models have been a popular research topic in recent years. This paper designs Deep Learning based models to automatically complete programming codes, which are LSTM-based neural networks, and are combined with several techniques such as Word Embedding models in NLP (Natural Language Processing), and Multihead Attention Mechanism. Moreover, in the models, this paper raises a new algorithm of generating input sequences from partial AST (Abstract Syntax Tree) that have most relevance with nodes to be predicted which is named as RZT (Reverse Zig-zag Traverse) Algorithm, and is the first work of applying Multihead Attention Block into this task. This paper makes insight into codes of several different programming languages, and the models this paper presents show good performances in accuracy comparing with the state-of-art models.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131798640","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}
The development of AI directly affects the emergence of new technologies. In modern video games, AI faces a wide range of tasks at various levels. The current situation is such that in addition to standard decision-making, to which the average player is casual, AI often has to do more complex things: to perceive the environment, interact with it, interact with the other AI, move in a complex three-dimensional space and other various tasks. Given the constant development of the gaming industry, the requirements for AI are constantly increasing. Therefore, there is the problem of AI flexibility. In video games, we can increasingly see how the battle of two NPCs turns into a simple search of teams to attack and defend. These primitives repel the player, destroying a decent part of the gameplay conceived by the developer. In the same way it is applicable to the visualization of historical events. For accurate reconstruction, it is necessary that the behavior of the agents be similar to human's behavior. In this paper, we give a brief review on some of well-known AI development methods, compare their effectiveness and present a new method of AI development that simulate the behavior of non- player character in melee and ranged combat based on the interaction of three levels: strategic, tactical and operational for decision-making. Combination of the well-known methods of AI development, base agent's model change and improvement in agent understanding of the environment by using the Voronoi diagram. The method proposed in this paper are showing significantly different results from the most popular design methods and the Utility-AI-Behavior Tree method, significantly reducing the distance in terms of key indicators such as survive time, use of useful resources, number of enemies killed. The used method imitates the player's actions, while excluding the human error factor and unexpected actions. The designed AI simulates the player's logical actions with a good accuracy, but is still more predictable than the real players. Mathematical calculations and the distribution of weights on each frame do not have a significant impact on performance, which allows simulating the behavior of many agents at once in one scenario, without losing performance and influencing the resulting sensations from the gameplay.
{"title":"Development of Tactical Level AI for Melee and Range Combat","authors":"V. Gorshkov, A. Zagarskikh","doi":"10.1145/3386164.3386178","DOIUrl":"https://doi.org/10.1145/3386164.3386178","url":null,"abstract":"The development of AI directly affects the emergence of new technologies. In modern video games, AI faces a wide range of tasks at various levels. The current situation is such that in addition to standard decision-making, to which the average player is casual, AI often has to do more complex things: to perceive the environment, interact with it, interact with the other AI, move in a complex three-dimensional space and other various tasks. Given the constant development of the gaming industry, the requirements for AI are constantly increasing. Therefore, there is the problem of AI flexibility. In video games, we can increasingly see how the battle of two NPCs turns into a simple search of teams to attack and defend. These primitives repel the player, destroying a decent part of the gameplay conceived by the developer. In the same way it is applicable to the visualization of historical events. For accurate reconstruction, it is necessary that the behavior of the agents be similar to human's behavior. In this paper, we give a brief review on some of well-known AI development methods, compare their effectiveness and present a new method of AI development that simulate the behavior of non- player character in melee and ranged combat based on the interaction of three levels: strategic, tactical and operational for decision-making. Combination of the well-known methods of AI development, base agent's model change and improvement in agent understanding of the environment by using the Voronoi diagram. The method proposed in this paper are showing significantly different results from the most popular design methods and the Utility-AI-Behavior Tree method, significantly reducing the distance in terms of key indicators such as survive time, use of useful resources, number of enemies killed. The used method imitates the player's actions, while excluding the human error factor and unexpected actions. The designed AI simulates the player's logical actions with a good accuracy, but is still more predictable than the real players. Mathematical calculations and the distribution of weights on each frame do not have a significant impact on performance, which allows simulating the behavior of many agents at once in one scenario, without losing performance and influencing the resulting sensations from the gameplay.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132284172","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}
Fault prediction is of great importance to ensuring weapon equipments' safety and reliability. Usually the data for fault detection and prediction of weapon equipments have features like small samples and multi-parameter. Currently the main fault prediction methods have achieved some success in practical applications, but all fall short at some aspects. Based on grey prediction theory and with analysis of disadvantages of GM(1, 1) model, an adaptive prediction model with several characteristic parameters for small samples is put forward. This model modifies initial value and background value, and takes interrelations of the parameters and characteristics of prediction series into account. The model is then used for prediction and analysis with the multi-parameter data of certain aero-engine. The results show that the model has good prediction precision, which in turn validates its availability.
{"title":"Adaptive Multi-Parameter Prediction Model Based on Grey Theory","authors":"Jie Jiang, Zhang Yan","doi":"10.1145/3386164.3386169","DOIUrl":"https://doi.org/10.1145/3386164.3386169","url":null,"abstract":"Fault prediction is of great importance to ensuring weapon equipments' safety and reliability. Usually the data for fault detection and prediction of weapon equipments have features like small samples and multi-parameter. Currently the main fault prediction methods have achieved some success in practical applications, but all fall short at some aspects. Based on grey prediction theory and with analysis of disadvantages of GM(1, 1) model, an adaptive prediction model with several characteristic parameters for small samples is put forward. This model modifies initial value and background value, and takes interrelations of the parameters and characteristics of prediction series into account. The model is then used for prediction and analysis with the multi-parameter data of certain aero-engine. The results show that the model has good prediction precision, which in turn validates its availability.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132357886","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}
Realistic pedestrian flow visualization is a complex problem of multi-agent modeling. It can be applied to different crowd dynamics solutions. This paper presents a new route planning method for agents that can be used in interactive simulation of crowd movement. It is based on game development technologies and modern artificial intelligence techniques. We use reinforcement learning to train the agent to navigate through moving pedestrians. A set of experiments were carried out and the results of the study were evaluated. The proposed approach is compared with existing methods and its perspectives are discussed.
{"title":"Intellectual Route Planning Methods for Realistic Agents' Movement","authors":"D. Chebotkov, A. Zagarskikh","doi":"10.1145/3386164.3389090","DOIUrl":"https://doi.org/10.1145/3386164.3389090","url":null,"abstract":"Realistic pedestrian flow visualization is a complex problem of multi-agent modeling. It can be applied to different crowd dynamics solutions. This paper presents a new route planning method for agents that can be used in interactive simulation of crowd movement. It is based on game development technologies and modern artificial intelligence techniques. We use reinforcement learning to train the agent to navigate through moving pedestrians. A set of experiments were carried out and the results of the study were evaluated. The proposed approach is compared with existing methods and its perspectives are discussed.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133624842","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}
The problem of large modeling error exists in the modeling and compensation of Fiber Optic Gyroscope (FOG) temperature drift by traditional polynomial fitting. In the case, a compensation method for FOG temperature drift error based on double-section polynomial fitting is studied, which can build the model of the FOG temperature drift error in both the startup section and the balanced section. Experimental results show that the new method can improve both the accuracy of modeling and the effect of compensation effectively.
{"title":"A Compensation Method for FOG Temperature Drift Error Based on Double-section Polynomial Fitting","authors":"Yang Li, Ke Chen, Liu-wei Mao","doi":"10.1145/3386164.3386179","DOIUrl":"https://doi.org/10.1145/3386164.3386179","url":null,"abstract":"The problem of large modeling error exists in the modeling and compensation of Fiber Optic Gyroscope (FOG) temperature drift by traditional polynomial fitting. In the case, a compensation method for FOG temperature drift error based on double-section polynomial fitting is studied, which can build the model of the FOG temperature drift error in both the startup section and the balanced section. Experimental results show that the new method can improve both the accuracy of modeling and the effect of compensation effectively.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123751453","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}
This paper proposes a combination between a neural network and an adaptive sliding mode control for trajectory tracking control of a 3-DOF planar parallel manipulator. It has a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. The proposed control algorithm is to use a PID sliding mode surface, an adaptive sliding mode controller with a neural network to overcome the drawback of the traditional sliding mode controllers, such as slow response rate with variation of uncertainties and external disturbances, chattering, and upper bound values of undefined dynamics which affects system performance, high wear of moving mechanical parts and high heat losses in power circuits. The radial basis function neural network is designed to compensate for uncertainties and external disturbances, which allows small switching gain. Hence, the chattering can be significantly reduced. In addition, an adaptive control law is used to adaptively converge small switching gains of the sliding mode controller as the neural network reduces model uncertainties. The effectiveness of the proposed control strategy is demonstrated by simulations which are conducted by using the combination of Sim-Mechanics and SolidWorks.
{"title":"Adaptive Neural Sliding Mode Control for 3-DOF Planar Parallel Manipulators","authors":"Thanh Nguyen Truong, Hee-Jun Kang, T. Le","doi":"10.1145/3386164.3387261","DOIUrl":"https://doi.org/10.1145/3386164.3387261","url":null,"abstract":"This paper proposes a combination between a neural network and an adaptive sliding mode control for trajectory tracking control of a 3-DOF planar parallel manipulator. It has a complicated dynamic model, including modelling uncertainties, frictional uncertainties and external disturbances. The proposed control algorithm is to use a PID sliding mode surface, an adaptive sliding mode controller with a neural network to overcome the drawback of the traditional sliding mode controllers, such as slow response rate with variation of uncertainties and external disturbances, chattering, and upper bound values of undefined dynamics which affects system performance, high wear of moving mechanical parts and high heat losses in power circuits. The radial basis function neural network is designed to compensate for uncertainties and external disturbances, which allows small switching gain. Hence, the chattering can be significantly reduced. In addition, an adaptive control law is used to adaptively converge small switching gains of the sliding mode controller as the neural network reduces model uncertainties. The effectiveness of the proposed control strategy is demonstrated by simulations which are conducted by using the combination of Sim-Mechanics and SolidWorks.","PeriodicalId":231209,"journal":{"name":"Proceedings of the 2019 3rd International Symposium on Computer Science and Intelligent Control","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124973286","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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