Pub Date : 2019-05-01DOI: 10.1109/DDCLS.2019.8908994
Gang Sun, Mingxin Wang
An adaptive tracking controller design method is developed for a class of nonlinear systems with non-lower triangular form and linear parameterized uncertainties by combining backstepping and dynamic surface control (DSC) technology. In the design, traditional backstepping design process is used to establish control laws recursively, and unknown parameters of control laws are estimated online. By using DSC technology, the problem of circular structure of the controller is eliminated. Stability results of closed-loop system show that the uniform ultimate boundedness of closed-loop system signals can be guaranteed. Besides, the steady state tracking error of the system can be adjusted to a small neighborhood of zero by selecting appropriate control parameters. The efficacy of the designed approach is demonstrated via a numerical simulation example.
{"title":"A DSC Based Adaptive Control Scheme for A Class of Uncertain Non-lower Triangular Nonlinear Systems","authors":"Gang Sun, Mingxin Wang","doi":"10.1109/DDCLS.2019.8908994","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908994","url":null,"abstract":"An adaptive tracking controller design method is developed for a class of nonlinear systems with non-lower triangular form and linear parameterized uncertainties by combining backstepping and dynamic surface control (DSC) technology. In the design, traditional backstepping design process is used to establish control laws recursively, and unknown parameters of control laws are estimated online. By using DSC technology, the problem of circular structure of the controller is eliminated. Stability results of closed-loop system show that the uniform ultimate boundedness of closed-loop system signals can be guaranteed. Besides, the steady state tracking error of the system can be adjusted to a small neighborhood of zero by selecting appropriate control parameters. The efficacy of the designed approach is demonstrated via a numerical simulation example.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"27 1","pages":"523-527"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74138087","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908973
Bi Zhang, Xiaowei Tan, Xingang Zhao
Hammerstein models have been considered as a class of well-known nonlinear systems, which have been prove to be attractive for system modeling and controller design tasks. In this brief, we introduce a new control strategy for such kind of systems. Interestingly, the system uncertainties, including the input block description error, the linear subsystem's unstable zero property and the colored added noise issues, have all been considered. According to the modified cost function, the parameter adaptation law has been online implemented throughout the use of a robust estimator. Meanwhile, based on the parameter estimates, the control law has been designed for the compensation of the modeling mismatch which is caused by unmodeled dynamics estimation. A simple but rigorous proof has been given to illustrate that the nonlinear model based control system stability can be properly achieved based on some reasonable and practical conditions. Finally, the proposed controller has been used for a representative nonlinear system, that is, a continuous stirred tank reactor (CSTR) system. Comparison studies have been presented to show the wider applicability of the novel method than some existing ones.
{"title":"A Novel Self-tuning Control Method with Application to Nonlinear Processes","authors":"Bi Zhang, Xiaowei Tan, Xingang Zhao","doi":"10.1109/DDCLS.2019.8908973","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908973","url":null,"abstract":"Hammerstein models have been considered as a class of well-known nonlinear systems, which have been prove to be attractive for system modeling and controller design tasks. In this brief, we introduce a new control strategy for such kind of systems. Interestingly, the system uncertainties, including the input block description error, the linear subsystem's unstable zero property and the colored added noise issues, have all been considered. According to the modified cost function, the parameter adaptation law has been online implemented throughout the use of a robust estimator. Meanwhile, based on the parameter estimates, the control law has been designed for the compensation of the modeling mismatch which is caused by unmodeled dynamics estimation. A simple but rigorous proof has been given to illustrate that the nonlinear model based control system stability can be properly achieved based on some reasonable and practical conditions. Finally, the proposed controller has been used for a representative nonlinear system, that is, a continuous stirred tank reactor (CSTR) system. Comparison studies have been presented to show the wider applicability of the novel method than some existing ones.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"44 1","pages":"292-297"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84131140","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908971
Dazi Li, Zhudan Chen, Xin Ma, Q. Jin
Due to the extensively existing complexity and uncertainty of systems, feature extraction based on samples is an important task in controller design. As one of the research hotspots, deep auto-encoder neural network can be used to extract features from raw data. This paper proposed a modified deep auto-encoder neural network (MDAENN). An accelerated proximal gradient (APG) method is proposed in this method. MDAENN has lower computational complexity, easier parameters tuning and better convergence than traditional neural network methods, such as RBF, in feature extraction and reconstruction. Based on the feature extraction, least squares policy iteration (LSPI) is used to design the optimal controller. When the dimension of state space is large or even continuous, value function approximation (VFA) method is used instead of value function. Experimental results show that the proposed method can successfully deal with feature extraction and learn control policies with low computational complexity.
{"title":"Feature Extraction for Controller Design by Deep Auto-Encoder Neural Network and Least squares Policy Iteration","authors":"Dazi Li, Zhudan Chen, Xin Ma, Q. Jin","doi":"10.1109/DDCLS.2019.8908971","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908971","url":null,"abstract":"Due to the extensively existing complexity and uncertainty of systems, feature extraction based on samples is an important task in controller design. As one of the research hotspots, deep auto-encoder neural network can be used to extract features from raw data. This paper proposed a modified deep auto-encoder neural network (MDAENN). An accelerated proximal gradient (APG) method is proposed in this method. MDAENN has lower computational complexity, easier parameters tuning and better convergence than traditional neural network methods, such as RBF, in feature extraction and reconstruction. Based on the feature extraction, least squares policy iteration (LSPI) is used to design the optimal controller. When the dimension of state space is large or even continuous, value function approximation (VFA) method is used instead of value function. Experimental results show that the proposed method can successfully deal with feature extraction and learn control policies with low computational complexity.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"79 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85354021","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908978
Weiwei Bai, Liang Cao, Guowei Dong, Hongyi Li
In this paper, the adaptive reinforcement learning tracking control problem is studied for second-order pure-feedback multi-agent systems (MASs). The pure-feedback MASs are transformed into strict-feedback form by using the mean value theorem. The reinforcement learning approach is applied to handle the unknown functions and system control performance index. Moreover, the error terms are introduced to the controller, which can improve the robust of the control scheme. The theoretical analysis indicates that all the signals and tracking errors in close-loop system are semi-global uniformly ultimately bounded (SGUUB), and the numerical simulation are conducted to verify the superiority of this scheme.
{"title":"Adaptive Reinforcement Learning Tracking Control for Second-Order Multi-Agent Systems","authors":"Weiwei Bai, Liang Cao, Guowei Dong, Hongyi Li","doi":"10.1109/DDCLS.2019.8908978","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908978","url":null,"abstract":"In this paper, the adaptive reinforcement learning tracking control problem is studied for second-order pure-feedback multi-agent systems (MASs). The pure-feedback MASs are transformed into strict-feedback form by using the mean value theorem. The reinforcement learning approach is applied to handle the unknown functions and system control performance index. Moreover, the error terms are introduced to the controller, which can improve the robust of the control scheme. The theoretical analysis indicates that all the signals and tracking errors in close-loop system are semi-global uniformly ultimately bounded (SGUUB), and the numerical simulation are conducted to verify the superiority of this scheme.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"202-207"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81826783","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908938
Yu Chen, Haiyan Wu, Jing Wang
Multi-sensor based fluidized bed reactor (FBR) agglomeration monitoring system faces the problem with mismatching from different sensors. Moreover, acoustic signals are sensitive to agglomeration as well as the environment interference, so information fusion method is required to improve the stability of fault monitoring systems based on acoustic sensors. In this paper, a support vector data description (SVDD) combined with improved weighted majority voting (WMV) method is proposed for FBR. Firstly, sigmoid function is added to each SVDD model, so the Boolean outputs of SVDD are converted to probability estimations to meet the need of information fusion and improve the detection accuracy. Moreover, a multi-penalty parameter is designed to evaluate classifier in different situations, replacing the single overall penalty parameter in general WMV method. Through the penalty vector, performance of each classifier is added to the prior condition of voting. The proposed method is tested in a pilot device. From the test results, it can be concluded that the conflict handling performance of proposed method is enhanced greatly, and the decision risk is reduced. Compared with that of general method, the detection accuracy of proposed method is improved.
{"title":"Improved SVDD-WMV Method for Fluidized Bed Multi-Sensor Agglomeration Detection","authors":"Yu Chen, Haiyan Wu, Jing Wang","doi":"10.1109/DDCLS.2019.8908938","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908938","url":null,"abstract":"Multi-sensor based fluidized bed reactor (FBR) agglomeration monitoring system faces the problem with mismatching from different sensors. Moreover, acoustic signals are sensitive to agglomeration as well as the environment interference, so information fusion method is required to improve the stability of fault monitoring systems based on acoustic sensors. In this paper, a support vector data description (SVDD) combined with improved weighted majority voting (WMV) method is proposed for FBR. Firstly, sigmoid function is added to each SVDD model, so the Boolean outputs of SVDD are converted to probability estimations to meet the need of information fusion and improve the detection accuracy. Moreover, a multi-penalty parameter is designed to evaluate classifier in different situations, replacing the single overall penalty parameter in general WMV method. Through the penalty vector, performance of each classifier is added to the prior condition of voting. The proposed method is tested in a pilot device. From the test results, it can be concluded that the conflict handling performance of proposed method is enhanced greatly, and the decision risk is reduced. Compared with that of general method, the detection accuracy of proposed method is improved.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"30 1","pages":"1106-1110"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80555942","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909057
Hua Chen, Z. Xiong, Yindong Ji
In the traditional iterative learning control (ILC) for automatic train operation (ATO), control inputs are usually continuous signals. In this paper, a practical ILC is presented to carry out the train operation by discrete traction or braking force. The train motion dynamic model is described by linear time-varying perturbation model along with the reference trajectories, which can be identified by the historical data. The ILC based on the perturbation model can be easily used to the case with the continuous control signals because the updating law of the ILC can be derived theoretically. Then the proposed ILC method is extended to the case with discrete gears by transforming the ILC with discrete control signals into a well-defined mixed integer programming (MIP) problem. The proposed method has been illustrated on the simulation case. Simulation results show that the method can not only track the reference trajectories to a fine accuracy but also restrict the gear shift frequency of the operation process, which is helpful to improve the ride comfort index of the whole train operation.
{"title":"Iterative Learning Control for Automatic Train Operation with Discrete Gears","authors":"Hua Chen, Z. Xiong, Yindong Ji","doi":"10.1109/DDCLS.2019.8909057","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909057","url":null,"abstract":"In the traditional iterative learning control (ILC) for automatic train operation (ATO), control inputs are usually continuous signals. In this paper, a practical ILC is presented to carry out the train operation by discrete traction or braking force. The train motion dynamic model is described by linear time-varying perturbation model along with the reference trajectories, which can be identified by the historical data. The ILC based on the perturbation model can be easily used to the case with the continuous control signals because the updating law of the ILC can be derived theoretically. Then the proposed ILC method is extended to the case with discrete gears by transforming the ILC with discrete control signals into a well-defined mixed integer programming (MIP) problem. The proposed method has been illustrated on the simulation case. Simulation results show that the method can not only track the reference trajectories to a fine accuracy but also restrict the gear shift frequency of the operation process, which is helpful to improve the ride comfort index of the whole train operation.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"59 1","pages":"1284-1289"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80153262","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8909065
Zhen Pan, Guoyong Huang, Yugang Fan
Aiming at the problem that the vibration signal of the check valve has background noise and low fault recognition rate, a signal characteristics extraction method based on variational mode decomposition and permutation entropy was proposed. The extreme learning machine was used for fault recognition. Firstly, the check valve vibration signal was decomposed by the variational mode decomposition, and the intrinsic mode functions were obtained in different scales. Secondly, the permutation entropy of each intrinsic mode function was calculated and used to compose the multiscale feature vector. Finally, the high-dimensional feature vector was input to the extreme learning machine for check valve fault diagnosis. The comparison is made with EEMD and LCD (local characteristic-scale decomposition). The experimental results show that the method can effectively identify the fault type of the check valve.
{"title":"A Check Valve Fault Diagnosis Method Based on Variational Mode Decomposition and Permutation Entropy","authors":"Zhen Pan, Guoyong Huang, Yugang Fan","doi":"10.1109/DDCLS.2019.8909065","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8909065","url":null,"abstract":"Aiming at the problem that the vibration signal of the check valve has background noise and low fault recognition rate, a signal characteristics extraction method based on variational mode decomposition and permutation entropy was proposed. The extreme learning machine was used for fault recognition. Firstly, the check valve vibration signal was decomposed by the variational mode decomposition, and the intrinsic mode functions were obtained in different scales. Secondly, the permutation entropy of each intrinsic mode function was calculated and used to compose the multiscale feature vector. Finally, the high-dimensional feature vector was input to the extreme learning machine for check valve fault diagnosis. The comparison is made with EEMD and LCD (local characteristic-scale decomposition). The experimental results show that the method can effectively identify the fault type of the check valve.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"33 1","pages":"650-655"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81187699","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908900
Linghuan Kong, Wei He
An adaptive fuzzy finite-time control policy is developed for an uncertain $n$-link robot with input saturation and time-varying output constraints. Compared with previous works, the introduced finite-time stability criterion is used for the tracking control of the robot. Furthermore, cot-type Barrier Lyapunov functions (BLFs) are introduced for guaranteeing output constraints, which can be considered as a substitution of other BLFs. A fuzzy approximation-based adaptive finite-time control scheme is constructed for stabilizing the robotic system. With Lyapunov theory, it has been proved that all the error signals are semi-global practical finite-time stable (SGPFS). At last, the effectiveness of the proposed scheme is verified by simulation results.
{"title":"Adaptive Fuzzy Control for a Constrained Robot in the Presence of Input Nonlinearity","authors":"Linghuan Kong, Wei He","doi":"10.1109/DDCLS.2019.8908900","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908900","url":null,"abstract":"An adaptive fuzzy finite-time control policy is developed for an uncertain $n$-link robot with input saturation and time-varying output constraints. Compared with previous works, the introduced finite-time stability criterion is used for the tracking control of the robot. Furthermore, cot-type Barrier Lyapunov functions (BLFs) are introduced for guaranteeing output constraints, which can be considered as a substitution of other BLFs. A fuzzy approximation-based adaptive finite-time control scheme is constructed for stabilizing the robotic system. With Lyapunov theory, it has been proved that all the error signals are semi-global practical finite-time stable (SGPFS). At last, the effectiveness of the proposed scheme is verified by simulation results.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"7 1","pages":"185-190"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82552565","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 study develops an adaptive neural inverse optimal control method for a class of stochastic nonlinear systems. Neural networks (NN) are used to approximate the unknown nonlinear functions. The designed inverse optimal control strategy avoids the objective of solving the Hamilton-Jacobi-Bellman (HJB) equation and devises an optimal controller, which is related to the meaningful cost functional. Based on adaptive backstepping algorithm and Lyapunov stability theory, it is proved that the proposed control strategy guarantees the asymptotic stability in probability of the control systems and solves the inverse optimal problem.
{"title":"Adaptive Neural Inverse Optimal Control for a Class of Strict Feedback Stochastic Nonlinear Systems","authors":"Fengxue Cao, Tingting Yang, Yong-ming Li, Shaocheng Tong","doi":"10.1109/DDCLS.2019.8908901","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908901","url":null,"abstract":"This study develops an adaptive neural inverse optimal control method for a class of stochastic nonlinear systems. Neural networks (NN) are used to approximate the unknown nonlinear functions. The designed inverse optimal control strategy avoids the objective of solving the Hamilton-Jacobi-Bellman (HJB) equation and devises an optimal controller, which is related to the meaningful cost functional. Based on adaptive backstepping algorithm and Lyapunov stability theory, it is proved that the proposed control strategy guarantees the asymptotic stability in probability of the control systems and solves the inverse optimal problem.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"32 1","pages":"432-436"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82966968","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 : 2019-05-01DOI: 10.1109/DDCLS.2019.8908897
W. Zhou, Miao Yu
In this paper, we focus on the direct learning control method for a class of continuous-time nonlinear systems with parametric uncertainties. First, the definitions of direct learning control are introduced. Second-order internal model is used to define the structure of non-repeatable reference trajectories. Then, a direct learning control algorithm is proposed to achieve control objective without iterations. By means of historical control data, direct learning control technique operates in a direct way. In order to achieve a satisfactory tracking performance, the second-order internal model is applied and embedded into the direct learning control law. Finally, the efficacy of the proposed direct learning control algorithm is demonstrated by a single-link robotic manipulator with desired trajectory matching second-order internal model.
{"title":"Direct Learning Control of Trajectories Subject to Second-Order Internal Model for a Class of Nonlinear Systems","authors":"W. Zhou, Miao Yu","doi":"10.1109/DDCLS.2019.8908897","DOIUrl":"https://doi.org/10.1109/DDCLS.2019.8908897","url":null,"abstract":"In this paper, we focus on the direct learning control method for a class of continuous-time nonlinear systems with parametric uncertainties. First, the definitions of direct learning control are introduced. Second-order internal model is used to define the structure of non-repeatable reference trajectories. Then, a direct learning control algorithm is proposed to achieve control objective without iterations. By means of historical control data, direct learning control technique operates in a direct way. In order to achieve a satisfactory tracking performance, the second-order internal model is applied and embedded into the direct learning control law. Finally, the efficacy of the proposed direct learning control algorithm is demonstrated by a single-link robotic manipulator with desired trajectory matching second-order internal model.","PeriodicalId":6699,"journal":{"name":"2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"25 1","pages":"1269-1273"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83356614","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：