Pub Date : 2014-12-01DOI: 10.1109/CICA.2014.7013247
Li-Xin Wang
We analyze some basic properties of the stock price dynamical model when trend-followers and contrarians interplay with each other. We prove that the price dynamical model has an infinite number of equilibriums, but all these equilibriums are unstable. We demonstrate the short-term predictability of the price volatility and derive the detailed formulas of the Lyapunov exponent as functions of the model parameters. We show that although the price is chaotic, the volatility converges to some constant very quickly at the rate of the Lyapunov exponent. We extract the formula relating the converged volatility to the model parameters based on Monte-Carlo simulations.
{"title":"What happens when trend-followers and contrarians interplay in stock market","authors":"Li-Xin Wang","doi":"10.1109/CICA.2014.7013247","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013247","url":null,"abstract":"We analyze some basic properties of the stock price dynamical model when trend-followers and contrarians interplay with each other. We prove that the price dynamical model has an infinite number of equilibriums, but all these equilibriums are unstable. We demonstrate the short-term predictability of the price volatility and derive the detailed formulas of the Lyapunov exponent as functions of the model parameters. We show that although the price is chaotic, the volatility converges to some constant very quickly at the rate of the Lyapunov exponent. We extract the formula relating the converged volatility to the model parameters based on Monte-Carlo simulations.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127356953","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013249
Q. Dang, Benyamine Allouche, L. Vermeiren, A. Dequidt, M. Dambrine
The rotary inverted pendulum (RIP) is an under-actuated mechanical system. Because of its nonlinear behavior, the RIP is widely used as a benchmark in control theory to illustrate and validate new ideas in nonlinear and linear control. This paper presents a robust Takagi-Sugeno (T-S) fuzzy descriptor approach for designing a stabilizing controller for the RIP with real-time implementation. It is shown in this paper how the modeling of the physical system on descriptor T-S form with a reduced number of rules possible can lead to a simplified controller that is practically implementable. Relaxed linear matrix inequality-based stability conditions for the non quadratic case are given. Experimental results illustrate the effectiveness of the proposed approach.
{"title":"Design and implementation of a robust fuzzy controller for a rotary inverted pendulum using the Takagi-Sugeno descriptor representation","authors":"Q. Dang, Benyamine Allouche, L. Vermeiren, A. Dequidt, M. Dambrine","doi":"10.1109/CICA.2014.7013249","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013249","url":null,"abstract":"The rotary inverted pendulum (RIP) is an under-actuated mechanical system. Because of its nonlinear behavior, the RIP is widely used as a benchmark in control theory to illustrate and validate new ideas in nonlinear and linear control. This paper presents a robust Takagi-Sugeno (T-S) fuzzy descriptor approach for designing a stabilizing controller for the RIP with real-time implementation. It is shown in this paper how the modeling of the physical system on descriptor T-S form with a reduced number of rules possible can lead to a simplified controller that is practically implementable. Relaxed linear matrix inequality-based stability conditions for the non quadratic case are given. Experimental results illustrate the effectiveness of the proposed approach.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133637903","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013235
Ming Zhang
New open box and nonlinear model of Ultra High Frequency Polynomial and Sine Artificial Higher Order Neural Network (UPS-HONN) is presented in this paper. A new learning algorithm for UPS-HONN is also developed from this study. A control signal generating system, UPS-HONN Simulator, is built based on the UPS-HONN models. Test results show that, to generate any nonlinear control signal, average error of UPS-HONN models is under 1e-6.
{"title":"Ultra high frequency polynomial and sine artificial higher order neural networks for control signal generator","authors":"Ming Zhang","doi":"10.1109/CICA.2014.7013235","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013235","url":null,"abstract":"New open box and nonlinear model of Ultra High Frequency Polynomial and Sine Artificial Higher Order Neural Network (UPS-HONN) is presented in this paper. A new learning algorithm for UPS-HONN is also developed from this study. A control signal generating system, UPS-HONN Simulator, is built based on the UPS-HONN models. Test results show that, to generate any nonlinear control signal, average error of UPS-HONN models is under 1e-6.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116171500","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013236
E. Sánchez, D. Rodriguez-Castellanos
In this paper, using recurrent high order neural networks as an identification strategy for unknown pinned nodes dynamics, a new scheme for pinning control of complex networks with changing unknown coupling strengths is proposed and a robust regulation behavior on such scenario is demonstrated.
{"title":"Robust pinning control of complex dynamical networks using recurrent neural networks","authors":"E. Sánchez, D. Rodriguez-Castellanos","doi":"10.1109/CICA.2014.7013236","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013236","url":null,"abstract":"In this paper, using recurrent high order neural networks as an identification strategy for unknown pinned nodes dynamics, a new scheme for pinning control of complex networks with changing unknown coupling strengths is proposed and a robust regulation behavior on such scenario is demonstrated.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993536","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013227
M. Prasad, Kuang-Pen Chou, A. Saxena, Omprakash Kaiwartya, Dong-Lin Li, Chin-Teng Lin
This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.
{"title":"Collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers","authors":"M. Prasad, Kuang-Pen Chou, A. Saxena, Omprakash Kaiwartya, Dong-Lin Li, Chin-Teng Lin","doi":"10.1109/CICA.2014.7013227","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013227","url":null,"abstract":"This paper demonstrates a novel model for Mamdani type fuzzy inference system by using the knowledge learning ability of collaborative fuzzy clustering and rule learning capability of FCM. The collaboration process finds consistency between different datasets, these datasets can be generated at various places or same place with diverse environment containing common features space and bring together to find common features within them. For any kind of collaboration or integration of datasets, there is a need of keeping privacy and security at some level. By using collaboration process, it helps fuzzy inference system to define the accurate numbers of rules for structure learning and keeps the performance of system at satisfactory level while preserving the privacy and security of given datasets.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122401856","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013254
Yongping Pan, Haoyong Yu
This paper presents a biomimetic hybrid feedback feedforword (HFF) adaptive neural control for a class of robotic arms. The control structure includes a proportional-derivative feedback term and an adaptive neural network (NN) feedforword term, which mimics the human motor learning and control mechanism. Semiglobal asymptotic stability of the closed-loop system is established by the Lyapunov synthesis. The major difference of the proposed design from the traditional feedback adaptive approximation-based control (AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as NN inputs. Such a slight difference leads to several attractive properties, including the convenient NN design, the decrease of the number of NN inputs, and semiglobal asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach has two unique features: 1) all above attractive properties are achieved by a much simpler control scheme; 2) the bounds of plant uncertainties are not required to be known. Simulation results have verified the effectiveness and superiority of this approach.
{"title":"Biomimetic hybrid feedback feedforword adaptive neural control of robotic arms","authors":"Yongping Pan, Haoyong Yu","doi":"10.1109/CICA.2014.7013254","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013254","url":null,"abstract":"This paper presents a biomimetic hybrid feedback feedforword (HFF) adaptive neural control for a class of robotic arms. The control structure includes a proportional-derivative feedback term and an adaptive neural network (NN) feedforword term, which mimics the human motor learning and control mechanism. Semiglobal asymptotic stability of the closed-loop system is established by the Lyapunov synthesis. The major difference of the proposed design from the traditional feedback adaptive approximation-based control (AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as NN inputs. Such a slight difference leads to several attractive properties, including the convenient NN design, the decrease of the number of NN inputs, and semiglobal asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach has two unique features: 1) all above attractive properties are achieved by a much simpler control scheme; 2) the bounds of plant uncertainties are not required to be known. Simulation results have verified the effectiveness and superiority of this approach.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123997114","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013229
Alisson Marques da Silva, W. Caminhas, A. Lemos, F. Gomide
This paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. The results suggest eNFN as a potential candidate to model complex, fast time-varying dynamic systems in real-time.
{"title":"Real-time nonlinear modeling of a twin rotor MIMO system using evolving neuro-fuzzy network","authors":"Alisson Marques da Silva, W. Caminhas, A. Lemos, F. Gomide","doi":"10.1109/CICA.2014.7013229","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013229","url":null,"abstract":"This paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. The results suggest eNFN as a potential candidate to model complex, fast time-varying dynamic systems in real-time.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"423 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126712472","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013232
Jin Huang, Jiaguang Sun, Xibin Zhao, M. Gu
A novel approach for optimal robust control of a class of generalized fuzzy dynamical systems is proposed. This is a novel use of fuzzy uncertainty in doing dynamical system control. The system may have nonlinear nominal terms and the other terms with uncertainty, including unknown parameters and input disturbances. The Fuzzy sets theory is creatively employed in presenting the system parameter and input uncertainty, and then the control structure is deterministic (versus if-then rule-based as is typical in Mamdani-type fuzzy control). The desired controlled system performance is also deterministic, with guaranteed performances of uniform boundedness and uniform ultimate boundedness. Fuzzy informations on the uncertainties are used in searching optimal control gain under a proposed LQG-like quadratic cost index. The control gain design problem is formulated as a constrained optimization problem with the solution be proved to be always existed and unique. Systematic procedure is summarized for such control design.
{"title":"Optimal robust control for generalized fuzzy dynamical systems: A novel use on fuzzy uncertainties","authors":"Jin Huang, Jiaguang Sun, Xibin Zhao, M. Gu","doi":"10.1109/CICA.2014.7013232","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013232","url":null,"abstract":"A novel approach for optimal robust control of a class of generalized fuzzy dynamical systems is proposed. This is a novel use of fuzzy uncertainty in doing dynamical system control. The system may have nonlinear nominal terms and the other terms with uncertainty, including unknown parameters and input disturbances. The Fuzzy sets theory is creatively employed in presenting the system parameter and input uncertainty, and then the control structure is deterministic (versus if-then rule-based as is typical in Mamdani-type fuzzy control). The desired controlled system performance is also deterministic, with guaranteed performances of uniform boundedness and uniform ultimate boundedness. Fuzzy informations on the uncertainties are used in searching optimal control gain under a proposed LQG-like quadratic cost index. The control gain design problem is formulated as a constrained optimization problem with the solution be proved to be always existed and unique. Systematic procedure is summarized for such control design.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126378863","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013245
Jorge C. Romero-Aragon, E. Sánchez, A. Alanis
In this paper, the field programmable gate array (FPGA) implementation of a discrete-time inverse neural optimal control for trajectory tracking is proposed to regulate glucose level for type 1 diabetes mellitus (T1DM) patients. For this controller, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to calculate the insulin delivery rate, which prevents hyperglycemia and hypoglycemia levels in T1DM patients. Besides this control law minimizes a cost functional. The neural model is obtained from an on-line neural identifier, which uses a recurrent high-order neural network (RHONN), trained with an extended Kalman filter (EKF). A virtual patient is implemented on a PC host computer, which is interconnected with the FPGA controller. This controller constitutes a step forward to develop an autonomous artificial pancreas.
{"title":"Glucose level regulation for diabetes mellitus type 1 patients using FPGA neural inverse optimal control","authors":"Jorge C. Romero-Aragon, E. Sánchez, A. Alanis","doi":"10.1109/CICA.2014.7013245","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013245","url":null,"abstract":"In this paper, the field programmable gate array (FPGA) implementation of a discrete-time inverse neural optimal control for trajectory tracking is proposed to regulate glucose level for type 1 diabetes mellitus (T1DM) patients. For this controller, a control Lyapunov function (CLF) is proposed to obtain an inverse optimal control law in order to calculate the insulin delivery rate, which prevents hyperglycemia and hypoglycemia levels in T1DM patients. Besides this control law minimizes a cost functional. The neural model is obtained from an on-line neural identifier, which uses a recurrent high-order neural network (RHONN), trained with an extended Kalman filter (EKF). A virtual patient is implemented on a PC host computer, which is interconnected with the FPGA controller. This controller constitutes a step forward to develop an autonomous artificial pancreas.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125260574","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 : 2014-12-01DOI: 10.1109/CICA.2014.7013243
Haopeng Zhang, Qing Hui
Mixed-binary nonlinear programming (MBNP), which can be used to optimize network structure and network parameters simultaneously, has been seen widely in applications of cyber-physical network systems. However, it is quite challenging to develop efficient algorithms to solve it practically. On the other hand, swarm intelligence based optimization algorithms can simulate the cooperation and interaction behaviors from social or nature phenomena to solve complex, nonconvex nonlinear problems with high efficiency. Hence, motivated by this observation, we propose a class of new computationally efficient algorithms called coupled spring forced multiagent coordination optimization (CSFMCO), by exploiting the chaos-like behavior of two-mass two-spring mechanical systems to improve the ability of algorithmic exploration and thus to fast solve the MBNP problem. Together with the continuous version of CSFMCO, a binary version of CSFMCO and a switching version between continuous and binary versions are presented. Moreover, to numerically illustrate our proposed algorithms, a formation control problem and resource allocation problem for cyber-physical networks are investigated by using the proposed algorithms.
{"title":"New multiagent coordination optimization algorithms for mixed-binary nonlinear programming with control applications","authors":"Haopeng Zhang, Qing Hui","doi":"10.1109/CICA.2014.7013243","DOIUrl":"https://doi.org/10.1109/CICA.2014.7013243","url":null,"abstract":"Mixed-binary nonlinear programming (MBNP), which can be used to optimize network structure and network parameters simultaneously, has been seen widely in applications of cyber-physical network systems. However, it is quite challenging to develop efficient algorithms to solve it practically. On the other hand, swarm intelligence based optimization algorithms can simulate the cooperation and interaction behaviors from social or nature phenomena to solve complex, nonconvex nonlinear problems with high efficiency. Hence, motivated by this observation, we propose a class of new computationally efficient algorithms called coupled spring forced multiagent coordination optimization (CSFMCO), by exploiting the chaos-like behavior of two-mass two-spring mechanical systems to improve the ability of algorithmic exploration and thus to fast solve the MBNP problem. Together with the continuous version of CSFMCO, a binary version of CSFMCO and a switching version between continuous and binary versions are presented. Moreover, to numerically illustrate our proposed algorithms, a formation control problem and resource allocation problem for cyber-physical networks are investigated by using the proposed algorithms.","PeriodicalId":340740,"journal":{"name":"2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115967057","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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