The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform

Kaoru Hirota - One of the best experts on this subject based on the ideXlab platform.

  • Anti-swing and positioning control of overhead traveling crane
    Information Sciences, 2003
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A new fuzzy controller for anti-swing and position control of an overhead traveling crane is proposed based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as the Input items, and the trolley acceleration as the output item. Each Input item is given with a SIRM and a dynamic importance degree. The control system is proved to be asymptotically stable to the destination. The controller is robust to different rope lengths and has generalization ability for different initial positions. Control simulation results show that by using the fuzzy controller, the crane is smoothly driven to the destination in short time with small swing angle and almost no overshoot.

  • A proposal of SIRMs dynamically connected fuzzy inference model for plural Input fuzzy control
    Fuzzy Sets and Systems, 2002
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    Single Input Rule modules (SIRMs) dynamically connected fuzzy inference model is proposed for plural Input fuzzy control. For each Input item, a SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the Input item through a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can be easily tuned based on the local information of current state. The model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each SIRM. The controller constructing method for constant value control systems is given, and constant value controls of typical first- and second-order lag plants are tested. The simulation results show that by using the proposed mode, the reaching time can be reduced by more than 15% without any steady-state error, overshoot, or vibration compared with the SIRMs fixed importance degree connected fuzzy inference model. The proposed model is further successfully applied to stabilization control of an inverted pendulum system including the position control of the cart.

  • A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2001
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as Input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy Rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].

  • Stabilization Controlof BallandBeam Syatems
    2001
    Co-Authors: Kaoru Hirota, Naoyoshi Yubazaki, Saga Hirosawa
    Abstract:

    A new fuzzy controller for stabilization control of ball and beam systems is proposed based on the SIRMs (Single Input Rule Modules) dynamically connected fuzzy mference model. The fuzzy controller deals with four Input items. Each Input item has a SIRM and a dynamic importance degree. By using the SIN and the dynamic importance degrees, the fuzzy controller switches automatically the ball position control and the beam angular control to control situations. control simulation results show the effectiveness of the fuzzy controller.

  • Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2000
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three Input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the Input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.

Naoyoshi Yubazaki - One of the best experts on this subject based on the ideXlab platform.

  • Anti-swing and positioning control of overhead traveling crane
    Information Sciences, 2003
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A new fuzzy controller for anti-swing and position control of an overhead traveling crane is proposed based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model. The trolley position and velocity, the rope swing angle and angular velocity are selected as the Input items, and the trolley acceleration as the output item. Each Input item is given with a SIRM and a dynamic importance degree. The control system is proved to be asymptotically stable to the destination. The controller is robust to different rope lengths and has generalization ability for different initial positions. Control simulation results show that by using the fuzzy controller, the crane is smoothly driven to the destination in short time with small swing angle and almost no overshoot.

  • A proposal of SIRMs dynamically connected fuzzy inference model for plural Input fuzzy control
    Fuzzy Sets and Systems, 2002
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    Single Input Rule modules (SIRMs) dynamically connected fuzzy inference model is proposed for plural Input fuzzy control. For each Input item, a SIRM is constructed and a dynamic importance degree is defined. The dynamic importance degree consists of a base value insuring the role of the Input item through a control process, and a dynamic value changing with control situations to adjust the dynamic importance degree. Each dynamic value can be easily tuned based on the local information of current state. The model output is obtained by summarizing the products of the dynamic importance degree and the fuzzy inference result of each SIRM. The controller constructing method for constant value control systems is given, and constant value controls of typical first- and second-order lag plants are tested. The simulation results show that by using the proposed mode, the reaching time can be reduced by more than 15% without any steady-state error, overshoot, or vibration compared with the SIRMs fixed importance degree connected fuzzy inference model. The proposed model is further successfully applied to stabilization control of an inverted pendulum system including the position control of the cart.

  • A New Fuzzy Controller for Stabilizing Inverted Pendulums Based on Single Input Rule Modules Dynamically Connected Fuzzy Inference Model
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2001
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A fuzzy controller is presented based on the Single Input Rule Modules (SIRMs) dynamically connected fuzzy inference model for stabilization control of inverted pendulums. The angle and angular velocity of the pendulum and the position and velocity of the cart are selected as Input items and the driving force as the output item. By using SIRMs and dynamic importance degrees, the fuzzy controller realizes angular control of the pendulum and position control of the cart in parallel with totally only 24 fuzzy Rules. Switching between angular control of the pendulum and position control of the cart is smoothly performed by automatically adjusting dynamic importance degrees according to control situations. For any inverted pendulums, of which the pendulum length is among [0.5m, 2.2m], simulation results show that the proposed fuzzy controller has a high generalization ability to stabilize the pendulum systems completely in about 6.0 seconds when the initial angle of the pendulum is among [-30.0°, +30.0°], or the initial position of the cart is among [-2.1m, +2.1m].

  • Stabilization Controlof BallandBeam Syatems
    2001
    Co-Authors: Kaoru Hirota, Naoyoshi Yubazaki, Saga Hirosawa
    Abstract:

    A new fuzzy controller for stabilization control of ball and beam systems is proposed based on the SIRMs (Single Input Rule Modules) dynamically connected fuzzy mference model. The fuzzy controller deals with four Input items. Each Input item has a SIRM and a dynamic importance degree. By using the SIN and the dynamic importance degrees, the fuzzy controller switches automatically the ball position control and the beam angular control to control situations. control simulation results show the effectiveness of the fuzzy controller.

  • Trajectory Tracking Control of Unconstrained Object Using the SIRMs Dynamically Connected Fuzzy Inference Model
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2000
    Co-Authors: Naoyoshi Yubazaki, Kaoru Hirota
    Abstract:

    A trajectory tracking experiment system taking an unconstrained table-tennis ball as the control object is constructed, and a fuzzy controller based on the SIRMs dynamically connected fuzzy inference model is proposed. For each of the three Input items of the fuzzy controller, a SIRM (Single Input Rule Module) is established and an importance degree is defined. Especially for the Input item corresponding to ball velocity, its importance degree is tuned dynamically according to moving conditions. The summation of the products of the importance degree and the fuzzy inference result of the SIRMs is calculated to control the angles of a table, making the ball on the table move along a desired trajectory. A virtual spiral asymptotic trajectory is also introduced to give the object an adequate desired position at each sampling time. Tracking experiment results for three kinds of circles and one kind of ellipses show that in more than 80% of the experiments performed under the SIRMs dynamically connected fuzzy inference model, the maximum tracking error is smaller than 0.05m and the unevenness of the sampling steps necessary for each round is very small. Compared with conventional fuzzy controller, the SIRMs dynamically connected fuzzy inference model is proved to be effective in tracking control of unconstrained objects.

Masaharu Mizumoto - One of the best experts on this subject based on the ideXlab platform.

  • SIRMs connected fuzzy inference method adopting emphasis and suppression
    Fuzzy Sets and Systems, 2013
    Co-Authors: Hirosato Seki, Masaharu Mizumoto
    Abstract:

    The single Input Rule modules connected fuzzy inference method (SIRMs method) can decrease the number of fuzzy Rules drastically in comparison with the conventional fuzzy inference methods. However, the inference results obtained by the SIRMs method is generally simple compared with those of the conventional fuzzy inference methods. For example, the SIRMs method may be not equivalent to the product-sum-gravity method and fuzzy singleton-type inference method, if the fuzzy sets of the antecedent parts are limited to normal fuzzy sets. In this paper, we propose a fuzzy singleton-type SIRMs method, which weights the Rules of the SIRMs method, in order to solve the above problem. This paper also clarifies the property of the fuzzy singleton-type SIRMs method, from the view point of equivalence and monotonicity. Moreover, the fuzzy singleton-type SIRMs method is shown to be superior to the conventional SIRMs method by applying to a medical diagnosis system.

  • SMC - On the properties of SIRMs connected fuzzy inference method with consequent fuzzy sets
    2012 IEEE International Conference on Systems Man and Cybernetics (SMC), 2012
    Co-Authors: Hirosato Seki, Masaharu Mizumoto
    Abstract:

    Yi et al. have proposed a Single Input Rule Modules connected fuzzy inference method with consequent fuzzy sets (SIRMs method with CFS), in which the consequent parts are extended to fuzzy sets from real numbers. This model is applied to the various control, and obtained good results. This paper first shows that the SIRMs method with CFS can be easily obtained from the center of graviy and area of fuzzy sets in consequent parts. Second, it clarifies the property of the SIRMs method with CFS, from the point of view of monotonicity. Moreover, the SIRMs method with CFS is shown to be superior to the conventional SIRMs method by applying to a medical diagnosis.

  • SMC - On the equivalence of single Input type fuzzy inference methods
    2009 IEEE International Conference on Systems Man and Cybernetics, 2009
    Co-Authors: Hirosato Seki, Masaharu Mizumoto
    Abstract:

    This paper addresses equivalence of fuzzy inference methods. It first presents single Input type fuzzy inference methods: the single Input Rule modules connected type fuzzy inference method (SIRMs method) and single Input connected fuzzy inference method (SIC method). Secondly, the equivalence conditions of the SIRMs method and SIC method are shown. Finally, this paper also discusses the equivalence conditions between the single Input type fuzzy inference methods and the conventional fuzzy inference methods like the simplified fuzzy inference method, product-sum-gravity method and fuzzy singleton-type inference method which are all widely used as fuzzy control methods.

  • An Extended Method of SIRMs Connected Fuzzy Inference Method Using Kernel Method
    IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2009
    Co-Authors: Hirosato Seki, Hiroaki Ishii, F. Mizuguchi, Satoshi Watanabe, Masaharu Mizumoto
    Abstract:

    The single Input Rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy Rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional-type SIRMs method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs methods can not be applied to XOR (Exclusive OR). In this paper, we propose a “kernel-type SIRMs method” which uses the kernel trick to the SIRMs method, and show that this method can treat XOR. Further, a learning algorithm of the proposed SIRMs method is derived by using the steepest descent method, and compared with the one of conventional SIRMs method and kernel perceptron by applying to identification of nonlinear functions, medical diagnostic system and discriminant analysis of Iris data.

  • IEA/AIE - Realization of XOR by SIRMs Connected Fuzzy Inference Method
    Next-Generation Applied Intelligence, 2009
    Co-Authors: Hirosato Seki, Hiroaki Ishii, Satoshi Watanabe, Masaharu Mizumoto
    Abstract:

    The single Input Rule modules connected fuzzy inference method (SIRMs method) by Yubazaki et al. can decrease the number of fuzzy Rules drastically in comparison with the conventional fuzzy inference methods. Moreover, Seki et al. have proposed a functional type single Input Rule modules connected fuzzy inference method which generalizes the consequent part of the SIRMs method to function. However, these SIRMs method can not realize XOR (Exclusive OR). In this paper, we propose a "neural network-type SIRMs method" which unites the neural network and SIRMs method, and show that this method can realize XOR. Further, a learning algorithm of the proposed SIRMs method is derived by steepest descent method, and is shown to be superior to the conventional SIRMs method and neural network by applying to identification of nonlinear functions.

Hirosato Seki - One of the best experts on this subject based on the ideXlab platform.

  • Optimization of Constrained SIRMs Connected Type Fuzzy Inference Model Using Two-Phase Simplex Method
    Journal of Advanced Computational Intelligence and Intelligent Informatics, 2018
    Co-Authors: Takeshi Nagata, Hirosato Seki, Hiroaki Ishii
    Abstract:

    Single Input Rule Modules connected fuzzy inference model (SIRMs model, for short) by Yubazaki et al. can decrease the number of fuzzy Rules drastically in comparison with the conventional fuzzy inference models. However, it is difficult to understand the meaning of the weight for the SIRMs model because the value of the weight has no restriction in the learning Rules. Therefore, the paper proposes a constrained SIRMs model in which the weights are in [0,1] by using two-phase simplex method. Moreover, it shows that the applicability of the proposed model by applying it to a medical diagnosis.

  • GrC - SIRMs connected fuzzy inference model with compatibility functions.
    2014 IEEE International Conference on Granular Computing (GrC), 2014
    Co-Authors: Hirosato Seki
    Abstract:

    The single Input Rule modules connected fuzzy inference model (SIRMs model) can decrease the number of fuzzy Rules drastically in comparison with the conventional fuzzy inference models. However, the inference results obtained by the SIRMs model is generally simple comapred with the conventional fuzzy inference models. For example, the SIRMs model can not transform to the product-sum-gravity model, if the fuzzy sets of the antecedent parts are limited to normal fuzzy sets. In this paper, we propose a SIRMs model with compatibility functions, which weights the Rules of the SIRMs model. Moreover, this paper shows that the inference results of the proposed model can be easily obtained even as the proposed model uses involved compatibility functions.

  • FUZZ-IEEE - Medical diagnosis and monotonicity clarification using SIRMs connected fuzzy inference model with functional weights
    2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014
    Co-Authors: Hirosato Seki, Tomoharu Nakashima
    Abstract:

    This paper discusses the SIRMs (Single-Input Rule Modules) connected fuzzy inference model with functional weights (SIRMs model with FW). The SIRMs model with FW consists of a number of groups of simple fuzzy if-then Rules with only a single attribute in the antecedent part. The final outputs of conventional SIRMs model are obtained by summarizing product of the functional weight and inference result from a Rule module. In the SIRMs model of the paper, we firstly clarify its monotonicity. Secondly, we apply the SIRMs model with FW to medical diagnosis.

  • SIRMs connected fuzzy inference method adopting emphasis and suppression
    Fuzzy Sets and Systems, 2013
    Co-Authors: Hirosato Seki, Masaharu Mizumoto
    Abstract:

    The single Input Rule modules connected fuzzy inference method (SIRMs method) can decrease the number of fuzzy Rules drastically in comparison with the conventional fuzzy inference methods. However, the inference results obtained by the SIRMs method is generally simple compared with those of the conventional fuzzy inference methods. For example, the SIRMs method may be not equivalent to the product-sum-gravity method and fuzzy singleton-type inference method, if the fuzzy sets of the antecedent parts are limited to normal fuzzy sets. In this paper, we propose a fuzzy singleton-type SIRMs method, which weights the Rules of the SIRMs method, in order to solve the above problem. This paper also clarifies the property of the fuzzy singleton-type SIRMs method, from the view point of equivalence and monotonicity. Moreover, the fuzzy singleton-type SIRMs method is shown to be superior to the conventional SIRMs method by applying to a medical diagnosis system.

  • Nonlinear function approximation using fuzzy functional SIRMs inference model
    2012
    Co-Authors: Hirosato Seki
    Abstract:

    Since the single Input Rule modules connected fuzzy inference model (SIRMs model) is proposed by Yubazaki, Yi et al., many researches on the extension of the SIRMs model have been reported. Moreover, the fuzzy functional SIRMs inference model, in which the consequent parts of the functional-type SIRMs model are generalized to fuzzy function, has proposed as one of various extension SIRMs models. In this paper, we apply the fuzzy functional SIRMs inference model to identification of nonrinear funcions, and show the applicability of the model.

Toshio Yoshimura - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive fuzzy dynamic surface control for a class of stochastic MIMO discrete-time nonlinear pure-feedback systems with full state constraints
    International Journal of Systems Science, 2018
    Co-Authors: Toshio Yoshimura
    Abstract:

    ABSTRACTThis paper presents the design of an adaptive fuzzy dynamic surface control for a class of stochastic MIMO discrete-time nonlinear pure-feedback systems with full state constraints using a set of noisy measurements. The design approach is described as follows. The nonlinear uncertainty is approximated by using the fuzzy logic system at the first stage, secondly the proposed adaptive fuzzy dynamic surface control is designed based on a new saturation function for full state constraints, thirdly the number of the adjustable parameters is reduced by using the simplified extended single Input Rule modules, and finally the simplified weighted least squares estimator is in a simplified structure designed to take the estimates for the un-measurable states and the adjustable parameters. The simulation provides that the proposed approach is effective for the improvement of the system performance.

  • Simplified algorithm of an adaptive fuzzy backstepping control for MIMO uncertain discrete-time nonlinear systems using a set of noisy measurements
    International Journal of Modelling Identification and Control, 2017
    Co-Authors: Toshio Yoshimura
    Abstract:

    This paper presents a simplified algorithm of an adaptive fuzzy backstepping control (AFBC) for multi-Input multi-output uncertain discrete-time nonlinear systems with uncertainties viewed as the modelling errors and the unknown external disturbances, and the observation of the states is taken with measurement noises. The simplified algorithm of the proposed AFBC is designed as follows. The explosion of complexity problem due to repeated computation of nonlinear functions is removed to derive the simplified algorithm at the first stage, secondly the number of the adjustable parameters is reduced by using the fuzzy inference approach based on the proposed simplified extended single Input Rule modules, and finally the simplified weighted least squares estimator is constructed by reducing the computational burden of the estimation for the un-measurable states and the adjustable parameters. The effectiveness of the proposed approach is indicated through the simulation experiment of a simple numerical system.

  • Direct adaptive fuzzy backstepping control for uncertain discrete-time nonlinear systems using noisy measurements
    International Journal of Systems Science, 2016
    Co-Authors: Toshio Yoshimura
    Abstract:

    ABSTRACTThis paper presents a direct adaptive fuzzy backstepping control (AFBC) for multi-Input multi-output uncertain discrete-time nonlinear systems. It is assumed that the systems are described by a discrete-time state equation with uncertainties to be viewed as the modelling errors and the unknown external disturbances, and the observation of the states is taken with independent measurement noises. The proposed direct AFBC is presented as follows. The proposed direct AFBC is assumed to be the fuzzy logic system by removing the explosion of complexity problem due to repeated computation of nonlinear functions at the first stage. Second, the number of the adjustable parameters is reduced by the fuzzy inference approach based on the extended single Input Rule modules. Third, the simplified weighted least squares estimator is constructed by reducing the computational burden of the estimation for the unmeasurable states and the adjustable parameters. The effectiveness of the proposed direct AFBC is illustr...

  • Design of a simplified adaptive fuzzy backstepping control for uncertain discrete-time nonlinear systems
    International Journal of Systems Science, 2014
    Co-Authors: Toshio Yoshimura
    Abstract:

    This paper presents a simplified adaptive fuzzy backstepping control for uncertain discrete-time nonlinear systems. It is assumed that the systems are described by a discrete-time equation with nonlinear uncertainties to be viewed as the modelling errors and the unknown external disturbances, and the states are observed with measurement noises. To design the simplified adaptive fuzzy backstepping control, the modelling errors are approximated by using the fuzzy inference approach based on the extended single-Input Rule modules, and the estimates for the unmeasurable states and the adjustable parameters are derived by using the weighted and its simplified weighted least squares estimators. It is proved that the states are ultimately bounded, and the estimation errors remain in the vicinity of zero. The effectiveness of the proposed approach is indicated through the simulation experiment of a simple numerical system.

  • Construction of an active suspension for one-wheel car models using fuzzy reasoning and dual dynamic vibration absorbers
    International Journal of Vehicle Autonomous Systems, 2008
    Co-Authors: Toshio Yoshimura, Shingo Miyai, Masao Kurimoto
    Abstract:

    This paper is concerned with the construction of an active suspension for one-wheel car models using fuzzy reasoning and dual dynamic vibration absorbers. The one-wheel car model with the dual dynamic vibration absorbers attached to the wheel part is approximately described by a non-linear system with four degrees of freedom subject to excitation from a road profile. The main purpose of the dual dynamic vibration absorbers is to improve the road holding of the wheel over a wide range of frequencies. The active control is determined by fuzzy reasoning based on the single-Input Rule modules because the car model is expressed as a complicated system and the control force is constructed by actuating the pneumatic actuator. The experimental result indicates that the proposed active suspension is significantly effective for the road holding of the wheel and also for the vibration suppression of the car body and the wheel part.