The Experts below are selected from a list of 90963 Experts worldwide ranked by ideXlab platform
Petr Dostál - One of the best experts on this subject based on the ideXlab platform.
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Control of Concentration inside CSTR Using Nonlinear Adaptive Controller
Nostradamus 2014: Prediction Modeling and Analysis of Complex Systems, 2014Co-Authors: Jiri Vojtesek, Petr DostálAbstract:An adaptive Nonlinear control is modification of the classic adaptive control where the controller is divided into the dynamic linear Part and the static Nonlinear Part. The dynamic linear Part is constructed with the use of polynomial synthesis together with the pole-placement method and the spectral factorization. The static Nonlinear Part uses static analysis of the controlled plant for introducing the mathematical Nonlinear description of the relation between the controlled output and the change of the control input. In this case, the output response could be tuned by the change of the closed-loop pole. The verification of the proposed control strategy was made by simulations on the mathematical model of CSTR with cooling in the jacket as a typical Nonlinear system.
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Simulation of Nonlinear adaptive control of a continuous stirred tank reactor
2011Co-Authors: Petr Dostál, Vladimir BobalAbstract:The paper presents design and simulation results of Nonlinear adaptive control of a chemical reactor. The control strategy appear from factorization of the controller on an adaptive dynamic linear Part and a static Nonlinear Part. The static Nonlinear Part is derived on the basis of simulated steady-state characteristics of the process and its subsequent inversion and approximation. The linear Part consisting of two linear feedback controllers results from an approximation of Nonlinear elements in the control system by an external linear model with recursively estimated parameters. The control law is derived via the polynomial approach and the pole placement method.
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Nonlinear adaptive control of a chemical reactor
2011 19th Mediterranean Conference on Control & Automation (MED), 2011Co-Authors: Petr Dostál, Vladimir Bobal, Marek Kubalcik, Jiri VojtesekAbstract:The paper deals with continuous-time Nonlinear adaptive control of a continuous stirred tank reactor. The control strategy is based on an application of the controller consisting of a linear and Nonlinear Part. The static Nonlinear Part is derived in the way of an inversion and exponential approximation of measured or simulated input-output data. The design of the dynamic linear Part is based on approximation of Nonlinear elements in the control loop by a continuous-time external linear model with directly estimated parameters. In the control design procedure, the polynomial approach with the pole assignment method is used. The Nonlinear adaptive control is tested by simulations on the Nonlinear model of the CSTR with a consecutive exothermic reaction.
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Adaptive Nonlinear control of a continuous stirred tank reactor
Chemical Papers, 2011Co-Authors: Petr Dostál, Monika Bakošová, Jiří Vojtěšek, Vladimir BobalAbstract:The paper deals with continuous-time Nonlinear adaptive control of a continuous stirred tank reactor (CSTR). Control strategy is based on the application of a controller consisting of a linear and a Nonlinear Part. The static Nonlinear Part is derived as an inversion and exponential approximation of measured or simulated input-output data. Design of the dynamic linear Part is based on an approximation of Nonlinear elements in the control loop by a continuous-time external linear model with directly estimated parameters. In the control design procedure, polynomial approach with the pole assignment method was used. The Nonlinear adaptive control was tested by simulations on a Nonlinear model of a CSTR with a consecutive exothermic reaction.
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ALCOSP - Adaptive Nonlinear Control of a Continuous Stirred Tank Reactor
IFAC Proceedings Volumes, 2010Co-Authors: Petr Dostál, Vladimir Bobal, J. VojtêŝekAbstract:Abstract The paper deals with continuous-time Nonlinear adaptive control of a continuous stirred tank reactor. The control strategy is based on an application of the controller consisting of a linear and Nonlinear Part. The static Nonlinear Part is derived in the way of a inversion and consecutive polynomial approximation of a measured or simulated input-output data. The design of the dynamic linear Part is based on approximation of Nonlinear elements in the control loop by a continuous-time external linear model with parameters estimated using a corresponding delta model. In the control design procedure, the polynomial approach with the pole assignment method is used. The Nonlinear adaptive control is tested by simulations on the Nonlinear model of the CSTR with a consecutive exothermic reaction.
Vladimir Bobal - One of the best experts on this subject based on the ideXlab platform.
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Simulation of Nonlinear adaptive control of a continuous stirred tank reactor
2011Co-Authors: Petr Dostál, Vladimir BobalAbstract:The paper presents design and simulation results of Nonlinear adaptive control of a chemical reactor. The control strategy appear from factorization of the controller on an adaptive dynamic linear Part and a static Nonlinear Part. The static Nonlinear Part is derived on the basis of simulated steady-state characteristics of the process and its subsequent inversion and approximation. The linear Part consisting of two linear feedback controllers results from an approximation of Nonlinear elements in the control system by an external linear model with recursively estimated parameters. The control law is derived via the polynomial approach and the pole placement method.
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Nonlinear adaptive control of a chemical reactor
2011 19th Mediterranean Conference on Control & Automation (MED), 2011Co-Authors: Petr Dostál, Vladimir Bobal, Marek Kubalcik, Jiri VojtesekAbstract:The paper deals with continuous-time Nonlinear adaptive control of a continuous stirred tank reactor. The control strategy is based on an application of the controller consisting of a linear and Nonlinear Part. The static Nonlinear Part is derived in the way of an inversion and exponential approximation of measured or simulated input-output data. The design of the dynamic linear Part is based on approximation of Nonlinear elements in the control loop by a continuous-time external linear model with directly estimated parameters. In the control design procedure, the polynomial approach with the pole assignment method is used. The Nonlinear adaptive control is tested by simulations on the Nonlinear model of the CSTR with a consecutive exothermic reaction.
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Adaptive Nonlinear control of a continuous stirred tank reactor
Chemical Papers, 2011Co-Authors: Petr Dostál, Monika Bakošová, Jiří Vojtěšek, Vladimir BobalAbstract:The paper deals with continuous-time Nonlinear adaptive control of a continuous stirred tank reactor (CSTR). Control strategy is based on the application of a controller consisting of a linear and a Nonlinear Part. The static Nonlinear Part is derived as an inversion and exponential approximation of measured or simulated input-output data. Design of the dynamic linear Part is based on an approximation of Nonlinear elements in the control loop by a continuous-time external linear model with directly estimated parameters. In the control design procedure, polynomial approach with the pole assignment method was used. The Nonlinear adaptive control was tested by simulations on a Nonlinear model of a CSTR with a consecutive exothermic reaction.
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ALCOSP - Adaptive Nonlinear Control of a Continuous Stirred Tank Reactor
IFAC Proceedings Volumes, 2010Co-Authors: Petr Dostál, Vladimir Bobal, J. VojtêŝekAbstract:Abstract The paper deals with continuous-time Nonlinear adaptive control of a continuous stirred tank reactor. The control strategy is based on an application of the controller consisting of a linear and Nonlinear Part. The static Nonlinear Part is derived in the way of a inversion and consecutive polynomial approximation of a measured or simulated input-output data. The design of the dynamic linear Part is based on approximation of Nonlinear elements in the control loop by a continuous-time external linear model with parameters estimated using a corresponding delta model. In the control design procedure, the polynomial approach with the pole assignment method is used. The Nonlinear adaptive control is tested by simulations on the Nonlinear model of the CSTR with a consecutive exothermic reaction.
Li Jia - One of the best experts on this subject based on the ideXlab platform.
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Parameter estimation of neuro-fuzzy based Hammerstein controlled autoregressive autoregressive moving average systems
2020 Chinese Automation Congress (CAC), 2020Co-Authors: Bensheng Lyu, Li JiaAbstract:This paper presents a parameter estimation scheme for neuro-fuzzy based Hammerstein controlled autoregressive autoregressive moving average (CARARMA) systems. The dynamic linear Part and the static Nonlinear Part can be decoupled with a special input signal. By the correlation analysis method, we can get parameter estimates of the linear Part. Moreover, the unmeasurable variables are replaced by their estimates, and the recursive generalized extended least squares algorithm can be applied to identify parameters for the noise model and Nonlinear Part. The validity of the developed method is proved by an example.
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Parameter estimation of Hammerstein–Wiener Nonlinear system with noise using special test signals
Neurocomputing, 2019Co-Authors: Li JiaAbstract:Abstract In this paper, an identification procedure of Hammerstein–Wiener Nonlinear system with process noise using special test signals is presented. Special test signals that contain separable signal and uniformly random multi-step signal are employed to separate the identification problems of the linear dynamic Part and the output static Nonlinear Part from that of the input static Nonlinear Part, and then correlation analysis method is applied to estimate the parameters of the output static Nonlinear Part and linear dynamic Part. Moreover, a filter is embedded to form extended Hammerstein–Wiener system to calculate the noise correlation functions by the information of zeros and poles of the extended system, further an error compensation term which consists of the noise correlation functions is added to least square estimation to compensate the error caused by process noise. Therefore, the unbiased estimation of model parameters can be derived by error compensation based recursive least square method. Simulation results demonstrate that proposed approach can effectively identify parameters of Hammerstein–Wiener Nonlinear system in the presence of process noise.
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Neuro-fuzzy based identification method for Hammerstein output error model with colored noise
Neurocomputing, 2017Co-Authors: Li Jia, Daogang Peng, Chao HanAbstract:The proposed method is suitable for the identification problem of the Nonlinear Part separated from that of linear Part.The proposed method can effectively compensate the bias caused by colored noise because of considering the correlation of noise at different times.The proposed method has high identification accuracy and good robustness in the presence of the disturbance of colored noise. In this paper, a neuro-fuzzy based identification procedure for Hammerstein output error model with colored noise is presented. Separable signal is used to realize the decoupling of the identification of dynamic linear Part from that of static Nonlinear Part, and then correlation analysis method is adopted to identify the parameters of the linear Part. Next, a filter is embedded to form extended Hammerstein model to calculate the noise correlation function by the information of zeros and poles of the extended model. The correlation functions which consist of the noise correlation function are applied to compensate the bias caused by colored noise. As a result, the parameters of the Nonlinear Part can be identified through recursive least square method. Examples results illustrate that the proposed approach has high identification accuracy and good robustness to the disturbance of colored noise.
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Correlation analysis based MIMO neuro-fuzzy Hammerstein model with noises
Journal of Process Control, 2016Co-Authors: Li Jia, Min-sen ChiuAbstract:Abstract A novel identification algorithm for neuro-fuzzy based MIMO Hammerstein system with noises by using the correlation analysis method is presented in this paper. A special test signal that contains independent separable signals and uniformly random multi-step signal is adopted to identify the MIMO Hammerstein system, resulting in the identification problem of the linear model separated from that of Nonlinear Part. As a result, it can circumvent the problem of initialization and convergence of the model parameters encountered by the existing iterative algorithms used for identification of MIMO Hammerstein model. Moreover, least square method based parameter identification algorithms of dynamic linear Part and static Nonlinear Part are proposed to avoid the influence of noise. Examples are used to illustrate the effectiveness of the proposed method.
Tian Zengshan - One of the best experts on this subject based on the ideXlab platform.
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Research on n-Scroll Chaos Generator
Computer Simulation, 2008Co-Authors: Tian ZengshanAbstract:Based on the modified Chua’s circuit,a deep study on an n-scroll chaos generator was made. First,the characteristics of the piecewise linearity in Nonlinear Part were discussed for the Chua’s circuit. The Nonlinear Part was subsequently modified and extended. And the mathematic model of the generator was built. The simulation proves the anticipated result. Then the relationship between equilibrium points and scrolls were researched. Finally,the scaling properties of the system were also studied.
Hong Wang - One of the best experts on this subject based on the ideXlab platform.
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Dynamic prediction model for mixed concentrate grade of mineral processing plant
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Jinliang Ding, Tianyou Chai, Hong WangAbstract:A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear Part and a Nonlinear Part is developed. The Nonlinear Part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches.
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CDC - Dynamic prediction model for mixed concentrate grade of mineral processing plant
49th IEEE Conference on Decision and Control (CDC), 2010Co-Authors: Jinliang Ding, Tianyou Chai, Hong WangAbstract:A non-linear modelling approach of dynamic prediction model for mixed concentrate grade consisting of a linear Part and a Nonlinear Part is developed. The Nonlinear Part is implemented using the least squares support vector machine (LS-SVM), where the problem of selecting model parameters is transformed into the probability distribution function (PDF) control of the modelling error. Both the PDF control based and minimum entropy based model parameter selection approaches are proposed. The experiment results show the effectiveness of the proposed approaches.
