In this paper,we propose a novel co-occurrence probabilities based similarity measure for inducing semantic classes.Clustering with the new similarity measure outperforms the widely used distance based on Kullback-Lei...In this paper,we propose a novel co-occurrence probabilities based similarity measure for inducing semantic classes.Clustering with the new similarity measure outperforms the widely used distance based on Kullback-Leibler divergence in precision,recall and F1 evaluation.In our experiments,we induced semantic classes from unannotated in-domain corpus and then used the induced classes and structures to generate large in-domain corpus which was then used for language model adaptation.Character recognition rate was improved from 85.2% to 91%.We imply a new measure to solve the lack of domain data problem by first induction then generation for a dialogue system.展开更多
Mountain glaciers have an obvious location advantage and tourist market condition over polar and high latitude glaciers. Due to the enormous economic benefit and heritage value, some mountain glaciers will always rece...Mountain glaciers have an obvious location advantage and tourist market condition over polar and high latitude glaciers. Due to the enormous economic benefit and heritage value, some mountain glaciers will always receive higher attention from commercial media, government departments and mountain tourists in China and abroad. At present, more than 100 glaciers have been devel- oped successfully as famous tourist destinations all over the world. However, global climate change seriously affects mountain glaciers and its surrounding environment. According to the current accelerated retreat trend, natural and cultural landscapes of some glaciers will be weakened, even disappear in the future. Climate change will also inevitably affect mountain ecosystems, and tourism routes under ice and glacier experience activities in these ecosystems. Simultaneously, the disappearance of mountain glaciers will also lead to a clear reduction of tourism and local economic benefits. Based on these reasons, this paper took Mr. Yulong Snow scenic area as an example and analyzed the retreat trend of a typical glacier. We then put forward some scientific and rational response mechanisms and adaptation models based on climate change in order to help future sustainable development of mountain glacier tourism.展开更多
Objective:This study was to evaluate the quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction in China.Methods:We systematically searche...Objective:This study was to evaluate the quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction in China.Methods:We systematically searched the Cnki,Wanfang and Vipdatabases,to get randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction.The search period was from inception to October 2020.According to the Cochrane risk bias assessment tool,the quality of the studies included was appraised.Results:A total of 55 studies were retrieved,and 11 were eventually included in the study.Among the studies included,the first study was published in 2008.The overall quality of the 11 studies included was relatively low.Conclusions:The overall quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction was not high,which would hinder the evidence transformation as well as clinical practice.展开更多
The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compe...The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.展开更多
The application of a simplifed model reference adaptive control(SMRAC) on a typical Pump controlled motor electrohydraulic servo system is studied here. The algorithm of first-order scalar SMRAC ac second-order vector...The application of a simplifed model reference adaptive control(SMRAC) on a typical Pump controlled motor electrohydraulic servo system is studied here. The algorithm of first-order scalar SMRAC ac second-order vector SMRAC are derived. Computer simulations of the algorithms are presented. Experimental results prove that the method of control adopted here perform satisfactorily over a wide range of operating conditions.展开更多
Aim To present an adaptive missile control system adaped to the external disturbance and the mobility of target movement. Methods Model reference adaptive control (MRAC) was applied and modified in the light of the ...Aim To present an adaptive missile control system adaped to the external disturbance and the mobility of target movement. Methods Model reference adaptive control (MRAC) was applied and modified in the light of the traits of the anti tank missile. Results Simulation results demonstrated this control system satisfied the requirement of anti tank missile of dive overhead attack. Conclusion It is successful to use MRAC in missile control system design, the quality is better than that designed by classical control theory.展开更多
A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performan...A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.展开更多
Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adap...Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.展开更多
For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For ...For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.展开更多
Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo...Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.展开更多
Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed perfor...Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model’s output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Notably,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%threshold.These findings underscore the clear superiority of the proposed method.展开更多
In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support ve...In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.展开更多
To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a sec...To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a secondary servo mechanism to control lens motion in the imaging optical path.Firstly,the dual-stage dynamics is mathematically modeled as a coupling multi-input multi-output(MIMO)control system.Then,by incorporating compensation of adaptive model to deal with parameter variations and nonlinearity,a systematic robust H∞control scheme is designed,which can achieve good tracking performance,as well as improve system robustness against model uncertainties.Lyapunov stability analysis confirmed the stability of the overall control system.Finally,simulation and experiment results are provided to demonstrate the feasibility and effectiveness of the proposed control design method.展开更多
For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated....For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.展开更多
This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot...This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.展开更多
The onboard adaptive model can achieve the online real-time estimation of performance parameters that are difficult to measure in a real aero-engine,which is the key to realizing modelbased performance control.It must...The onboard adaptive model can achieve the online real-time estimation of performance parameters that are difficult to measure in a real aero-engine,which is the key to realizing modelbased performance control.It must possess satisfactory numerical stability and estimation accuracy.However,the positive definiteness of the state covariance matrix may be destroyed in filter estimation because of the existence of some uncertain factors,such as the accumulated measurement error,noise,and disturbance in the strongly nonlinear engine system,inevitably causing divergence of estimates of Cholesky decomposition-based Spherical Unscented Kalman Filter(SUKF).Therefore,this paper proposes an improved SUKF algorithm(iSUKF)and applies it to the performance degradation estimation of the engine.Compared to SUKF,the iSUKF mainly replaces the Cholesky decomposition with the Singular Value Decomposition(SVD),which is numerically stable without any strict requirement for the state covariance matrix.Meanwhile,a correction factor is designed to assess the measurement deviation between the real engine and the nonlinear onboard model to correct the state covariance matrix,thus maintaining better numerical stability of parameters estimated by the filter.Then,an offline correction strategy is also proposed to eliminate the influence of the degradation of unestimated health parameters or the filter’s inadequate estimation of the coupled health parameters.This action effectively promotes the onboard adaptive model’s estimation accuracy concerning the degradation of the engine’real health parameters and its performance parameters.Finally,the simulation results show that the iSUKF can maintain the numerical stability of the filter’s estimation of health parameters.Compared with the existing methods,the offline correction strategy improves the estimation accuracy of the iSUKF-based nonlinear onboard adaptive model for the performance parameters of the real engine by more than 50%.The proposed method will provide feasible technical support for model-based aero-engine performance control.展开更多
A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,...A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,or environmental changes.Since PLS decomposition structure enables multi-loop controller design within latent spaces,a multivariable adaptive control scheme can be converted easily into several independent univariable control loops in the PLS space.In each latent subspace,once the model error exceeds a specific threshold,online adaptation rules are implemented separately to correct the plant model mismatch via a recursive least squares (RLS) algorithm.Because the IMC extracts the inverse of the minimum part of the internal model as its structure,the IMC controller is self-tuned by explicitly updating the parameters,which are parts of the internal model.Both parameter convergence and system stability are briefly analyzed,and proved to be effective.Finally,the proposed control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output (MIMO) system with pure delay.展开更多
The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is ...The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.展开更多
Structural components may enter an initial-elastic state,a plastic-hardening state and a residual-elastic state during strong seismic excitations.In the residual-elastic state,structural components keep in an unloadin...Structural components may enter an initial-elastic state,a plastic-hardening state and a residual-elastic state during strong seismic excitations.In the residual-elastic state,structural components keep in an unloading/reloading stage that is dominated by a tangent stiffness,thus structural components remain residual deformations but behave in an elastic manner.It has a great potential to make model order reduction for such structural components using the tangent-stiffness-based vibration modes as a reduced order basis.In this paper,an adaptive substructure-based model order reduction method is developed to perform nonlinear seismic analysis for structures that have a priori unknown damage distribution.This method is able to generate time-varying substructures and make nonlinear model order reduction for substructures in the residual-elastic phase.The finite element program OpenSees has been extended to provide the adaptive substructure-based nonlinear seismic analysis.At the low level of OpenSees framework,a new abstract layer is created to represent the time-varying substructures and implement the modeling process of substructures.At the high level of OpenSees framework,a new transient analysis class is created to implement the solving process of substructure-based governing equations.Compared with the conventional time step integration method,the adaptive substructure-based model order reduction method can yield comparative results with a higher computational efficiency.展开更多
Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction me...Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos. 10925419,90920302,10874203,60875014,61072124,11074275,11161140319.
文摘In this paper,we propose a novel co-occurrence probabilities based similarity measure for inducing semantic classes.Clustering with the new similarity measure outperforms the widely used distance based on Kullback-Leibler divergence in precision,recall and F1 evaluation.In our experiments,we induced semantic classes from unannotated in-domain corpus and then used the induced classes and structures to generate large in-domain corpus which was then used for language model adaptation.Character recognition rate was improved from 85.2% to 91%.We imply a new measure to solve the lack of domain data problem by first induction then generation for a dialogue system.
基金funded by the open fund (SKLCS2011-04) from Stake Key Laboratory of Cryospheric Sciences and National Social Science Foundation of China(12BJY127)
文摘Mountain glaciers have an obvious location advantage and tourist market condition over polar and high latitude glaciers. Due to the enormous economic benefit and heritage value, some mountain glaciers will always receive higher attention from commercial media, government departments and mountain tourists in China and abroad. At present, more than 100 glaciers have been devel- oped successfully as famous tourist destinations all over the world. However, global climate change seriously affects mountain glaciers and its surrounding environment. According to the current accelerated retreat trend, natural and cultural landscapes of some glaciers will be weakened, even disappear in the future. Climate change will also inevitably affect mountain ecosystems, and tourism routes under ice and glacier experience activities in these ecosystems. Simultaneously, the disappearance of mountain glaciers will also lead to a clear reduction of tourism and local economic benefits. Based on these reasons, this paper took Mr. Yulong Snow scenic area as an example and analyzed the retreat trend of a typical glacier. We then put forward some scientific and rational response mechanisms and adaptation models based on climate change in order to help future sustainable development of mountain glacier tourism.
基金This research was supported by National Natural Science Foundation of China(No.81603565)Tianjin University of Traditional Chinese Medicine Postgraduate Research Innovation Project(YJSKC-20201032).
文摘Objective:This study was to evaluate the quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction in China.Methods:We systematically searched the Cnki,Wanfang and Vipdatabases,to get randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction.The search period was from inception to October 2020.According to the Cochrane risk bias assessment tool,the quality of the studies included was appraised.Results:A total of 55 studies were retrieved,and 11 were eventually included in the study.Among the studies included,the first study was published in 2008.The overall quality of the 11 studies included was relatively low.Conclusions:The overall quality of the randomized controlled trials on Roy adaptation model nursing in individuals suffering from acute myocardial infarction was not high,which would hinder the evidence transformation as well as clinical practice.
文摘The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.
文摘The application of a simplifed model reference adaptive control(SMRAC) on a typical Pump controlled motor electrohydraulic servo system is studied here. The algorithm of first-order scalar SMRAC ac second-order vector SMRAC are derived. Computer simulations of the algorithms are presented. Experimental results prove that the method of control adopted here perform satisfactorily over a wide range of operating conditions.
文摘Aim To present an adaptive missile control system adaped to the external disturbance and the mobility of target movement. Methods Model reference adaptive control (MRAC) was applied and modified in the light of the traits of the anti tank missile. Results Simulation results demonstrated this control system satisfied the requirement of anti tank missile of dive overhead attack. Conclusion It is successful to use MRAC in missile control system design, the quality is better than that designed by classical control theory.
文摘A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.
文摘Model reference adaptive control is a viable control method to impose the demanded dynamics on plants whose parameters are affected by large uncertainty. In this paper, we show by means of experiments that robust adaptive methods can effectively face nonlinearities that are common to many automotive electromechanical devices. We consider here, as a representative case study, the control of a strongly nonlinear automotive actuator. The experimental results confirm the effectiveness of the method to cope with unmodeled nonlinear terms and unknown parameters. In addition, the engineering performance indexes computed on experimental data clearly show that the robust adaptive strategy provides better performance compared with those given by a classical model-based control solution with fixed gains.
基金This work was supported by the National Natural Science Foundation(NNSF)of China under grant no.61673386,62073335the China Postdoctoral Science Foundation(2017M613201,2019T120944).
文摘For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang For the case that two pursuers intercept an evasive target,the cooperative strategies and state estimation methods taken by pursuers can seriously affect the guidance accuracy for the target,which performs a bang-bang evasive maneuver with a random switching time.Combined Fast multiple model adaptive estimation(Fast MMAE)algorithm,the cooperative guidance law takes detection configuration affecting the accuracy of interception into consideration.Introduced the detection error model related to the line-of-sight(LOS)separation angle of two interceptors,an optimal cooperative guidance law solving the optimization problem is designed to modulate the LOS separation angle to reduce the estimation error and improve the interception performance.Due to the uncertainty of the target bang-bang maneuver switching time and the effective fitting of its multi-modal motion,Fast MMAE is introduced to identify its maneuver switching time and estimate the acceleration of the target to track and intercept the target accurately.The designed cooperative optimal guidance law with Fast MMAE has better estimation ability and interception performance than the traditional guidance law and estimation method via Monte Carlo simulation.
基金supported by Beijing Natural Science Foundation(No.4142017)International Cooperation Project of National Natural Science Foundation of China(No.61120106009)Beijing Science and Technology Commission Precision Machinery Projects(No.Z121100001612007)
文摘Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.
基金co-supported by the National Natural Science Foundation of China(Nos.61890921,61890924)the National Science and Technology Major Project,China(No.J2019-1-0019-0018).
文摘Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model’s output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Notably,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%threshold.These findings underscore the clear superiority of the proposed method.
基金co-supported by Aeronautical Science Foundation of China (No. 2010ZB52011)Funding of Jiangsu Innovation Program for Graduate Education (No.CXLX11_0213)
文摘In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.
基金Project (61174203) supported by the National Natural Science Foundation of China
文摘To achieve excellent tracking accuracy,a coarse-fine dual-stage control system is chosen for inertially stabilized platform.The coarse stage is a conventional inertially stabilized platform,and the fine stage is a secondary servo mechanism to control lens motion in the imaging optical path.Firstly,the dual-stage dynamics is mathematically modeled as a coupling multi-input multi-output(MIMO)control system.Then,by incorporating compensation of adaptive model to deal with parameter variations and nonlinearity,a systematic robust H∞control scheme is designed,which can achieve good tracking performance,as well as improve system robustness against model uncertainties.Lyapunov stability analysis confirmed the stability of the overall control system.Finally,simulation and experiment results are provided to demonstrate the feasibility and effectiveness of the proposed control design method.
基金supported by National Natural Science Foundation of China (No. 60774010, 10971256, 60974028)Natural Science Foundation of Jiangsu Province (No. BK2009083)+2 种基金Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province(No. 07KJB510114)Shandong Provincial Natural Science Foundation of China (No. ZR2009GM008)Natural Science Foundation of Jining University (No. 2009KJLX02)
文摘For a class of discrete-time systems with unmodeled dynamics and bounded disturbance, the design and analysis of robust indirect model reference adaptive control （MRAC） with normalized adaptive law are investigated. The main work includes three parts. Firstly, it is shown that the constructed parameter estimation algorithm not only possesses the same properties as those of traditional estimation algorithms, but also avoids the possibility of division by zero. Secondly, by establishing a relationship between the plant parameter estimate and the controller parameter estimate, some similar properties of the latter are also established. Thirdly, by using the relationship between the normalizing signal and all the signals of the closed-loop system, and some important mathematical tools on discrete-time systems, as in the continuous-time case, a systematic stability and robustness analysis approach to the discrete indirect robust MRAC scheme is developed rigorously.
基金The work was supported by the National Science Foundation，the Office of Naval Research grant，the AFOSR (Air Force Office of Scientific Research) EOARD (European Office of Aerospace Research and Development) grant，the U.S. Army Research Office grant
文摘This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.
基金the National Natural Science Foundation of China(Nos.51906103,52176009).
文摘The onboard adaptive model can achieve the online real-time estimation of performance parameters that are difficult to measure in a real aero-engine,which is the key to realizing modelbased performance control.It must possess satisfactory numerical stability and estimation accuracy.However,the positive definiteness of the state covariance matrix may be destroyed in filter estimation because of the existence of some uncertain factors,such as the accumulated measurement error,noise,and disturbance in the strongly nonlinear engine system,inevitably causing divergence of estimates of Cholesky decomposition-based Spherical Unscented Kalman Filter(SUKF).Therefore,this paper proposes an improved SUKF algorithm(iSUKF)and applies it to the performance degradation estimation of the engine.Compared to SUKF,the iSUKF mainly replaces the Cholesky decomposition with the Singular Value Decomposition(SVD),which is numerically stable without any strict requirement for the state covariance matrix.Meanwhile,a correction factor is designed to assess the measurement deviation between the real engine and the nonlinear onboard model to correct the state covariance matrix,thus maintaining better numerical stability of parameters estimated by the filter.Then,an offline correction strategy is also proposed to eliminate the influence of the degradation of unestimated health parameters or the filter’s inadequate estimation of the coupled health parameters.This action effectively promotes the onboard adaptive model’s estimation accuracy concerning the degradation of the engine’real health parameters and its performance parameters.Finally,the simulation results show that the iSUKF can maintain the numerical stability of the filter’s estimation of health parameters.Compared with the existing methods,the offline correction strategy improves the estimation accuracy of the iSUKF-based nonlinear onboard adaptive model for the performance parameters of the real engine by more than 50%.The proposed method will provide feasible technical support for model-based aero-engine performance control.
基金Project supported by the National Natural Science Foundation of China (No.60574047)the National High-Tech R & D Program (863)of China (No.2007AA04Z168)the Research Fund for the Doctoral Program of Higher Education of China (No.20050335018)
文摘A multi-loop adaptive internal model control (IMC) strategy based on a dynamic partial least squares (PLS) frame-work is proposed to account for plant model errors caused by slow aging,drift in operational conditions,or environmental changes.Since PLS decomposition structure enables multi-loop controller design within latent spaces,a multivariable adaptive control scheme can be converted easily into several independent univariable control loops in the PLS space.In each latent subspace,once the model error exceeds a specific threshold,online adaptation rules are implemented separately to correct the plant model mismatch via a recursive least squares (RLS) algorithm.Because the IMC extracts the inverse of the minimum part of the internal model as its structure,the IMC controller is self-tuned by explicitly updating the parameters,which are parts of the internal model.Both parameter convergence and system stability are briefly analyzed,and proved to be effective.Finally,the proposed control scheme is tested and evaluated using a widely-used benchmark of a multi-input multi-output (MIMO) system with pure delay.
基金supported in part by National Basic Research Program of China(No.2012CB821200)in part by the National Natural Science Foundation of China(No.61174024)
文摘The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
基金supported by the National Nature Science Foundation of China(No.51678210)National Key Research and Development Program of China(No.2016YFC0701400).
文摘Structural components may enter an initial-elastic state,a plastic-hardening state and a residual-elastic state during strong seismic excitations.In the residual-elastic state,structural components keep in an unloading/reloading stage that is dominated by a tangent stiffness,thus structural components remain residual deformations but behave in an elastic manner.It has a great potential to make model order reduction for such structural components using the tangent-stiffness-based vibration modes as a reduced order basis.In this paper,an adaptive substructure-based model order reduction method is developed to perform nonlinear seismic analysis for structures that have a priori unknown damage distribution.This method is able to generate time-varying substructures and make nonlinear model order reduction for substructures in the residual-elastic phase.The finite element program OpenSees has been extended to provide the adaptive substructure-based nonlinear seismic analysis.At the low level of OpenSees framework,a new abstract layer is created to represent the time-varying substructures and implement the modeling process of substructures.At the high level of OpenSees framework,a new transient analysis class is created to implement the solving process of substructure-based governing equations.Compared with the conventional time step integration method,the adaptive substructure-based model order reduction method can yield comparative results with a higher computational efficiency.
基金Project supported by the National Natural Science Foundation of China(Nos.12272211,12072181,12121002)。
文摘Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.
