Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear sy...Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.展开更多
This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. ...This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.展开更多
The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific re...The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific research institutions. An integrated ANFIS model is built and the effectiveness of the model is verified by means of investigation data and their processing results. The model merges the learning mechanism of neural network and the language inference ability of fuzzy system, and thereby remedies the defects of neural network and fuzzy logic system. Result of this case study shows that the model is suitable for complicated socio-technical systems and has bright application perspective to solve such problems of prediction, evaluation and policy-making in managerial fields.展开更多
The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator fa...The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.展开更多
An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control...An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.展开更多
An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and am...An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set （nine weeks, total 845 observations） was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.展开更多
In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal w...In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.展开更多
For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chippin...For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic展开更多
In this paper,adaptive dynamic surface control(DSC) is developed for a class of nonlinear systems with unknown discrete and distributed time-varying delays and unknown dead-zone.Fuzzy logic systems are used to approxi...In this paper,adaptive dynamic surface control(DSC) is developed for a class of nonlinear systems with unknown discrete and distributed time-varying delays and unknown dead-zone.Fuzzy logic systems are used to approximate the unknown nonlinear functions.Then,by combining the backstepping technique and the appropriate Lyapunov-Krasovskii functionals with the dynamic surface control approach,the adaptive fuzzy tracking controller is designed.Our development is able to eliminate the problem of 'explosion of complexity' inherent in the existing backstepping-based methods.The main advantages of our approach include:1) for the n-th-order nonlinear systems,only one parameter needs to be adjusted online in the controller design procedure,which reduces the computation burden greatly.Moreover,the input of the dead-zone with only one adjusted parameter is much simpler than the ones in the existing results;2) the proposed control scheme does not need to know the time delays and their upper bounds.It is proven that the proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error is smaller than a prescribed error bound,Finally,simulation results demonstrate the effectiveness of the proposed approach.展开更多
An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variat...An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.展开更多
A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, th...A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.展开更多
Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semicon...Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.展开更多
A novel control method for a general class of nonlinear systems using fuzzy logic systems （FLSs） is presertted. Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a ...A novel control method for a general class of nonlinear systems using fuzzy logic systems （FLSs） is presertted. Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.展开更多
Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus （SMIB） and...Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus （SMIB） and multi-machine power system stabilizer subjected to a three phase fault. The Nussbaum gain is used to avoid the positive sign constraint and the problem of controllability of the system. A comparative simulation study is presented to evaluate the achieved performance.展开更多
A new extension of the conventional adaptive fuzzy sliding mode control（AFSMC） scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output（MIMO） systems, is presented. In particular,...A new extension of the conventional adaptive fuzzy sliding mode control（AFSMC） scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output（MIMO） systems, is presented. In particular, the assumption for non-zero diagonal entries of the input gain matrix of the plant is relaxed. In other words, the control effect of one actuator can propagate from a subgroup of canonical state equations to the rest of equations in an indirect sense. The asymptotic stability of the proposed AFSM control method is proved using a Lyapunov-based methodology. The effectiveness of the proposed method for the case of under-actuated systems is investigated in the presence of plant uncertainties and disturbances, through simulation studies.展开更多
In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the ...In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.展开更多
A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation err...A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.展开更多
In this paper, an adaptive fuzzy robust feedback control approach is proposed for a class of single-input and singleoutput （SISO） strict-feedback nonlinear systems with unknown nonlinear functions, time delays, unkn...In this paper, an adaptive fuzzy robust feedback control approach is proposed for a class of single-input and singleoutput （SISO） strict-feedback nonlinear systems with unknown nonlinear functions, time delays, unknown high-frequency gain sign, and without the measurements of the states. In the backstepping recursive design, fuzzy logic systems are employed to approximate the unknown smooth nonlinear functions, K-filters is designed to estimate the unmeasured states, and Nussbaum gain functions are introduced to solve the problem of unknown sign of high-frequency gain. By combining adaptive fuzzy control theory and adaptive backstepping design, a stable adaptive fuzzy output feedback control scheme is developed. It has been proven that the proposed adaptive fuzzy robust control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin by appropriately choosing design parameters. Simulation results have shown the effectiveness of the proposed method.展开更多
This paper presents a robust output feedback control method for uncertain chaotic systems, which comprises a nonlinear inversion-based controller with a fuzzy robust compensator. The proposed controller eliminates the...This paper presents a robust output feedback control method for uncertain chaotic systems, which comprises a nonlinear inversion-based controller with a fuzzy robust compensator. The proposed controller eliminates the unknown nonlinear function by using a fuzzy system, whose inputs are not the state variables but feedback error signals. The underlying stability analysis as well as parameter update law design are carried out by using the Lyapunov-based technique. The proposed method indicates that the nonlinear inversion-based control approach can also be applied to uncertain chaotic systems. Theoretical results are illustrated through two simulation examples.展开更多
An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membe...An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.展开更多
文摘Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.
基金the National Natural Science Foundation of China under Grant U22A2043.
文摘This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.
基金Supported by the Soft Science Program of Jiangsu Province(BR2010079)~~
文摘The adaptive neural fuzzy inference system （ANFIS） is used to make a ease study considering features of complex social-technical system with the target of increasing organizational efficiency of public scientific research institutions. An integrated ANFIS model is built and the effectiveness of the model is verified by means of investigation data and their processing results. The model merges the learning mechanism of neural network and the language inference ability of fuzzy system, and thereby remedies the defects of neural network and fuzzy logic system. Result of this case study shows that the model is suitable for complicated socio-technical systems and has bright application perspective to solve such problems of prediction, evaluation and policy-making in managerial fields.
基金partially supported by the National Natural Science Foundation of China(62322307)Sichuan Science and Technology Program,China(2023NSFSC1968).
文摘The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults.The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging.To tackle this issue,we propose a novel practical prescribed-time fuzzy tracking control strategy,which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators.We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system.The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control,and extend the range of prescribed-time tracking control to infinity.Regardless of the initial conditions,the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults.Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.
文摘An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(No. 50778009)
文摘An adaptive neuro fuzzy inference system was used for classifying water quality status of river. It applied several physical and inorganic chemical indicators including dissolved oxygen, chemical oxygen demand, and ammonia-nitrogen. A data set （nine weeks, total 845 observations） was collected from 100 monitoring stations in all major river basins in China and used for training and validating the model. Up to 89.59% of the data could be correctly classified using this model. Such performance was more competitive when compared with artificial neural networks. It is applicable in evaluation and classification of water quality status.
基金supported in part by the National Natural Science Foundation of China (61773051,61773072,61761166011)the Fundamental Research Fund for the Central Universities (2016RC021,2017JBZ003)
文摘In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.
文摘For electro-discharge machining, only in the optimum state could the highest material removal rate be realized. In practical machining process, the timely elevation of the tool electrode is needed to eliminate chipping, which ordinarily occupies quite a lot of time. Therefore, besides the control of the machining parameters, the control of the optimum discharge gap and the conversion of different machining states is also needed. In this paper, the adaptive fuzzy control system of servomechanism for EDM combined with ultrasonic vibration is studied, the servomechanism of which is composed of the stepping motor comprising variable steps and the inductive synchronizer. The fuzzy control technology is used to realize the control of the frequency and the step of the servomechanism. The adaptive fuzzy controller has three inputs and two outputs, which can well meet the actual control requirements. The constitution of the fuzzy control regulation for the step frequency is the key to the design of the whole fuzzy control system of the servomechanism. The step frequency is mainly determined by the position error and the change rate of the position error. When the value of the position error is high or medium, the controlled parameters are selected to eliminate the error; when the position error is lower, the controlled parameters are selected to avoid the over-orientation and thus keep the stability of the system. According to these, a fuzzy control table is established in advanced, which is used to express the relations between the fuzzy input parameters and the fuzzy output parameters. The input parameters and the output parameters are all expressed by the level-values in fuzzy field. Therefore, the output parameters used for control can be obtained for the fuzzy control table according to the detected actual input parameters, by which the EDM combined with ultrasonic vibration is improved and the machining efficiency is increased. In addition, a stimulation program is designed by means of Microsoft Visual Basic
基金supported by National Natural Science Foundation of China (Nos. 60974139 and 60804021)Fundamental Research Funds for the Central Universities (No. 72103676)
文摘In this paper,adaptive dynamic surface control(DSC) is developed for a class of nonlinear systems with unknown discrete and distributed time-varying delays and unknown dead-zone.Fuzzy logic systems are used to approximate the unknown nonlinear functions.Then,by combining the backstepping technique and the appropriate Lyapunov-Krasovskii functionals with the dynamic surface control approach,the adaptive fuzzy tracking controller is designed.Our development is able to eliminate the problem of 'explosion of complexity' inherent in the existing backstepping-based methods.The main advantages of our approach include:1) for the n-th-order nonlinear systems,only one parameter needs to be adjusted online in the controller design procedure,which reduces the computation burden greatly.Moreover,the input of the dead-zone with only one adjusted parameter is much simpler than the ones in the existing results;2) the proposed control scheme does not need to know the time delays and their upper bounds.It is proven that the proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error is smaller than a prescribed error bound,Finally,simulation results demonstrate the effectiveness of the proposed approach.
基金The National Natural Science Foundation of China under contract No.51379002the Fundamental Research Funds for the Central Universities of China under contract Nos 3132016322 and 3132016314the Applied Basic Research Project Fund of the Chinese Ministry of Transport of China under contract No.2014329225010
文摘An efficient and accurate prediction of a precise tidal level in estuaries and coastal areas is indispensable for the management and decision-making of human activity in the field wok of marine engineering. The variation of the tidal level is a time-varying process. The time-varying factors including interference from the external environment that cause the change of tides are fairly complicated. Furthermore, tidal variations are affected not only by periodic movement of celestial bodies but also by time-varying interference from the external environment. Consequently, for the efficient and precise tidal level prediction, a neuro-fuzzy hybrid technology based on the combination of harmonic analysis and adaptive network-based fuzzy inference system（ANFIS）model is utilized to construct a precise tidal level prediction system, which takes both advantages of the harmonic analysis method and the ANFIS network. The proposed prediction model is composed of two modules： the astronomical tide module caused by celestial bodies’ movement and the non-astronomical tide module caused by various meteorological and other environmental factors. To generate a fuzzy inference system（FIS） structure,three approaches which include grid partition（GP）, fuzzy c-means（FCM） and sub-clustering（SC） are used in the ANFIS network constructing process. Furthermore, to obtain the optimal ANFIS based prediction model, large numbers of simulation experiments are implemented for each FIS generating approach. In this tidal prediction study, the optimal ANFIS model is used to predict the non-astronomical tide module, while the conventional harmonic analysis model is used to predict the astronomical tide module. The final prediction result is performed by combining the estimation outputs of the harmonious analysis model and the optimal ANFIS model. To demonstrate the applicability and capability of the proposed novel prediction model, measured tidal level samples of Fort Pulaski tidal station are selected as the testing database. Simulation and experimental results confirm that the proposed prediction approach can achieve precise predictions for the tidal level with high accuracy, satisfactory convergence and stability.
基金Project(51005253) supported by the National Natural Science Foundation of ChinaProject(2007AA04Z344) supported by the National High Technology Research and Development Program of China
文摘A new adaptive Type-2 （T2） fuzzy controller was developed and its potential performance advantage over adaptive Type-1 （T1） fuzzy control was also quantified in computer simulation. Base on the Lyapunov method, the adaptive laws with guaranteed system stability and convergence were developed. The controller updates its parameters online using the laws to control a system and tracks its output command trajectory. The simulation study involving the popular inverted pendulum control problem shows theoretically predicted system stability and good tracking performance. And the comparison simulation experiments subjected to white noige or step disturbance indicate that the T2 controller is better than the T1 controller by 0--18%, depending on the experiment condition and performance measure.
基金Supported by the National Key Basic Research and Development Program of China (2009CB320602)the National Natural Science Foundation of China (60834004, 61025018)+2 种基金the Open Project Program of the State Key Lab of Industrial ControlTechnology (ICT1108)the Open Project Program of the State Key Lab of CAD & CG (A1120)the Foundation of Key Laboratory of System Control and Information Processing (SCIP2011005),Ministry of Education,China
文摘Semiconductor manufacturing (SM) system is one of the most complicated hybrid processes involved continuously variable dynamical systems and discrete event dynamical systems. The optimization and scheduling of semiconductor fabrication has long been a hot research direction in automation. Bottleneck is the key factor to a SM system, which seriously influences the throughput rate, cycle time, time-delivery rate, etc. Efficient prediction for the bottleneck of a SM system provides the best support for the consequent scheduling. Because categorical data (product types, releasing strategies) and numerical data (work in process, processing time, utilization rate, buffer length, etc.) have significant effect on bottleneck, an improved adaptive network-based fuzzy inference system (ANFIS) was adopted in this study to predict bottleneck since conventional neural network-based methods accommodate only numerical inputs. In this improved ANFIS, the contribution of categorical inputs to firing strength is reflected through a transformation matrix. In order to tackle high-dimensional inputs, reduce the number of fuzzy rules and obtain high prediction accuracy, a fuzzy c-means method combining binary tree linear division method was applied to identify the initial structure of fuzzy inference system. According to the experimental results, the main-bottleneck and sub-bottleneck of SM system can be predicted accurately with the proposed method.
基金This project was supported by the National Natural Science Foundation of China (90405011).
文摘A novel control method for a general class of nonlinear systems using fuzzy logic systems （FLSs） is presertted. Indirect and direct methods are combined to design the adaptive fuzzy output feedback controller and a high-gain observer is used to estimate the derivatives of the system output. The closed-loop system is proven to be semiglobally uniformly ultimately bounded. In addition, it is shown that if the approximation accuracy of the fuzzy logic system is high enough and the observer gain is chosen sufficiently large, an arbitrarily small tracking error can be achieved. Simulation results verify the effectiveness of the newly designed scheme and the theoretical discussion.
文摘Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus （SMIB） and multi-machine power system stabilizer subjected to a three phase fault. The Nussbaum gain is used to avoid the positive sign constraint and the problem of controllability of the system. A comparative simulation study is presented to evaluate the achieved performance.
文摘A new extension of the conventional adaptive fuzzy sliding mode control（AFSMC） scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output（MIMO） systems, is presented. In particular, the assumption for non-zero diagonal entries of the input gain matrix of the plant is relaxed. In other words, the control effect of one actuator can propagate from a subgroup of canonical state equations to the rest of equations in an indirect sense. The asymptotic stability of the proposed AFSM control method is proved using a Lyapunov-based methodology. The effectiveness of the proposed method for the case of under-actuated systems is investigated in the presence of plant uncertainties and disturbances, through simulation studies.
基金supported by National Natural Science Foundationof China (No. 60674056)National Key Basic Research and Devel-opment Program of China (No. 2002CB312200)+1 种基金Outstanding YouthFunds of Liaoning Province (No. 2005219001)Educational De-partment of Liaoning Province (No. 2006R29 and No. 2007T80)
文摘In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(90510010).
文摘A novel H∞ tracking-based decentralized indirect adaptive output feedback fuzzy controller for a class of uncertain large-scale nonlinear systems is developed. By virtue of the proper filtering of the observation error dynamics, the observer-based decentralized indirect adaptive fuzzy control scheme is presented for a class of large-scale nonlinear systems using the combination of H∞ tracking technique, a fuzzy adaptive observer and fuzzy inference systems. The output feedback and adaptation mechanisms are both robust and implementable indeed owing to their freedom from the unavailable observation error vector. All the signals of the closed-loop largescale system are guaranteed to stay uniformly bounded and the output errors take on H∞ tracking performance. Simulation results substantiate the effectiveness of the proposed scheme.
基金supported by National Natural Science Foundation of China (No. 61074014)the Outstanding Youth Funds of Liaoning Province (No. 2005219001)Educational Department of Liaoning Province (No. 2006R29, No. 2007T80)
文摘In this paper, an adaptive fuzzy robust feedback control approach is proposed for a class of single-input and singleoutput （SISO） strict-feedback nonlinear systems with unknown nonlinear functions, time delays, unknown high-frequency gain sign, and without the measurements of the states. In the backstepping recursive design, fuzzy logic systems are employed to approximate the unknown smooth nonlinear functions, K-filters is designed to estimate the unmeasured states, and Nussbaum gain functions are introduced to solve the problem of unknown sign of high-frequency gain. By combining adaptive fuzzy control theory and adaptive backstepping design, a stable adaptive fuzzy output feedback control scheme is developed. It has been proven that the proposed adaptive fuzzy robust control approach can guarantee that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can converge to a small neighborhood of the origin by appropriately choosing design parameters. Simulation results have shown the effectiveness of the proposed method.
基金Project supported by the Young Talents Natural Science Foundation for Universities of Anhui Province,China(Grant No.2012SQRL179)
文摘This paper presents a robust output feedback control method for uncertain chaotic systems, which comprises a nonlinear inversion-based controller with a fuzzy robust compensator. The proposed controller eliminates the unknown nonlinear function by using a fuzzy system, whose inputs are not the state variables but feedback error signals. The underlying stability analysis as well as parameter update law design are carried out by using the Lyapunov-based technique. The proposed method indicates that the nonlinear inversion-based control approach can also be applied to uncertain chaotic systems. Theoretical results are illustrated through two simulation examples.
基金surported by Tianjin Science and Technology Development for Higher Education(20051206).
文摘An indirect adaptive fuzzy control scheme is developed for a class of nonlinear discrete-time systems. In this method, two fuzzy logic systems are used to approximate the unknown functions, and the parameters of membership functions in fuzzy logic systems are adjusted according to adaptive laws for the purpose of controlling the plant to track a reference trajectory. It is proved that the scheme can not only guarantee the boundedness of the input and output of the closed-loop system, but also make the tracking error converge to a small neighborhood of the origin. Simulation results indicate the effectiveness of this scheme.
