Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncerta...Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.展开更多
The control law design for a near-space hypersonic vehicle（NHV） is highly challenging due to its inherent nonlinearity,plant uncertainties and sensitivity to disturbances.This paper presents a novel functional link ...The control law design for a near-space hypersonic vehicle（NHV） is highly challenging due to its inherent nonlinearity,plant uncertainties and sensitivity to disturbances.This paper presents a novel functional link network（FLN） control method for an NHV with dynamical thrust and parameter uncertainties.The approach devises a new partially-feedback-functional-link-network（PFFLN） adaptive law and combines it with the nonlinear generalized predictive control（NGPC） algorithm.The PFFLN is employed for approximating uncertainties in flight.Its weights are online tuned based on Lyapunov stability theorem for the first time.The learning process does not need any offline training phase.Additionally,a robust controller with an adaptive gain is designed to offset the approximation error.Finally,simulation results show a satisfactory performance for the NHV attitude tracking,and also illustrate the controller＇s robustness.展开更多
Based on fuzzy adaptive and dynamic surface(FADS),an integrated guidance and control(IGC)approach was proposed for large caliber naval gun guided projectile,which was robust to target maneuver,canard dynamic character...Based on fuzzy adaptive and dynamic surface(FADS),an integrated guidance and control(IGC)approach was proposed for large caliber naval gun guided projectile,which was robust to target maneuver,canard dynamic characteristics,and multiple constraints,such as impact angle,limited measurement of line of sight(LOS)angle rate and nonlinear saturation of canard deflection.Initially,a strict feedback cascade model of IGC in longitudinal plane was established,and extended state observer(ESO)was designed to estimate LOS angle rate and uncertain disturbances with unknown boundary inside and outside of system,including aerodynamic parameters perturbation,target maneuver and model errors.Secondly,aiming at zeroing LOS angle tracking error and LOS angle rate in finite time,a nonsingular terminal sliding mode(NTSM)was designed with adaptive exponential reaching law.Furthermore,combining with dynamic surface,which prevented the complex differential of virtual control laws,the fuzzy adaptive systems were designed to approximate observation errors of uncertain disturbances and to reduce chatter of control law.Finally,the adaptive Nussbaum gain function was introduced to compensate nonlinear saturation of canard deflection.The LOS angle tracking error and LOS angle rate were convergent in finite time and whole system states were uniform ultimately bounded,rigorously proven by Lyapunov stability theory.Hardware-in-the-loop simulation(HILS)and digital simulation experiments both showed FADS provided guided projectile with good guidance performance while striking targets with different maneuvering forms.展开更多
An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wid...An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.展开更多
An adaptive current compensation control for a single-sided linear induction motor(SLIM) with nonlinear disturbance observer was developed. First, to maintain t-axis secondary component flux constant with consideratio...An adaptive current compensation control for a single-sided linear induction motor(SLIM) with nonlinear disturbance observer was developed. First, to maintain t-axis secondary component flux constant with consideration of the specially dynamic eddy-effect(DEE) of the SLIM, a instantaneously tracing compensation of m-axis current component was analyzed. Second,adaptive current compensation based on Taylor-discretization algorithm was proposed. Third, an effective kind of nonlinear disturbance observer(NDOB) was employed to estimate and compensate the undesired load vibrations, then the robustness of the control system could be guaranteed. Experimental verification of the feasibility of the proposed method for an SLIM control system was performed, and it showed that the proposed adaptive compensation scheme with NDOB could significantly promote speed dynamical response and minimize speed ripple under the conditions of external load coupled vibrations and unavoidable feedback control variables measured errors, i.e., current and speed.展开更多
Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employe...Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.展开更多
In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neu...In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.展开更多
In order to solve the problem of enhancing the vehicle driving stability and safety,which has been the hot question researched by scientific and engineering in the vehicle industry,the new control method was investiga...In order to solve the problem of enhancing the vehicle driving stability and safety,which has been the hot question researched by scientific and engineering in the vehicle industry,the new control method was investigated.After the analysis of tire moving characteristics and the vehicle stress analysis,the tire model based on the extension pacejka magic formula which combined longitudinal motion and lateral motion was developed and a nonlinear vehicle dynamical stability model with seven freedoms was made.A new model reference adaptive control project which made the slip angle and yaw rate of vehicle body as the output and feedback variable in adjusting the torque of vehicle body to control the vehicle stability was designed.A simulation model was also built in Matlab/Simulink to evaluate this control project.It was made up of many mathematical subsystem models mainly including the tire model module,the yaw moment calculation module,the center of mass parameter calculation module,tire parameter calculation module of multiple and so forth.The severe lane change simulation result shows that this vehicle model and the model reference adaptive control method have an excellent performance.展开更多
For a class of systems with unmodeled dynamics, robust adaptive stabilization problemis considered in this paper. Firstly, by a series of coordinate changes, the original system is re-parameterized. Then, by introduci...For a class of systems with unmodeled dynamics, robust adaptive stabilization problemis considered in this paper. Firstly, by a series of coordinate changes, the original system is re-parameterized. Then, by introducing a reduced-order observer, an error system is obtained. Basedon the system, a reduced-order adaptive backstepping controller design scheme is given. It is provedthat all the signals in the adaptive control system are globally uniformly bounded, and the regulationerror converges to zero asymptotically. Due to the order deduction of the controller, the design schemein this paper has more practical values. A simulation example further demonstrates the e?ciency ofthe control scheme.展开更多
Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put for...Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put forward by analyzing the interrelating concept and research. The model takes the adaptive process as a multi-stage decision making problem. The 2-phases method is presented to calculate the model, which obtains the related parameters by running the colored Petri net （CPN） model of AOC2R and then searches for the result by ant colony optimization （ACO） algorithm integrated with genetic optimization techniques. The simulation results demonstrate that the proposed algorithm greatly improves the performance of AOC2R.展开更多
基金National Natural Science Foundation of China(62373102)Jiangsu Natural Science Foundation(BK20221455)Anhui Provincial Key Research and Development Project(2022i01020013)。
文摘Enhancing the stability and performance of practical control systems in the presence of nonlinearity,time delay,and uncertainty remains a significant challenge.Particularly,a class of strict-feedback nonlinear uncertain systems characterized by unknown control directions and time-varying input delay lacks comprehensive solutions.In this paper,we propose an observerbased adaptive tracking controller to address this gap.Neural networks are utilized to handle uncertainty,and a unique coordinate transformation is employed to untangle the coupling between input delay and unknown control directions.Subsequently,a new auxiliary signal counters the impact of time-varying input delay,while a Nussbaum function is introduced to solve the problem of unknown control directions.The leverage of an advanced dynamic surface control technique avoids the“complexity explosion”and reduces boundary layer errors.Synthesizing these techniques ensures that all the closed-loop signals are semi-globally uniformly ultimately bounded(SGUUB),and the tracking error converges to a small region around the origin by selecting suitable parameters.Simulation examples are provided to demonstrate the feasibility of the proposed approach.
基金supported by the National Natural Science Foundation of China (9071602860974106)
文摘The control law design for a near-space hypersonic vehicle（NHV） is highly challenging due to its inherent nonlinearity,plant uncertainties and sensitivity to disturbances.This paper presents a novel functional link network（FLN） control method for an NHV with dynamical thrust and parameter uncertainties.The approach devises a new partially-feedback-functional-link-network（PFFLN） adaptive law and combines it with the nonlinear generalized predictive control（NGPC） algorithm.The PFFLN is employed for approximating uncertainties in flight.Its weights are online tuned based on Lyapunov stability theorem for the first time.The learning process does not need any offline training phase.Additionally,a robust controller with an adaptive gain is designed to offset the approximation error.Finally,simulation results show a satisfactory performance for the NHV attitude tracking,and also illustrate the controller＇s robustness.
基金supported by Naval Weapons and Equipment Pre-Research Project(Grant No.3020801010105).
文摘Based on fuzzy adaptive and dynamic surface(FADS),an integrated guidance and control(IGC)approach was proposed for large caliber naval gun guided projectile,which was robust to target maneuver,canard dynamic characteristics,and multiple constraints,such as impact angle,limited measurement of line of sight(LOS)angle rate and nonlinear saturation of canard deflection.Initially,a strict feedback cascade model of IGC in longitudinal plane was established,and extended state observer(ESO)was designed to estimate LOS angle rate and uncertain disturbances with unknown boundary inside and outside of system,including aerodynamic parameters perturbation,target maneuver and model errors.Secondly,aiming at zeroing LOS angle tracking error and LOS angle rate in finite time,a nonsingular terminal sliding mode(NTSM)was designed with adaptive exponential reaching law.Furthermore,combining with dynamic surface,which prevented the complex differential of virtual control laws,the fuzzy adaptive systems were designed to approximate observation errors of uncertain disturbances and to reduce chatter of control law.Finally,the adaptive Nussbaum gain function was introduced to compensate nonlinear saturation of canard deflection.The LOS angle tracking error and LOS angle rate were convergent in finite time and whole system states were uniform ultimately bounded,rigorously proven by Lyapunov stability theory.Hardware-in-the-loop simulation(HILS)and digital simulation experiments both showed FADS provided guided projectile with good guidance performance while striking targets with different maneuvering forms.
基金supported by the China Postdoctoral Science Foundation (200904501035 201003548)+3 种基金the National Natural Science Foundation of China (60835001907160289101600460804017)
文摘An adaptive integral dynamic surface control approach based on fully tuned radial basis function neural network （FTRBFNN） is presented for a general class of strict-feedback nonlinear systems,which may possess a wide class of uncertainties that are not linearly parameterized and do not have any prior knowledge of the bounding functions.FTRBFNN is employed to approximate the uncertainty online,and a systematic framework for adaptive controller design is given by dynamic surface control. The control algorithm has two outstanding features,namely,the neural network regulates the weights,width and center of Gaussian function simultaneously,which ensures the control system has perfect ability of restraining different unknown uncertainties and the integral term of tracking error introduced in the control law can eliminate the static error of the closed loop system effectively. As a result,high control precision can be achieved.All signals in the closed loop system can be guaranteed bounded by Lyapunov approach.Finally,simulation results demonstrate the validity of the control approach.
基金Supported by National Natural Science Foundation of China（60874044） Research Foundation for Key Disciplines of Beijing Municipal Commission of Education （XK100060422）
基金Supported by National High Technology Research and Development Program of China （863 Program） （2006AA04Z183）, National Nat- ural Science Foundation of China （60621001, 60534010, 60572070, 60774048, 60728307）, and the Program for Changjiang Scholars and Innovative Research Groups of China （60728307, 4031002）
基金Project(114601034)supported by the Scholarship Award for Excellent Doctoral Students Granted by the Ministry of Education of ChinaProject(61273158)supported by the National Natural Science Foundation of China
文摘An adaptive current compensation control for a single-sided linear induction motor(SLIM) with nonlinear disturbance observer was developed. First, to maintain t-axis secondary component flux constant with consideration of the specially dynamic eddy-effect(DEE) of the SLIM, a instantaneously tracing compensation of m-axis current component was analyzed. Second,adaptive current compensation based on Taylor-discretization algorithm was proposed. Third, an effective kind of nonlinear disturbance observer(NDOB) was employed to estimate and compensate the undesired load vibrations, then the robustness of the control system could be guaranteed. Experimental verification of the feasibility of the proposed method for an SLIM control system was performed, and it showed that the proposed adaptive compensation scheme with NDOB could significantly promote speed dynamical response and minimize speed ripple under the conditions of external load coupled vibrations and unavoidable feedback control variables measured errors, i.e., current and speed.
基金Project(60634020) supported by the National Natural Science Foundation of China
文摘Based on consideration of the differential relations between the immeasurable variables and measurable variables in electro-hydraulic servo system,adaptive dynamic recurrent fuzzy neural networks(ADRFNNs) were employed to identify the primary uncertainty and the mathematic model of the system was turned into an equivalent linear model with terms of secondary uncertainty.At the same time,gain adaptive sliding mode variable structure control(GASMVSC) was employed to synthesize the control effort.The results show that the unrealization problem caused by some system's immeasurable state variables in traditional fuzzy neural networks(TFNN) taking all state variables as its inputs is overcome.On the other hand,the identification by the ADRFNNs online with high accuracy and the adaptive function of the correction term's gain in the GASMVSC make the system possess strong robustness and improved steady accuracy,and the chattering phenomenon of the control effort is also suppressed effectively.
基金China Postdoctoral Science Foundation and Natural Science of Heibei Province!698004
文摘In this paper, an adaptive dynamic control scheme based on a fuzzy neural network is presented, that presents utilizes both feed-forward and feedback controller elements. The former of the two elements comprises a neural network with both identification and control role, and the latter is a fuzzy neural algorithm, which is introduced to provide additional control enhancement. The feedforward controller provides only coarse control, whereas the feedback controller can generate on-line conditional proposition rule automatically to improve the overall control action. These properties make the design very versatile and applicable to a range of industrial applications.
文摘In order to solve the problem of enhancing the vehicle driving stability and safety,which has been the hot question researched by scientific and engineering in the vehicle industry,the new control method was investigated.After the analysis of tire moving characteristics and the vehicle stress analysis,the tire model based on the extension pacejka magic formula which combined longitudinal motion and lateral motion was developed and a nonlinear vehicle dynamical stability model with seven freedoms was made.A new model reference adaptive control project which made the slip angle and yaw rate of vehicle body as the output and feedback variable in adjusting the torque of vehicle body to control the vehicle stability was designed.A simulation model was also built in Matlab/Simulink to evaluate this control project.It was made up of many mathematical subsystem models mainly including the tire model module,the yaw moment calculation module,the center of mass parameter calculation module,tire parameter calculation module of multiple and so forth.The severe lane change simulation result shows that this vehicle model and the model reference adaptive control method have an excellent performance.
文摘For a class of systems with unmodeled dynamics, robust adaptive stabilization problemis considered in this paper. Firstly, by a series of coordinate changes, the original system is re-parameterized. Then, by introducing a reduced-order observer, an error system is obtained. Basedon the system, a reduced-order adaptive backstepping controller design scheme is given. It is provedthat all the signals in the adaptive control system are globally uniformly bounded, and the regulationerror converges to zero asymptotically. Due to the order deduction of the controller, the design schemein this paper has more practical values. A simulation example further demonstrates the e?ciency ofthe control scheme.
文摘Adaptive optimization is one of the means that agile organization of command and control resource （AOC2R） adapts for the dynamic battlefield environment. A math model of the adaptive optimization of AOC2R is put forward by analyzing the interrelating concept and research. The model takes the adaptive process as a multi-stage decision making problem. The 2-phases method is presented to calculate the model, which obtains the related parameters by running the colored Petri net （CPN） model of AOC2R and then searches for the result by ant colony optimization （ACO） algorithm integrated with genetic optimization techniques. The simulation results demonstrate that the proposed algorithm greatly improves the performance of AOC2R.
