In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to t...In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.展开更多
Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch ...Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,whic...To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.展开更多
The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain deg...The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.展开更多
A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predicti...A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.展开更多
To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch c...To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.展开更多
The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control des...The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control design strategies.The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.展开更多
An adaptive state feedback predictive control （SFPC） scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment...An adaptive state feedback predictive control （SFPC） scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.展开更多
This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher...This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.展开更多
Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predict...Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.展开更多
For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainab...For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.展开更多
The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control meth...The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.展开更多
In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive fun...In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.展开更多
In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a...In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.展开更多
Cascade control is one of the most popular structures for process control as it is a special architecture for dealing with disturbances. However, the drawbacks of cascade control are obvious that primary controller an...Cascade control is one of the most popular structures for process control as it is a special architecture for dealing with disturbances. However, the drawbacks of cascade control are obvious that primary controller and secondary controller should be tuned together, which influences each other. In this paper, a new Adaptive Cascade Generalized Predictive Controller (ACGPC) is introduced. ACGPC is a method issued from GPC and the inner and outer controllers of a cascade system are replaced by one cascade generalized predictive controller, where both loops model are updated by Recursive Least Squares method. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.展开更多
Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based I...Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC applications.However,applying constant design parameters cannot maintain consistent control effects in all states.Meanwhile,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high complexity.This means that any potential hardware/software faults will endanger the engine.Therefore,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking error.Thus,without introducing additional design parameters,an Adaptive Tracking Weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states effectively.Then,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure safety.The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump transition.The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the engine.The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full state.The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults,such as sensor/onboard adaptive model faults.The approach proposed is applicable to the design of a model-based engine intelligent control system.展开更多
The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil application...The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil applications.In this paper,we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator(VCO)equipped with a time to digital converter(TDC),which can significantly improve the frequency accuracy of the VCO thus calibrated.The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration.One advantage of this method is that the working frequency calibration employs the system history of input/output(I/O)data,instead of establishing an accurate VCO voltagecontrolled oscillator model.Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities.Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from±20 ppm to±10 ppb,which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.展开更多
Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This p...Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This paper presents a model-free adaptive control(MFAC) strategy to track the desired trajectory under different gravity environment. A dynamic transformation method and full form dynamic linearization(FFDL) approach are selected to dynamicly linearize the system, which can better eliminate the complex dynamics that may exist in the original system. The controlled object uses the two degrees of freedom of space manipulator and the controller only depends on the desired angle and torque of each joint of the space manipulator. Moreover, the proof of stability is also provided. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy. It is shown that the proposed approach can achieve better trajectory tracking performance under different gravity environment without changing the control parameters, and the tracking precision can be significantly improved as compared with the proportional differential(PD) control results.展开更多
This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay ...This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization(local-CFDL).To take advantage of the resulting structure,use a discrete-time extended state observer(DESO)to estimate the unknown residual factor.Then,according to the study,the inclusion of a time delay has no effect on the linearization structure,and an improved control approach is provided,in which DESO is used to adjust for uncertainties.Furthermore,a DESO-based event-triggered model-free adaptive control(ET-DESO-MFAC)is established by designing event-triggered conditions to assure Lyapunov stability.Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated;otherwise,the control input will stay the same as it is at the last trigger moment.A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking.Finally,simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.展开更多
基金supported in part by the National Natural Science Foundation of China(62403396,62433018,62373113)the Guangdong Basic and Applied Basic Research Foundation(2023A1515011527,2023B1515120010)the Postdoctoral Fellowship Program of CPSF(GZB20240621)
文摘In this paper, the containment control problem in nonlinear multi-agent systems(NMASs) under denial-of-service(DoS) attacks is addressed. Firstly, a prediction model is obtained using the broad learning technique to train historical data generated by the system offline without DoS attacks. Secondly, the dynamic linearization method is used to obtain the equivalent linearization model of NMASs. Then, a novel model-free adaptive predictive control(MFAPC) framework based on historical and online data generated by the system is proposed, which combines the trained prediction model with the model-free adaptive control method. The development of the MFAPC method motivates a much simpler robust predictive control solution that is convenient to use in the case of DoS attacks. Meanwhile, the MFAPC algorithm provides a unified predictive framework for solving consensus tracking and containment control problems. The boundedness of the containment error can be proven by using the contraction mapping principle and the mathematical induction method. Finally, the proposed MFAPC is assessed through comparative experiments.
基金supported by National Natural Science Foundation of China(Grant No.50776005 and 51577008)。
文摘Aiming at the problem that the existing model-based control strategy cannot fully reflect stochastic fluctuations of wind power,this paper presents a model-free adaptive predictive controller(MFAPC)for variable pitch systems with speed disturbance suppression.First,an improved small-world neural network with topology optimization is used for 15-second-ahead forecasting of wind speed,whose rolling time is 1s,and the predicted value serves as a feedforward to obtain the early compensation variation of the pitch angle.Second,a function of the multi-objective optimization at full wind speed with optimal power point tracking and minimum control variation is constructed,and an advanced one-step adaptive predictive control algorithm for wind power is proposed based on the online estimation and prediction of the time-varying pseudo partial derivative(PPD).In addition,the compound MFAPC framework is synthetically obtained,whose closed-loop effectiveness is verified by a BP-built pitch system based on the SCADA data with all working conditions.Robustness of the schemes has been analyzed in terms of parametric uncertainties and different operating conditions,and a detailed comparison is finally presented.The results show that the proposed MFAPC can not only effectively suppress the random disturbance of wind speed,but also meet the stability of wind power and the security of grid-connections for all operating conditions.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
文摘To solve the attitude trajectory tracking problem for hypersonic vehicles in the presence of system constraints and unknown disturbances,this paper designed a nonlinear robust model predictive control(RMPC)scheme,which can produce near-optimal tracking commands.Unlike the existing designs,the proposed scheme is less conservative and successfully prioritizes the solution optimality.The established RMPC follows a dualloop structure.Specifically,in the outer feedback loop,the reference attitude angle profiles are optimally tracked,while in the inner feedback loop,the control moment commands are produced by optimally tracking the desired angular rate trajectories.Besides,an adaptive disturbance observer(ADO)is designed and embedded in the inner and outer RMPC controllers to alleviate the negative effects caused by unknown external disturbances.The recursive feasibility of the optimization process,together with the input-to-state stability of the proposed RMPC,is theoretically guaranteed by introducing a tightened control constraint and terminal region.The derived property reveals that our proposal can steer the tracking error within a small region of convergence.Finally,the effectiveness of the proposed scheme is demonstrated by performing simulation studies.
基金Financial support was provided by the State Grid Sichuan Electric Power Company Science and Technology Project“Key Research on Development Path Planning and Key Operation Technologies of New Rural Electrification Construction”under Grant No.52199623000G.
文摘The increasing penetration of second-life battery energy storage systems(SLBESS)in power grids presents substantial challenges to system operation and control due to the heterogeneous characteristics and uncertain degradation patterns of repurposed batteries.This paper presents a novel model-free adaptive voltage controlembedded dung beetle-inspired heuristic optimization algorithmfor optimal SLBESS capacity configuration and power dispatch.To simultaneously address the computational complexity and ensure system stability,this paper develops a comprehensive bilevel optimization framework.At the upper level,a dung beetle optimization algorithmdetermines the optimal SLBESS capacity configuration byminimizing total lifecycle costswhile incorporating the charging/discharging power trajectories derived from the model-free adaptive voltage control strategy.At the lower level,a health-priority power dispatch optimization model intelligently allocates power demands among heterogeneous battery groups based on their real-time operational states,state-of-health variations,and degradation constraints.The proposed model-free approach circumvents the need for complex battery charging/discharging power controlmodels and extensive historical data requirements whilemaintaining system stability through adaptive controlmechanisms.A novel cycle life degradation model is developed to quantify the relationship between remaining useful life,depth of discharge,and operational patterns.The integrated framework enables simultaneous strategic planning and operational control,ensuring both economic efficiency and extended battery lifespan.The effectiveness of the proposed method is validated through comprehensive case studies on hybrid energy storage systems,demonstrating superior computational efficiency,robust performance across different network configurations,and significant improvements in battery utilization compared to conventional approaches.
基金Supported by the National Nature Science Foundation of China (90716028)~~
文摘A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.
文摘To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.
基金supported by the Deanship of Scientific Research(DSR)at KFUPM Through Distinguished Professorship Research(IN161065)
文摘The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control design strategies.The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.
基金the State Key Development Program for Basic Research of China(2002CB312200)the National High Technology Research and Development Program of China(2007AA04Z193)
文摘An adaptive state feedback predictive control （SFPC） scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.
文摘This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.
文摘Aiming at the time-varying characteristics of industrial process, this paper introduces an adaptive subspace predictive control(ASPC) strategy with time-varying forgetting factor based on the original subspace predictive control algorithm(SPC). The new method uses model matching error to calculate the variable forgetting factor, and applies it to constructing Hankel data matrix.This makes the data represent the changes of system information better. For eliminating the steady state error, the derivation of the incremental control is made. Simulation results on a rotary kiln show that this control strategy has achieved a good control effect.
基金Project supported by the National Hi-Tech Research and Develop-ment Program (863) of China (No. 2006AA11Z204)the Qianji-ang Program of Zhejiang Province (No. 2009R10008)
文摘For automated vehicles,comfortable driving will improve passengers’ satisfaction.Reducing fuel consumption brings economic profits for car owners,decreases the impact on the environment and increases energy sustainability.In addition to comfort and fuel-economy,automated vehicles also have the basic requirements of safety and car-following.For this purpose,an adaptive cruise control (ACC) algorithm with multi-objectives is proposed based on a model predictive control (MPC) framework.In the proposed ACC algorithm,safety is guaranteed by constraining the inter-distance within a safe range; the requirements of comfort and car-following are considered to be the performance criteria and some optimal reference trajectories are introduced to increase fuel-economy.The performances of the proposed ACC algorithm are simulated and analyzed in five representative traffic scenarios and multiple experiments.The results show that not only are safety and car-following objectives satisfied,but also driving comfort and fuel-economy are improved significantly.
文摘The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.
基金Project(2007AA04Z162) supported by the National High-Tech Research and Development Program of ChinaProjects(2006T089, 2009T062) supported by the University Innovation Team in the Educational Department of Liaoning Province, China
文摘In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.
基金supported in part by the National Natural Science Foundation of China(U1804147,61833001,61873139,61573129)the Innovative Scientists and Technicians Team of Henan Polytechnic University(T2019-2)the Innovative Scientists and Technicians Team of Henan Provincial High Education(20IRTSTHN019)。
文摘In this paper,an asymmetric bipartite consensus problem for the nonlinear multi-agent systems with cooperative and antagonistic interactions is studied under the event-triggered mechanism.For the agents described by a structurally balanced signed digraph,the asymmetric bipartite consensus objective is firstly defined,assigning the agents'output to different signs and module values.Considering with the completely unknown dynamics of the agents,a novel event-triggered model-free adaptive bipartite control protocol is designed based on the agents'triggered outputs and an equivalent compact form data model.By utilizing the Lyapunov analysis method,the threshold of the triggering condition is obtained.Subsequently,the asymptotic convergence of the tracking error is deduced and a sufficient condition is obtained based on the contraction mapping principle.Finally,the simulation example further demonstrates the effectiveness of the protocol.
文摘Cascade control is one of the most popular structures for process control as it is a special architecture for dealing with disturbances. However, the drawbacks of cascade control are obvious that primary controller and secondary controller should be tuned together, which influences each other. In this paper, a new Adaptive Cascade Generalized Predictive Controller (ACGPC) is introduced. ACGPC is a method issued from GPC and the inner and outer controllers of a cascade system are replaced by one cascade generalized predictive controller, where both loops model are updated by Recursive Least Squares method. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.
基金National Natural Science Foundation of China (Nos. 52176009, 51906103) for financial support
文摘Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation aeroengine.The latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC applications.However,applying constant design parameters cannot maintain consistent control effects in all states.Meanwhile,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high complexity.This means that any potential hardware/software faults will endanger the engine.Therefore,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking error.Thus,without introducing additional design parameters,an Adaptive Tracking Weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states effectively.Then,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure safety.The designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump transition.The PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the engine.The main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full state.The comparison results consistently indicate that the PBHC can guarantee engine safety in occurrence of hardware/software faults,such as sensor/onboard adaptive model faults.The approach proposed is applicable to the design of a model-based engine intelligent control system.
文摘The operating frequency accuracy of the local oscillators is critical for the overall system performance in the communication systems.However,the high-precision oscillators could be too expensive for civil applications.In this paper,we propose a model-free adaptive frequency calibration framework for a voltage-controlled crystal oscillator(VCO)equipped with a time to digital converter(TDC),which can significantly improve the frequency accuracy of the VCO thus calibrated.The idea is to utilize a high-precision TDC to directly measure the VCO period which is then passed to a model-free method for working frequency calibration.One advantage of this method is that the working frequency calibration employs the system history of input/output(I/O)data,instead of establishing an accurate VCO voltagecontrolled oscillator model.Another advantage is the lightweight calibration method with low complexity such that it can be implemented on an MCU with limited computation capabilities.Experimental results show that the proposed calibration method can improve the frequency accuracy of a VCO from±20 ppm to±10 ppb,which indicates the promise of the modelfree adaptive frequency calibrator for VCOs.
基金Sponsored by the National Natural Science Foundation of China(No.51605415)Natural Science Foundation of Hebei Province(No.F2016203494,E2017203240)。
文摘Due to the release of gravity in the space environment, the dynamic characteristics of the space manipulator have changed compared with that of the ground, which results in the change of its tracking precision. This paper presents a model-free adaptive control(MFAC) strategy to track the desired trajectory under different gravity environment. A dynamic transformation method and full form dynamic linearization(FFDL) approach are selected to dynamicly linearize the system, which can better eliminate the complex dynamics that may exist in the original system. The controlled object uses the two degrees of freedom of space manipulator and the controller only depends on the desired angle and torque of each joint of the space manipulator. Moreover, the proof of stability is also provided. Finally, simulation results are presented to demonstrate the effectiveness of the proposed strategy. It is shown that the proposed approach can achieve better trajectory tracking performance under different gravity environment without changing the control parameters, and the tracking precision can be significantly improved as compared with the proportional differential(PD) control results.
基金supported by the Natural Science Foundation of Jiangsu Province(BK20201159).
文摘This paper provides an improved model-free adaptive control(IMFAC)strategy for solving the surface vessel trajectory tracking issue with time delay and restricted disturbance.Firstly,the original nonlinear time-delay system is transformed into a structure consisting of an unknown residual term and a parameter term with control inputs using a local compact form dynamic linearization(local-CFDL).To take advantage of the resulting structure,use a discrete-time extended state observer(DESO)to estimate the unknown residual factor.Then,according to the study,the inclusion of a time delay has no effect on the linearization structure,and an improved control approach is provided,in which DESO is used to adjust for uncertainties.Furthermore,a DESO-based event-triggered model-free adaptive control(ET-DESO-MFAC)is established by designing event-triggered conditions to assure Lyapunov stability.Only when the system’s indicator fulfills the provided event-triggered condition will the control input signal be updated;otherwise,the control input will stay the same as it is at the last trigger moment.A coordinate compensation approach is developed to reduce the steady-state inaccuracy of trajectory tracking.Finally,simulation experiments are used to assess the effectiveness of the proposed technique for trajectory tracking.
