A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant i...A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.展开更多
The robust stabilization problem for a class of uncertain discrete-time switched systems is presented. A predictive sliding mode control strategy is proposed, and a discrete-time reaching law is improved. By applying ...The robust stabilization problem for a class of uncertain discrete-time switched systems is presented. A predictive sliding mode control strategy is proposed, and a discrete-time reaching law is improved. By applying a predictive sliding surface and a reference trajectory, combining with the state feedback correction and rolling optimization method in the predictive control strategy, a predictive sliding mode controller is synthesized, which guarantees the asymptotic stability for the closed-loop systems. The designed control strategy has stronger robustness and chattering reduction property to conquer with the system uncertainties. In addition, a unique nonswitched sliding surface is designed. The reason is to avoid the repetitive jump of the trajectories of the state components of the closed-loop system between sliding surfaces because it might cause the possible instability. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.展开更多
The integrated energy systems(IESs)offer a practical solution for achieving low-carbon targets in residential buildings.However,IES encounters several challenges related to increased energy consumption and costs due t...The integrated energy systems(IESs)offer a practical solution for achieving low-carbon targets in residential buildings.However,IES encounters several challenges related to increased energy consumption and costs due to fluctuations in renewable energy generation.Leveraging building flexibility to address these power fluctuations within IES is a promising strategy,which requires coordinated control between air-conditioning systems and other IES components.This study proposes a cross-time-scale control framework that contains optimal scheduling and on-the-fly flexible control to reduce the cost impacts of a residential IES system equipped with photovoltaic(PV)panels,batteries,a heat pump,and a domestic hot water tank.The method involves three key steps:solar irradiance prediction,day-ahead optimal scheduling of energy storage,and intra-day flexible control of the heat pump.The method is validated through a high-fidelity residential building model with actual weather and energy usage data in Frankfurt,Germany.Results reveal that the proposed method limits the cost increase to just 2.67% compared to the day-ahead schedule,whereas the cost could increase by 7.39% without the flexible control.Additionally,computational efficiency is enhanced by transforming the mixed-integer programming(MIP)into nonlinear programming(NLP)problem via introducing action-exclusive constraints.This approach offers valuable support for residential IES operations.展开更多
Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-s...Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results, we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.展开更多
The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the laneexchanging scenario.The nearby vehicle trajectory needs to be predicted,from which the autonomous vehi...The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the laneexchanging scenario.The nearby vehicle trajectory needs to be predicted,from which the autonomous vehicle is controlled to prevent possible collisions.This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control.A trajectory prediction method is developed to anticipate the nearby vehicle trajectory.The Gaussian mixture model(GMM),together with the vehicle kinematic model,are synthesized to predict the nearby vehicle trajectory.A potential-feldbased model predictive control(MPC)approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver.The potential feld of the nearby vehicle is considered in the controller design for collision avoidance.On-road driving data verifcation shows that the nearby vehicle trajectory can be predicted by the proposed method.CarSim®simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy.The autonomous vehicle can thus safely perform the laneexchanging maneuver and avoid the nearby vehicle.展开更多
Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainti...Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.展开更多
By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence...By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence of rock burst or depressingthe magnitude of rock burst was considered.The No.237 working face was selected asthe typical working face contacting gas in deep mining;aimed at this working face,a systemof rock burst prediction and control for coal seam contacting gas in deep mining wasestablished.This system includes three parts:① regional prediction of rock burst hazardbefore mining,② local prediction of rock burst hazard during mining,and ③ rock burstcontrol.展开更多
Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristi...Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristics of induction motors in high-speed operation are studied.A field weakening control method of induction motor based on model predictive control(MPC)algorithm is proposed,which can predict the future state of the controlled object,and then obtain the optimal control variables by colling optimization.The simulation results show that the field-weakening control method based on MPC algorithm has faster response speed,stronger robustness and better control performance than the traditional control methods.展开更多
This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of w...This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.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
This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hype...This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.展开更多
Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectiv...Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.展开更多
A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source di...A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.展开更多
Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive cont...Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.展开更多
Purpose–This study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives.The method is intended to optimize the output torque of each motor,maximize the utilizatio...Purpose–This study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives.The method is intended to optimize the output torque of each motor,maximize the utilization of train adhesion within the total torque command,reduce the train skidding/sliding phenomenon and achieve optimal adhesion utilization for each axle,thus realizing the optimal allocation of the multi-motor electric locomotives.Design/methodology/approach–In this study,a model predictive control(MPC)-based cooperative maximum adhesion tracking control method for multi-motor electric locomotives is presented.Firstly,train traction system with multiple motors is constructed in accordance with Newton’s second law.These equations include the train dynamics equations,the axle dynamics equations,and the wheel-rail adhesion coefficient equations.Then,a new MPC-based multi-axle adhesion co-optimization method is put forward.This method calculates the optimal output torque through real-time iteration based on the known reference slip speed to achieve multi-axle co-optimization under different circumstances.Findings–This paper presents a MPC system designed for the cooperative control of multi-axle adhesion.The results indicate that the proposed control system is able to optimize the adhesion of multiple axles under numerous different conditions and achieve the optimal power distribution based on the reduction of train skidding/sliding.Originality/value–This study presents a novel cooperative adhesion tracking control scheme.It is designed for multi-motor electric locomotives,which has rarely been studied before.And simulations are carried out in different conditions,including variable surfaces and motor failing.展开更多
Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluct...Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.展开更多
This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,t...This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,the optimization problem turns into an unconstrained,continuous,and differentiable form.An analytical two-step method is also proposed to solve the rest of the problem.In the first step,it is assumed that only input constraints are active and states are unconstrained.The optimal solution for this case is calculated directly with the optimality condition.The calculated control signal is revised in the second step according to system dynamics and state constraints.Simulation results of the auto-landing system show that the MPC computation speed is significantly increased by the new algebraic MPC(AMPC)without compromising the control performance,which makes the method realistic for using MPC in systems with high-speed changing dynamics.展开更多
In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the l...In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.展开更多
Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or...Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.展开更多
Due to errors in vehicle dynamics modeling,uncertainty in model parameters,and disturbances from curvature,the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature ...Due to errors in vehicle dynamics modeling,uncertainty in model parameters,and disturbances from curvature,the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature conditions.Therefore,a path tracking robust control strategy based on force-driven H_(∞)and MPC is proposed.To fully exploit the nonlinear dynamics characteristics of tires,a force-driven state space model of a path tracking system based on a linear time-varying tire model is established;the H_(∞)and MPC methods are used to design a robust controller.Considering disturbance and system state constraints,the robust control constraint model based on LMI is established.Finally,the proposed controller is validated through joint simulations using CarSim and MATLAB.The results show that the maximum lateral deviation is reduced by 17.07%,and the maximum course angle deviation is reduced by 13.04%under large curvature disturbance conditions.The maximum lateral deviation is reduced by 27.85%,and the maximum course angle deviation is reduced by 31.17%under conditions of uncertain road adhesion coefficients.Based on the controller’s performance,the proposed controller effectively mitigates modeling errors,parameter uncertainties,and curvature disturbances.展开更多
基金This work has been supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and the Teach and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,China.
文摘A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.
基金supported by the Youth Science and Innovation Foundation of Harbin(2007RFQXG052).
文摘The robust stabilization problem for a class of uncertain discrete-time switched systems is presented. A predictive sliding mode control strategy is proposed, and a discrete-time reaching law is improved. By applying a predictive sliding surface and a reference trajectory, combining with the state feedback correction and rolling optimization method in the predictive control strategy, a predictive sliding mode controller is synthesized, which guarantees the asymptotic stability for the closed-loop systems. The designed control strategy has stronger robustness and chattering reduction property to conquer with the system uncertainties. In addition, a unique nonswitched sliding surface is designed. The reason is to avoid the repetitive jump of the trajectories of the state components of the closed-loop system between sliding surfaces because it might cause the possible instability. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.
基金supported by the National Key Research and Development Program of China(2022YFB4200902)。
文摘The integrated energy systems(IESs)offer a practical solution for achieving low-carbon targets in residential buildings.However,IES encounters several challenges related to increased energy consumption and costs due to fluctuations in renewable energy generation.Leveraging building flexibility to address these power fluctuations within IES is a promising strategy,which requires coordinated control between air-conditioning systems and other IES components.This study proposes a cross-time-scale control framework that contains optimal scheduling and on-the-fly flexible control to reduce the cost impacts of a residential IES system equipped with photovoltaic(PV)panels,batteries,a heat pump,and a domestic hot water tank.The method involves three key steps:solar irradiance prediction,day-ahead optimal scheduling of energy storage,and intra-day flexible control of the heat pump.The method is validated through a high-fidelity residential building model with actual weather and energy usage data in Frankfurt,Germany.Results reveal that the proposed method limits the cost increase to just 2.67% compared to the day-ahead schedule,whereas the cost could increase by 7.39% without the flexible control.Additionally,computational efficiency is enhanced by transforming the mixed-integer programming(MIP)into nonlinear programming(NLP)problem via introducing action-exclusive constraints.This approach offers valuable support for residential IES operations.
文摘Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results, we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.
基金Supported by Project of National Natural Science Foundation of China(Grand No.52102469)Science and Technology Major Project of Guangxi(Grant Nos.AB21196029 and AA18242033)State Key Laboratory of Automotive Safety and Energy(Grant No.KF2014).
文摘The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the laneexchanging scenario.The nearby vehicle trajectory needs to be predicted,from which the autonomous vehicle is controlled to prevent possible collisions.This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control.A trajectory prediction method is developed to anticipate the nearby vehicle trajectory.The Gaussian mixture model(GMM),together with the vehicle kinematic model,are synthesized to predict the nearby vehicle trajectory.A potential-feldbased model predictive control(MPC)approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver.The potential feld of the nearby vehicle is considered in the controller design for collision avoidance.On-road driving data verifcation shows that the nearby vehicle trajectory can be predicted by the proposed method.CarSim®simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy.The autonomous vehicle can thus safely perform the laneexchanging maneuver and avoid the nearby vehicle.
基金supported by the National Key Research and Development Project of China(2018YFE0122200).
文摘Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.
基金Supported by the National Natural Science Foundation(Instrument)of China(50427401)the National High Technology Research and Development Program of China(2006AA06Z119)+1 种基金the National Key Technology R&D Program in 11th Five Years Plan of China(2007BA29B01)the New Century Excellent Talents in University(NCET-06-0477)
文摘By analyzing the characteristics and the production mechanism of rock burstthat goes with abnormal gas emission in deep coal seams,the essential method of eliminatingabnormal gas emission by eliminating the occurrence of rock burst or depressingthe magnitude of rock burst was considered.The No.237 working face was selected asthe typical working face contacting gas in deep mining;aimed at this working face,a systemof rock burst prediction and control for coal seam contacting gas in deep mining wasestablished.This system includes three parts:① regional prediction of rock burst hazardbefore mining,② local prediction of rock burst hazard during mining,and ③ rock burstcontrol.
基金National Natural Science Foundation of China(No.61663022)Changjiang Scholars and Innovaton Team Develpment Plan(No.Rt_16R36)。
文摘Considering the actual demand for high-speed operation of induction motors in industrial occasions,the characteristics of induction motors in different regions are analyzed,especially the field weakening characteristics of induction motors in high-speed operation are studied.A field weakening control method of induction motor based on model predictive control(MPC)algorithm is proposed,which can predict the future state of the controlled object,and then obtain the optimal control variables by colling optimization.The simulation results show that the field-weakening control method based on MPC algorithm has faster response speed,stronger robustness and better control performance than the traditional control methods.
基金the Key Research&Development Program of Xinjiang(Grant Number 2022B01003).
文摘This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.
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.
基金supported by the National Natural Science Foundation of China(12072090).
文摘This work proposes the application of an iterative learning model predictive control(ILMPC)approach based on an adaptive fault observer(FOBILMPC)for fault-tolerant control and trajectory tracking in air-breathing hypersonic vehicles.In order to increase the control amount,this online control legislation makes use of model predictive control(MPC)that is based on the concept of iterative learning control(ILC).By using offline data to decrease the linearized model’s faults,the strategy may effectively increase the robustness of the control system and guarantee that disturbances can be suppressed.An adaptive fault observer is created based on the suggested ILMPC approach in order to enhance overall fault tolerance by estimating and compensating for actuator disturbance and fault degree.During the derivation process,a linearized model of longitudinal dynamics is established.The suggested ILMPC approach is likely to be used in the design of hypersonic vehicle control systems since numerical simulations have demonstrated that it can decrease tracking error and speed up convergence when compared to the offline controller.
基金supported in part by the National Natural Science Foundation of China(62173255,62188101)Shenzhen Key Laboratory of Control Theory and Intelligent Systems(ZDSYS20220330161800001)
文摘Dear Editor,In this letter,a constrained networked predictive control strategy is proposed for the optimal control problem of complex nonlinear highorder fully actuated(HOFA)systems with noises.The method can effectively deal with nonlinearities,constraints,and noises in the system,optimize the performance metric,and present an upper bound on the stable output of the system.
基金co-supported by the National Key R&D Program of China(No.2023YFB4704400)the Zhejiang Provincial Natural Science Foundation of China(No.LQ24F030012)the National Natural Science Foundation of China General Project(No.62373033)。
文摘A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network(MTN)is presented for unmanned systems subject to time-delay and multi-source disturbances.First,the multi-source disturbances are addressed according to their specific characteristics as follows:(A)an MTN data-driven model,which is used for uncertainty description,is designed accompanied with the mechanism model to represent the unmanned systems;(B)an adaptive MTN filter is used to remove the influence of the internal disturbance;(C)an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance;(D)the Extended Kalman Filter(EKF)algorithm is utilized as the learning mechanism for MTNs.Second,to address the time-delay effect,a recursiveτstep-ahead MTN predictive model is designed utilizing recursive technology,aiming to mitigate the impact of time-delay,and the EKF algorithm is employed as its learning mechanism.Then,the MTN predictive control law is designed based on the quadratic performance index.By implementing the proposed composite controller to unmanned systems,simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted.Finally,the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem.Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.
文摘Fluctuating voltage levels in power grids necessitate automatic voltage regulators(AVRs)to ensure stability.This study examined the modeling and control of AVR in hydroelectric power plants using model predictive control(MPC),which utilizes an extensive mathe-matical model of the voltage regulation system to optimize the control actions over a defined prediction horizon.This predictive feature enables MPC to minimize voltage deviations while accounting for operational constraints,thereby improving stability and performance under dynamic conditions.Thefindings were compared with those derived from an optimal proportional integral derivative(PID)con-troller designed using the artificial bee colony(ABC)algorithm.Although the ABC-PID method adjusts the PID parameters based on historical data,it may be difficult to adapt to real-time changes in system dynamics under constraints.Comprehensive simulations assessed both frameworks,emphasizing performance metrics such as disturbance rejection,response to load changes,and resilience to uncertainties.The results show that both MPC and ABC-PID methods effectively achieved accurate voltage regulation;however,MPC excelled in controlling overshoot and settling time—recording 0.0%and 0.25 s,respectively.This demonstrates greater robustness compared to conventional control methods that optimize PID parameters based on performance criteria derived from actual system behavior,which exhibited settling times and overshoots exceeding 0.41 s and 5.0%,respectively.The controllers were implemented using MATLAB/Simulink software,indicating a significant advancement for power plant engineers pursuing state-of-the-art automatic voltage regulations.
基金supported by Scientific Research Projects of China Association of Metros(CAMET-KY-2022039)State Key Laboratory of Traction and Control System of EMU and Locomotive(2023YJ386).
文摘Purpose–This study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives.The method is intended to optimize the output torque of each motor,maximize the utilization of train adhesion within the total torque command,reduce the train skidding/sliding phenomenon and achieve optimal adhesion utilization for each axle,thus realizing the optimal allocation of the multi-motor electric locomotives.Design/methodology/approach–In this study,a model predictive control(MPC)-based cooperative maximum adhesion tracking control method for multi-motor electric locomotives is presented.Firstly,train traction system with multiple motors is constructed in accordance with Newton’s second law.These equations include the train dynamics equations,the axle dynamics equations,and the wheel-rail adhesion coefficient equations.Then,a new MPC-based multi-axle adhesion co-optimization method is put forward.This method calculates the optimal output torque through real-time iteration based on the known reference slip speed to achieve multi-axle co-optimization under different circumstances.Findings–This paper presents a MPC system designed for the cooperative control of multi-axle adhesion.The results indicate that the proposed control system is able to optimize the adhesion of multiple axles under numerous different conditions and achieve the optimal power distribution based on the reduction of train skidding/sliding.Originality/value–This study presents a novel cooperative adhesion tracking control scheme.It is designed for multi-motor electric locomotives,which has rarely been studied before.And simulations are carried out in different conditions,including variable surfaces and motor failing.
基金supported by the National Natural Science Foundation of China(Project No.52377082)the Scientific Research Program of Jilin Provincial Department of Education(Project No.JJKH20230123KJ).
文摘Large-scale new energy grid connection leads to the weakening of the system frequency regulation capability,and the system frequency stability is facing unprecedented challenges.In order to solve rapid frequency fluctuation caused by new energy units,this paper proposes a new energy power system frequency regulation strategy with multiple units including the doubly-fed pumped storage unit(DFPSU).Firstly,based on the model predictive control(MPC)theory,the state space equations are established by considering the operating characteristics of the units and the dynamic behavior of the system;secondly,the proportional-differential control link is introduced to minimize the frequency deviation to further optimize the frequency modulation(FM)output of the DFPSU and inhibit the rapid fluctuation of the frequency;lastly,it is verified on theMatlab/Simulink simulation platform,and the results show that the model predictive control with proportional-differential control link can further release the FM potential of the DFPSU,increase the depth of its FM,effectively reduce the frequency deviation of the system and its rate of change,realize the optimization of the active output of the DFPSU and that of other units,and improve the frequency response capability of the system.
文摘This article proposes an algebraic model predictive control(MPC)method for automatic landing.While defining the constraint functions in the optimization problem,the tangent hyperbolic function is preferred.Therefore,the optimization problem turns into an unconstrained,continuous,and differentiable form.An analytical two-step method is also proposed to solve the rest of the problem.In the first step,it is assumed that only input constraints are active and states are unconstrained.The optimal solution for this case is calculated directly with the optimality condition.The calculated control signal is revised in the second step according to system dynamics and state constraints.Simulation results of the auto-landing system show that the MPC computation speed is significantly increased by the new algebraic MPC(AMPC)without compromising the control performance,which makes the method realistic for using MPC in systems with high-speed changing dynamics.
文摘In this paper,a new study concerning the usage of artificial neural networks in the control application is given.It is shown,that the data gathered during proper operation of a given control plant can be used in the learning process to fully embrace the control pattern.Interestingly,the instances driven by neural networks have the ability to outperform the original analytically driven scenarios.Three different control schemes,namely perfect,linear-quadratic,and generalized predictive controllers were used in the theoretical study.In addition,the nonlinear recurrent neural network-based generalized predictive controller with the radial basis function-originated predictor was obtained to exemplify the main results of the paper regarding the real-world application.
文摘Numerous uncertainties in practical production and operation can seriously affect the drive performance of permanent magnet synchronous machines(PMSMs).Various robust control methods have been developed to mitigate or eliminate the effects of these uncertainties.However,the robustness to uncertainties of electrical drive systems has not been clearly defined.No systemic procedures have been proposed to evaluate a control system's robustness(how robust it is).This paper proposes a systemic method for evaluating control systems'robustness to uncertainties.The concept and fundamental theory of robust control are illustrated by considering a simple uncertain feedback control system.The effects of uncertainties on the control performance and stability are analyzed and discussed.The concept of design for six-sigma(a robust design method)is employed to numerically evaluate the robustness levels of control systems.To show the effectiveness of the proposed robustness evaluation method,case studies are conducted for second-order systems,DC motor drive systems,and PMSM drive systems.Besides the conventional predictive control of PMSM drive,three different robust predictive control methods are evaluated in terms of two different parametric uncertainty ranges and three application requirements against parametric uncertainties.
基金Supported by Qinghai University Youth Research Fund,China(Grant No.2023-QGY-15)。
文摘Due to errors in vehicle dynamics modeling,uncertainty in model parameters,and disturbances from curvature,the performance of the path tracking controller is poor or even unstable under high-speed and large-curvature conditions.Therefore,a path tracking robust control strategy based on force-driven H_(∞)and MPC is proposed.To fully exploit the nonlinear dynamics characteristics of tires,a force-driven state space model of a path tracking system based on a linear time-varying tire model is established;the H_(∞)and MPC methods are used to design a robust controller.Considering disturbance and system state constraints,the robust control constraint model based on LMI is established.Finally,the proposed controller is validated through joint simulations using CarSim and MATLAB.The results show that the maximum lateral deviation is reduced by 17.07%,and the maximum course angle deviation is reduced by 13.04%under large curvature disturbance conditions.The maximum lateral deviation is reduced by 27.85%,and the maximum course angle deviation is reduced by 31.17%under conditions of uncertain road adhesion coefficients.Based on the controller’s performance,the proposed controller effectively mitigates modeling errors,parameter uncertainties,and curvature disturbances.
