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
This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences withi...This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.展开更多
In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is base...In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed.First,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm.Then,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is constructed.It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.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
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
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
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
Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccu...Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.展开更多
With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess,the cloud control system(CCS)has exhibited formidable poten-tial in the realm of connected assisted dr...With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess,the cloud control system(CCS)has exhibited formidable poten-tial in the realm of connected assisted driving,such as the adap-tive cruise control(ACC).Based on the CCS architecture,this paper proposes a cloud-based predictive ACC(PACC)strategy,which fully considers the road slope information and the preced-ing vehicle status.In the cloud,based on the dynamic program-ming(DP),the long-term economic speed planning is carried out by using the slope information.At the vehicle side,the real-time fusion planning of the economic speed and the preceding vehi-cle state is realized based on the model predictive control(MPC),taking into account the safety and economy of driving.In order to ensure the safety and stability of the vehicle-cloud cooperative control system,an event-triggered cruise mode switching method is proposed based on the state of each sub-system of the vehicle-cloud-network-map.Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions.Moreover,under normal conditions,compared to the ACC system,the PACC sys-tem can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle,thus achiev-ing fuel savings of 3%to 8%.展开更多
Achieving accurate control of main steam temperature is a very difficult task in thermal power plants due to the large process lag （8 to 10 minutes） associated with the superheater system and there exists a deviatio...Achieving accurate control of main steam temperature is a very difficult task in thermal power plants due to the large process lag （8 to 10 minutes） associated with the superheater system and there exists a deviation of ±10 ℃ in closed loop control. A control oriented boiler model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. This paper offers a comprehensive integrated 8th order mathematical model for the boiler and a Kalman Filter based state predictive controller for effectively controlling the main steam temperature within ± 2 ℃ and to enhance the efficiency of the boiler. It is proved through simulation that the predictive controller method with Kalman filter state estimator and predictor is the most appropriate one for the optimization of main steam temperature control as compared to other methods. This control system is under field implementation in a 210 MW boiler of a thermal power plant.展开更多
This work is to develop a blending-based multiple model adaptive explicit predictive control scheme for nonlinear MiMo systems that can handle parametric uncertainties.Here,for each identification model,an explicit no...This work is to develop a blending-based multiple model adaptive explicit predictive control scheme for nonlinear MiMo systems that can handle parametric uncertainties.Here,for each identification model,an explicit nonlinear model predictive control(ENMPC)law is computed in advance for the corresponding model.The generated control inputs from the set of ENMPC controllers are being blended online using a weighting vector that is continuously updated by the proposed adaptive identification schemes.The proposed control scheme is used to govern the tracking of a highly nonlinear helicopter model known as the twin rotor MIMO system(TRMS).Here,an extended Kalman filter(EKF)is used to estimate the unavailable states of the TRMS.Finally,simulation and experimental results are presented to prove that the proposed controller gives better performance than some reported works in the literature.The effectiveness of the proposed controller is demonstrated by experimental studies of the TRMS model.展开更多
In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corr...In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.展开更多
Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the n...Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the novel model predictive control(MPC) for the yaw system of the wind turbines. The control horizon is adapted to the one with the best predictive performance among multiple control horizons. The adaptive MPC is demonstrated by simulations using real wind data, and its performance is compared with the baseline MPC at fixed control horizon. Results show that the adaptive MPC provides better comprehensive performance than the baseline ones at different preview time of wind directions. Therefore, the proposed adaptive technique is potentially useful for the wind turbines in the future.展开更多
This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackl...This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackle the challenges associated with scheduling large-scale mobile robots,an improved space-time multi-robot planning algorithm is proposed.The cloud servers are adopted in this algorithm for computation,which enables faster response to the planning requirements of large-scale mobile robots.Furthermore,enhancements to a model-free adaptive predictive control method are proposed to enhance the networked control effectiveness of the nonlinear robots.The algorithm's capability to accommodate conflict-free path planning for large-scale mobile robots is demonstrated through simulation results.Experimental findings further validate the effectiveness of the cloud-based large-scale mobile robot planning and control system in achieving both conflict-free path planning and accurate path tracking.This research holds substantial implications for enhancing logistics transportation efficiency and driving ad-vancements in the field of smart factories and intelligent warehouse logistics.展开更多
Enhancing the motion performance of wheeled biped robots amidst uncertain disturbances remains a challenge due to their under-actuated and inherently unstable nature.Aiming to address this issue,this paper proposes a ...Enhancing the motion performance of wheeled biped robots amidst uncertain disturbances remains a challenge due to their under-actuated and inherently unstable nature.Aiming to address this issue,this paper proposes a disturbance adaptive control framework for such robots.The framework introduces a disturbance variable to describe the comprehensive effect of disturbances due to environmental interactions on the robotic system.A Kalman filter is also employed to estimate the robot’s center of mass(CoM)state and the uncertain disturbances by leveraging the dynamic coupling intrinsic to the robots.Estimated results are then integrated into a nominal model predictive control framework to generate an optimal CoM trajectory over a finite time horizon.This approach enables the robot to adapt to various types of external disturbances in the sagittal plane while maintaining accurate velocity tracking.The efficacy of the proposed approach is validated by conducting experimental evaluations on a hydraulically driven wheeled biped robot.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“MOST”for the support under Grants No.MOST 104-2632-B-468-001,No.MOST 103-2221-E-468-009-MY2,No.MOST 104-2221-E-182-008-MY2,No.MOST 105-2221-E-468-009,No.MOST 106-2221-E-468-023,No.MOST 106-2221-E-182-033Chang Gung Memorial Hospital under Grant No.CMRPD2C0053
文摘This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.
基金Supported by National Natural Science Foundation of China(61873006,61673053)National Key Research and Development Project(2018YFC1602704,2018YFB1702704)。
文摘In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed.First,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm.Then,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is constructed.It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.
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.
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 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.
基金supported by the National Key Research and the Development Program of China(2022YFC3803700)the National Natural Science Foundation of China(52202391 and U20A20155).
文摘Obstacle detection and platoon control for mixed traffic flows,comprising human-driven vehicles(HDVs)and connected and autonomous vehicles(CAVs),face challenges from uncertain disturbances,such as sensor faults,inaccurate driver operations,and mismatched model errors.Furthermore,misleading sensing information or malicious attacks in vehicular wireless networks can jeopardize CAVs’perception and platoon safety.In this paper,we develop a two-dimensional robust control method for a mixed platoon,including a single leading CAV and multiple following HDVs that incorpo-rate robust information sensing and platoon control.To effectively detect and locate unknown obstacles ahead of the leading CAV,we propose a cooperative vehicle-infrastructure sensing scheme and integrate it with an adaptive model predictive control scheme for the leading CAV.This sensing scheme fuses information from multiple nodes while suppressing malicious data from attackers to enhance robustness and attack resilience in a distributed and adaptive manner.Additionally,we propose a distributed car-following control scheme with robustness to guarantee the following HDVs,considering uncertain disturbances.We also provide theoretical proof of the string stability under this control framework.Finally,extensive simulations are conducted to validate our approach.The simulation results demonstrate that our method can effectively filter out misleading sensing information from malicious attackers,significantly reduce the mean-square deviation in obstacle sensing,and approach the theoretical error lower bound.Moreover,the proposed control method successfully achieves obstacle avoidance for the mixed platoon while ensuring stability and robustness in the face of external attacks and uncertain disturbances.
基金supported by the National Key R&D Program of China(2021YFB2501000)the Consultancy Research Project on the Strategic Study of the Integration and Innovative Development of Intelligent Connected Vehicles and New Energy Ecology in Zhejiang Province(2023ZL0007)+1 种基金the Hetao Shenzhen-HongKong Science and Technology Innovation Cooperation Zone(HZQB-KCZYZ-2021055)the Open Project of the Key Laboratory of Modern Measurement and Control Technology of the Ministry of Education(KF2022-1123202).
文摘With the advantage of exceptional long-range traffic perception capabilities and data fusion computational prowess,the cloud control system(CCS)has exhibited formidable poten-tial in the realm of connected assisted driving,such as the adap-tive cruise control(ACC).Based on the CCS architecture,this paper proposes a cloud-based predictive ACC(PACC)strategy,which fully considers the road slope information and the preced-ing vehicle status.In the cloud,based on the dynamic program-ming(DP),the long-term economic speed planning is carried out by using the slope information.At the vehicle side,the real-time fusion planning of the economic speed and the preceding vehi-cle state is realized based on the model predictive control(MPC),taking into account the safety and economy of driving.In order to ensure the safety and stability of the vehicle-cloud cooperative control system,an event-triggered cruise mode switching method is proposed based on the state of each sub-system of the vehicle-cloud-network-map.Simulation results indicate that the PACC system can still ensure stable cruising under delays and some complex conditions.Moreover,under normal conditions,compared to the ACC system,the PACC sys-tem can further improve economy while ensuring safety and improve the overall energy efficiency of the vehicle,thus achiev-ing fuel savings of 3%to 8%.
文摘Achieving accurate control of main steam temperature is a very difficult task in thermal power plants due to the large process lag （8 to 10 minutes） associated with the superheater system and there exists a deviation of ±10 ℃ in closed loop control. A control oriented boiler model and an appropriate optimal control strategy are the essential tools for improving the accuracy of this control system. This paper offers a comprehensive integrated 8th order mathematical model for the boiler and a Kalman Filter based state predictive controller for effectively controlling the main steam temperature within ± 2 ℃ and to enhance the efficiency of the boiler. It is proved through simulation that the predictive controller method with Kalman filter state estimator and predictor is the most appropriate one for the optimization of main steam temperature control as compared to other methods. This control system is under field implementation in a 210 MW boiler of a thermal power plant.
文摘This work is to develop a blending-based multiple model adaptive explicit predictive control scheme for nonlinear MiMo systems that can handle parametric uncertainties.Here,for each identification model,an explicit nonlinear model predictive control(ENMPC)law is computed in advance for the corresponding model.The generated control inputs from the set of ENMPC controllers are being blended online using a weighting vector that is continuously updated by the proposed adaptive identification schemes.The proposed control scheme is used to govern the tracking of a highly nonlinear helicopter model known as the twin rotor MIMO system(TRMS).Here,an extended Kalman filter(EKF)is used to estimate the unavailable states of the TRMS.Finally,simulation and experimental results are presented to prove that the proposed controller gives better performance than some reported works in the literature.The effectiveness of the proposed controller is demonstrated by experimental studies of the TRMS model.
基金Supported by Program for New Century Excellent Talents In University(NCET-12-0049)Beijing Natural Science Foundation(4132034)
文摘In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.
基金supported by the National Natural Science Foundation of China (No. 61803393)the Natural Science Foundation of Hunan Province (No.2020JJ4751)+1 种基金the Innovation-Driven Project of Central South University (No.2020CX031)the Basic Science Research Program of Korea (No. NRF-2016R1A6A1A03013567)。
文摘Due to varying characteristics of the wind condition, the performance of the wind turbines can be optimized by adapting the parameters of the control system. In this letter, an adaptive technique is proposed for the novel model predictive control(MPC) for the yaw system of the wind turbines. The control horizon is adapted to the one with the best predictive performance among multiple control horizons. The adaptive MPC is demonstrated by simulations using real wind data, and its performance is compared with the baseline MPC at fixed control horizon. Results show that the adaptive MPC provides better comprehensive performance than the baseline ones at different preview time of wind directions. Therefore, the proposed adaptive technique is potentially useful for the wind turbines in the future.
基金supported in part by the Natural Science Foundation of Hunan Province(Grant 2023J110015)the Project of State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle(Grant 72275007)the National Natural Science Foundation of China(Grants 62403075 and Vehicle 62293513).
文摘This paper addresses a crucial challenge in the domain of smart factories and intelligent warehouse logistics,focusing on conflict-free planning and the smooth operation of large-scale nonlinear mobile robots.To tackle the challenges associated with scheduling large-scale mobile robots,an improved space-time multi-robot planning algorithm is proposed.The cloud servers are adopted in this algorithm for computation,which enables faster response to the planning requirements of large-scale mobile robots.Furthermore,enhancements to a model-free adaptive predictive control method are proposed to enhance the networked control effectiveness of the nonlinear robots.The algorithm's capability to accommodate conflict-free path planning for large-scale mobile robots is demonstrated through simulation results.Experimental findings further validate the effectiveness of the cloud-based large-scale mobile robot planning and control system in achieving both conflict-free path planning and accurate path tracking.This research holds substantial implications for enhancing logistics transportation efficiency and driving ad-vancements in the field of smart factories and intelligent warehouse logistics.
基金supported by the National Natural Science Foundation of China(Grant No.52405059)Heilongjiang Province Young Science and Technology Talent Lifting Project(Grant No.2023QNTJ008)+1 种基金the Natural Science Foundation of Heilongjiang Province of China(Grant No.YQ2024E017)Self-Planned Task of State Key Laboratory of Robotics and System(Harbin Institute of Technology,China)(Grant No.SKLRS202401C).
文摘Enhancing the motion performance of wheeled biped robots amidst uncertain disturbances remains a challenge due to their under-actuated and inherently unstable nature.Aiming to address this issue,this paper proposes a disturbance adaptive control framework for such robots.The framework introduces a disturbance variable to describe the comprehensive effect of disturbances due to environmental interactions on the robotic system.A Kalman filter is also employed to estimate the robot’s center of mass(CoM)state and the uncertain disturbances by leveraging the dynamic coupling intrinsic to the robots.Estimated results are then integrated into a nominal model predictive control framework to generate an optimal CoM trajectory over a finite time horizon.This approach enables the robot to adapt to various types of external disturbances in the sagittal plane while maintaining accurate velocity tracking.The efficacy of the proposed approach is validated by conducting experimental evaluations on a hydraulically driven wheeled biped robot.
