Through modifying the CPN model, a kind of multivariable fuzzy model is put forward, and the matching fuzzy multistep predictive control algorithm is deduced based on the model. The modified model works in a competiti...Through modifying the CPN model, a kind of multivariable fuzzy model is put forward, and the matching fuzzy multistep predictive control algorithm is deduced based on the model. The modified model works in a competitive output manner which results in its local representation property. While studying on line, only a few parameters need to be regulated. So the model has the merits of fast learning and on line self organizing modeling. The control algorithm is simple, adaptive and useful in multivariable and time delay systems. Applying the algorithm in a paper making system, simulation shows its good effect.展开更多
A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solvin...A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.展开更多
This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatl...This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.展开更多
A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and c...A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output (MIMO) plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output (SISO) generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.展开更多
The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the t...The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.展开更多
This paper firstly presents an algorithm of multivariable Nyquist predictivecontrol.Also,its relevant closed-feedback frame is proposed,by which the stability of thesystem can be immediately tested and the calculation...This paper firstly presents an algorithm of multivariable Nyquist predictivecontrol.Also,its relevant closed-feedback frame is proposed,by which the stability of thesystem can be immediately tested and the calculation of the algorithm is very convenient.Finally,the simulation of the predictive control algorithm to design a multivariable tem-perature control system for a 200MW thermal power unit is illustrated.The results of thesimulation show that the control system has good control performance.展开更多
A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross...A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output MIMO plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output SISO generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.展开更多
A simple delay-predictive continuous-time generalized predictive controller with filter (F-SDCGPC) is proposed. By using modified predictive output signal and cost function, the delay compensator is incorporated in th...A simple delay-predictive continuous-time generalized predictive controller with filter (F-SDCGPC) is proposed. By using modified predictive output signal and cost function, the delay compensator is incorporated in the control law with observer structure, and a filter is added for enhancing robustness. The design of filter does not affect the nominal set-point response, and it is more flexible than the design of observer polynomial. The analysis and simulation results show that the F-SDCGPC has better robustness than the observer structure without filter when large time-delay error is considered.展开更多
Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines.In terms of limit protection control,a novel model-based multivariable limit protection control method,whic...Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines.In terms of limit protection control,a novel model-based multivariable limit protection control method,which is achieved by adaptive command reconstruction and multiplecontrol loop selection and switch logic,is proposed in this paper to address the problem of balancing smaller thrust loss and safe operations by comparing with widely-used Min-Max logic.Five different combination modes of control loops,which represent the online control loop of last time instant and that of current time instant,is analyzed.Different command reconstructions are designed for these modes,which is based on static gain conversion of amplitude beyond limits by using an onboard model.The double-prediction based control loop selection and switch logic is developed to choose a control loop appropriately by comparing converted amplitude beyond limits regardless of one or more parameters tending to exceed limits.The proposed method is implemented in a twin-spool turbofan engine to achieve limit protection with direct thrust control,and the loss of thrust is improved by about 30% in comparison with the loss of thrust caused by Min-Max logic when limit protection control is activated,which demonstrates the effectiveness of the proposed method.展开更多
Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop...Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.展开更多
Rehearing furnace is an important device with complex dynamic characteristicsin steel plants. The temperature tracing control of reheating furnace has great importance both tothe quality of slabs and energy saving. A ...Rehearing furnace is an important device with complex dynamic characteristicsin steel plants. The temperature tracing control of reheating furnace has great importance both tothe quality of slabs and energy saving. A model-based control strategy, multivariable constrainedcontrol (MCC) for the reheating furnace control is used. With this control method, the furnace istreated as a six-input-six-output general model with loops coupled in nature. Compared with thetraditional control, the proposed control strategy gets better temperature tracing accuracy andexhibits some energy saving feature. The simulation results show that the performance of the furnaceis greatly improved.展开更多
Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to contr...Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.展开更多
The vast majority of today's agtering ayateas poas operational constsinta and have multiple inputa and outputa.This classifen them an Mults-Input Multi-Orutput(MIMO)ayn tena.This pape devekops a novel obeerver-has...The vast majority of today's agtering ayateas poas operational constsinta and have multiple inputa and outputa.This classifen them an Mults-Input Multi-Orutput(MIMO)ayn tena.This pape devekops a novel obeerver-hased fault diagnoas schene with the capability d simultaneoua state and actuator fault estimation for Linear Time-In ariant(LTI)MIMO aystenaa,which is then integrated with Model Predictive Control(MPC)method for achie ving fault-tolerant control.The application within this study is chosen to be the longitudinal flight control o a fixad-wing Unmanmed Aerial Vetücle(UAV).The oberver-based method is dom hüned with two MPC schemas to detect and compansate randomly oeeurring actuator faults in real time.The faults are modeled asa Lans Of Efkctiveess(LOE).For the first(dfident)MPCmethod,a simpke remnniguration can be perkormed in the esent of faulta,as it is based on a abaolute Input foemmlation.Howeve,as the seeond(integrd-action)MPC is based on a incamen tal input formulation,rconfiguration is notrequired,sinee this algorithm has°rc of implicst fault tokeranee.Numerfcal simulationa danstrate the afetivens of the panposed approach for both MPC sebemes.展开更多
文摘Through modifying the CPN model, a kind of multivariable fuzzy model is put forward, and the matching fuzzy multistep predictive control algorithm is deduced based on the model. The modified model works in a competitive output manner which results in its local representation property. While studying on line, only a few parameters need to be regulated. So the model has the merits of fast learning and on line self organizing modeling. The control algorithm is simple, adaptive and useful in multivariable and time delay systems. Applying the algorithm in a paper making system, simulation shows its good effect.
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in University of China (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013).
文摘A constrained decoupling (generalized predictive control) GPC algorithm is proposed for MIMO (malti-input multi-output) system. This algorithm takes account of all constraints of inputs and their increments. By solving matrix equations, the multi-step predictive decoupling controllers are realized. This algorithm need not solve Diophantine functions, and weakens the cross-coupling of the variables. At last the simulation results demon- strate the effectiveness of this proposed strategy.
文摘This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.
基金Supported by the National Natural Science Foundation of China (No.60374037, No.60574036), the Program for New Century Excellent Talents in Education Ministry (NCET), and the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20050055013).
文摘A novel method of incorporating generalized predictive control (GPC) algorithms based on quantitative feedback theory (QFT) principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output (MIMO) plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output (SISO) generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.
基金Project(61203021)supported by the National Natural Science Foundation of ChinaProject(2011216011)supported by the Scientific and Technological Program of Liaoning Province,China+2 种基金Project(2013020024)supported by the Natural Science Foundation of Liaoning Province,ChinaProject(2012BAF05B00)supported by the National Science and Technology Support Program,ChinaProject(LJQ2015061)supported by the Program for Liaoning Excellent Talents in Universities,China
文摘The control of gas fractionation unit(GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay.PID controllers are still applied in most industry processes.However,the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process.In this work,an incremental multivariable predictive functional control(IMPFC) algorithm was proposed with less online computation,great precision and fast response.An incremental transfer function matrix model was set up through the step-response data,and predictive outputs were deduced with the theory of single-value optimization.The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm,and thereby making the control variable smoother.The predictive output error and future set-point were approximated by a polynomial,which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory.Then,the design of incremental multivariable predictive functional control was studied.Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.
文摘This paper firstly presents an algorithm of multivariable Nyquist predictivecontrol.Also,its relevant closed-feedback frame is proposed,by which the stability of thesystem can be immediately tested and the calculation of the algorithm is very convenient.Finally,the simulation of the predictive control algorithm to design a multivariable tem-perature control system for a 200MW thermal power unit is illustrated.The results of thesimulation show that the control system has good control performance.
基金the National Natural Science Foundation of China (No.60374037, No.60574036)the Program for New CenturyExcellent Talents in Education Ministry (NCET)the Specialized Research Fund for the Doctoral Program of Higher Edu-cation of China (No.20050055013)
文摘A novel method of incorporating generalized predictive control GPC algorithms based on quantitative feedback theory QFT principles is proposed for solving the feedback control problem of the highly uncertain and cross-coupling plants. The quantitative feedback theory decouples the multi-input and multi-output MIMO plant and is also used to reduce the uncertainties of the system, stabilize the system, and achieve tracking performance of the system to a certain extent. Single-input and single-output SISO generalized predictive control is used to achieve performance with higher performance. In GPC, the model is identified on-line, which is based on the QFT input and the plant output signals. The simulation results show that the performance of the system is superior to the performance when only QFT is used for highly uncertain MIMO plants.
基金Supported by the National Natural Science Foundation of China (No.60774080)the Common Project Plan of Beijing Municipal Education Commission (No.100100435)
文摘A simple delay-predictive continuous-time generalized predictive controller with filter (F-SDCGPC) is proposed. By using modified predictive output signal and cost function, the delay compensator is incorporated in the control law with observer structure, and a filter is added for enhancing robustness. The design of filter does not affect the nominal set-point response, and it is more flexible than the design of observer polynomial. The analysis and simulation results show that the F-SDCGPC has better robustness than the observer structure without filter when large time-delay error is considered.
基金supported by China Scholarship Council(No.201906830081)。
文摘Control technologies are innovated to satisfy increasingly complicated control demands of gas turbine engines.In terms of limit protection control,a novel model-based multivariable limit protection control method,which is achieved by adaptive command reconstruction and multiplecontrol loop selection and switch logic,is proposed in this paper to address the problem of balancing smaller thrust loss and safe operations by comparing with widely-used Min-Max logic.Five different combination modes of control loops,which represent the online control loop of last time instant and that of current time instant,is analyzed.Different command reconstructions are designed for these modes,which is based on static gain conversion of amplitude beyond limits by using an onboard model.The double-prediction based control loop selection and switch logic is developed to choose a control loop appropriately by comparing converted amplitude beyond limits regardless of one or more parameters tending to exceed limits.The proposed method is implemented in a twin-spool turbofan engine to achieve limit protection with direct thrust control,and the loss of thrust is improved by about 30% in comparison with the loss of thrust caused by Min-Max logic when limit protection control is activated,which demonstrates the effectiveness of the proposed method.
文摘Design of general multivariable process controllers is an attractive and practical alternative to optimizing design by evolutionary algorithms (EAs) since it can be formulated as an optimization problem. A closed-loop particle swarm optimization (CLPSO) algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories. At each time step, a proportional integral (PI) controller is used to calculate an updated inertia weight for each particle in swarms from its last fitness. With this modification, limitations caused by a uniform inertia weight for the whole population are avoided, and the particles have enough diversity. After the effectiveness, efficiency and robustness are tested by benchmark functions, CLPSO is applied to design a multivariable proportional-integral-derivative (PID) controller for a solvent dehydration tower in a chemical plant and has improved its performances.
基金This work was supported by a grant from State 863 High Technology R&D Project of China (No. 2001 AA413130) National Natural Science Foundation of China (No.60004001).]
文摘Rehearing furnace is an important device with complex dynamic characteristicsin steel plants. The temperature tracing control of reheating furnace has great importance both tothe quality of slabs and energy saving. A model-based control strategy, multivariable constrainedcontrol (MCC) for the reheating furnace control is used. With this control method, the furnace istreated as a six-input-six-output general model with loops coupled in nature. Compared with thetraditional control, the proposed control strategy gets better temperature tracing accuracy andexhibits some energy saving feature. The simulation results show that the performance of the furnaceis greatly improved.
基金The authors thank the MOE AcRF Grant in Singapore for financial support of the projects on Precision Healthcare Development,Manufacturing and Supply Chain Optimization(Grant No.R-279-000-513-133)Advanced Process Control and Machine Learning Methods for Enhanced Continuous Manufacturing of Pharmaceutical Products(Grant No.R-279-000-541-114).
文摘Advanced model-based control strategies,e.g.,model predictive control,can offer superior control of key process variables for multiple-input multiple-output systems.The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization.This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control.To showcase this approach,five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system.This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges.These controllers also had reasonable per-iteration times of ca.0.1 s.This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which,in the face of process uncertainties or modelling limitations,allow rapid and stable control over wider operating ranges.
基金supported by the Concordia Graduate Scholarship in Natural Sciences and Engineering and the Natural Sciences and Engineering Research Council of Canada.
文摘The vast majority of today's agtering ayateas poas operational constsinta and have multiple inputa and outputa.This classifen them an Mults-Input Multi-Orutput(MIMO)ayn tena.This pape devekops a novel obeerver-hased fault diagnoas schene with the capability d simultaneoua state and actuator fault estimation for Linear Time-In ariant(LTI)MIMO aystenaa,which is then integrated with Model Predictive Control(MPC)method for achie ving fault-tolerant control.The application within this study is chosen to be the longitudinal flight control o a fixad-wing Unmanmed Aerial Vetücle(UAV).The oberver-based method is dom hüned with two MPC schemas to detect and compansate randomly oeeurring actuator faults in real time.The faults are modeled asa Lans Of Efkctiveess(LOE).For the first(dfident)MPCmethod,a simpke remnniguration can be perkormed in the esent of faulta,as it is based on a abaolute Input foemmlation.Howeve,as the seeond(integrd-action)MPC is based on a incamen tal input formulation,rconfiguration is notrequired,sinee this algorithm has°rc of implicst fault tokeranee.Numerfcal simulationa danstrate the afetivens of the panposed approach for both MPC sebemes.
