The main objective of the turboprop engine control system is to ensure propeller absorbed power at a constant propeller speed by controlling fuel flow and blade angle. Since each input variable affects the selected ou...The main objective of the turboprop engine control system is to ensure propeller absorbed power at a constant propeller speed by controlling fuel flow and blade angle. Since each input variable affects the selected output variables, there exist strong interactions between different control loops of a Two-Spool Turbo Prop Engine(TSTPE). Inverted decoupling is used to decouple the interactions and decompose the TSTPE into two independent single-input single-output systems. The multi-variable PI controller and two single-variable PI controllers are designed for the TSTPE with actuator dynamics based on Linear Matrix Inequality(LMI), respectively, which is derived from static output feedback and pole placement condition. The step responses show that due to the difference in the response times of the selected output variables, it is difficult to design an appropriate multi-variable PI controller. The designed single-variable PI controllers are tested on the TSTPE integrated model to illustrate the effectiveness of the proposed method, that is,the interactions are first decoupled and then the controllers are designed, and the resulting simulated responses show that compared with the controller designed without actuator dynamics, the gas-generator shaft speed and power turbine shaft speed can better track their respective commands under the action of the controller designed with actuator dynamics.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
Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet...Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet system, and based on some reasonable assumptions its nonlinear mathematical model, a MIMO coupling system, is derived. To realize the linearization and decoupling from the input to the output, the model is linearized exactly by means of feedback linearization, and an equivalent linear decoupling model is obtained. Based on the linear model, a nonlinear suspension controller is designed using state feedback. Simulations and experiments show that the controller can effectually solve the coupling problem in double-electromagnet suspension system.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
This paper investigates the stabilisation problem and consider transient optimisation for a class of the multi-input-multi-output(MIMO)semi-linear stochastic systems.A control algorithm is presented via an m-block bac...This paper investigates the stabilisation problem and consider transient optimisation for a class of the multi-input-multi-output(MIMO)semi-linear stochastic systems.A control algorithm is presented via an m-block backstepping controller design where the closed-loop system has been stabilized in a probabilistic sense and the transient performance is optimisable by optimised by searching the design parameters under the given criterion.In particular,the transient randomness and the probabilistic decoupling will be investigated as case studies.Note that the presented control algorithm can be potentially extended as a framework based on the various performance criteria.To evaluate the effectiveness of this proposed control framework,a numerical example is given with simulation results.In summary,the key contributions of this paper are stated as follows:1)one block backstepping-based output feedback control design is developed to stabilize the dynamic MIMO semi-linear stochastic systems using a linear estimator;2)the randomness and probabilistic couplings of the system outputs have been minimized based on the optimisation of the design parameters of the controller;3)a control framework with transient performance enhancement of multi-variable semi-linear stochastic systems has been discussed.展开更多
文摘The main objective of the turboprop engine control system is to ensure propeller absorbed power at a constant propeller speed by controlling fuel flow and blade angle. Since each input variable affects the selected output variables, there exist strong interactions between different control loops of a Two-Spool Turbo Prop Engine(TSTPE). Inverted decoupling is used to decouple the interactions and decompose the TSTPE into two independent single-input single-output systems. The multi-variable PI controller and two single-variable PI controllers are designed for the TSTPE with actuator dynamics based on Linear Matrix Inequality(LMI), respectively, which is derived from static output feedback and pole placement condition. The step responses show that due to the difference in the response times of the selected output variables, it is difficult to design an appropriate multi-variable PI controller. The designed single-variable PI controllers are tested on the TSTPE integrated model to illustrate the effectiveness of the proposed method, that is,the interactions are first decoupled and then the controllers are designed, and the resulting simulated responses show that compared with the controller designed without actuator dynamics, the gas-generator shaft speed and power turbine shaft speed can better track their respective commands under the action of the controller designed with actuator dynamics.
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Supported by National Natural Science Foundation of P. R. China (60404003)the Natural Science Foundation of Hunan Province (03JJY3108)Fok Ying-Tong Education Foundation (94028)
文摘Aiming at the coupling characteristic between the two groups of electromagnets embedded in the module of the maglev train, a nonlinear decoupling controller is designed. The module is modeled as a double-electromagnet system, and based on some reasonable assumptions its nonlinear mathematical model, a MIMO coupling system, is derived. To realize the linearization and decoupling from the input to the output, the model is linearized exactly by means of feedback linearization, and an equivalent linear decoupling model is obtained. Based on the linear model, a nonlinear suspension controller is designed using state feedback. Simulations and experiments show that the controller can effectually solve the coupling problem in double-electromagnet suspension system.
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 Higher Education Innovation Fund (No. HEIF 20182020), De Montfort University, Leicester, UK
文摘This paper investigates the stabilisation problem and consider transient optimisation for a class of the multi-input-multi-output(MIMO)semi-linear stochastic systems.A control algorithm is presented via an m-block backstepping controller design where the closed-loop system has been stabilized in a probabilistic sense and the transient performance is optimisable by optimised by searching the design parameters under the given criterion.In particular,the transient randomness and the probabilistic decoupling will be investigated as case studies.Note that the presented control algorithm can be potentially extended as a framework based on the various performance criteria.To evaluate the effectiveness of this proposed control framework,a numerical example is given with simulation results.In summary,the key contributions of this paper are stated as follows:1)one block backstepping-based output feedback control design is developed to stabilize the dynamic MIMO semi-linear stochastic systems using a linear estimator;2)the randomness and probabilistic couplings of the system outputs have been minimized based on the optimisation of the design parameters of the controller;3)a control framework with transient performance enhancement of multi-variable semi-linear stochastic systems has been discussed.
