On the simple continuously variable transmission （CVT） driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-t...On the simple continuously variable transmission （CVT） driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.展开更多
Passive torque servo system (PTSS) simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturban...Passive torque servo system (PTSS) simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control (AFTC) algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno (T-S) fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator (ELS), and the comparative experimental results indicate that AFTC controller is effective for PTSS.展开更多
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric u...This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.展开更多
Level control in flotation columns is an important factor that influences the recovery and the grade of concentrate from the column. A flotation column is a nonlinear, multi-variable problem with changeable parameters...Level control in flotation columns is an important factor that influences the recovery and the grade of concentrate from the column. A flotation column is a nonlinear, multi-variable problem with changeable parameters that traditional methods have difficulty controlling. We have applied fuzzy control methods to the flotation column and tested the performance of the design by Matlab/Simulink simulation. The simulations show that level control in the flotation column becomes smoother and more rapid with the fuzzy controller. Compared to PID control methods the overshoot in valve position, the adjustment time, and the robustness of the controller are all improved. This indicates that it is suitable to model fuzzy controllers in applications for the study of automatic control of flotation column.展开更多
To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership functi...To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.展开更多
The measurement and control of the molten steel level are studied, which affect the quality of strip surface in strip casting. A system of molten steel measurement with a CCD (Charge Coupled Devices) sensor is designe...The measurement and control of the molten steel level are studied, which affect the quality of strip surface in strip casting. A system of molten steel measurement with a CCD (Charge Coupled Devices) sensor is designed, real-time measured data are given and its precision is analyzed. The level fluctuation model is derived, and an adaptive fuzzy-PID controller with supervisory control (AFPS) is proposed. The stability of the system is proved using Lyapunov theorem, and the simulation results are given when the model, the casting speed and the roll gap change. It is suggested that this kind of coupled nonlinear and time varying system is stable and robust using the designed AFPS controller.展开更多
The control model in the course of an aircraft auto-landing is first proposed. Then, the common basic hypotheses in the design of a fuzzy logic controller axe described. The fuzzy inference system of an aircraft auto-...The control model in the course of an aircraft auto-landing is first proposed. Then, the common basic hypotheses in the design of a fuzzy logic controller axe described. The fuzzy inference system of an aircraft auto-landing fuzzy controller in the course of automatic control on landing is investigated. The auto-landing model for controlling, membership functions of state variables, inference rules in the system, algorithms for fuzzy inference and defuzzification, etc, are analyzed and devised in detail with the emphasis on optimal analysis and design of Takagi-Sugeno ALFC based on adaptive neural fuzzy inference systems. Finally, the simulation for verification and analysis of the designed schemes is made by utilizing Simulink and fuzzy logic toolbox with MATLAB. The simulated results show that the ANFIS based T-S type ALFC scheme has excellent behavior in performance.展开更多
文摘On the simple continuously variable transmission （CVT） driveline model, the design of adaptive fuzzy control system for CVT vehicle is presented. The adaptive fuzzy control system consists of a scaling factor self-tuning fuzzy-PI throttle controller, and a hybrid fuzzy-PID CVT ratio and brake controller. The presented adaptive fuzzy control strategy is vehicle model independent, which depends only on the instantaneous vehicle states, but does not depend on vehicle parameters. So it has good robustness against uncertain vehicle parameters and exogenous load disturbance. Simulation results show that the proposed adaptive fuzzy strategy has good adaptability and practicality value.
基金National High-tech Research and Development Program of China (2009AA04Z412)"111" ProjectBUAA Fund of Graduate Education and Development
文摘Passive torque servo system (PTSS) simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control (AFTC) algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno (T-S) fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator (ELS), and the comparative experimental results indicate that AFTC controller is effective for PTSS.
基金This work was supported by the National Natural Science Foundation of China (No. 50375001)
文摘This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.
基金support from the Fundamental Research Funds for the Central Universitiesthe National Key Technology R & D Program in the 11th Five Year Plan of China (No. 2008BAB31B03)
文摘Level control in flotation columns is an important factor that influences the recovery and the grade of concentrate from the column. A flotation column is a nonlinear, multi-variable problem with changeable parameters that traditional methods have difficulty controlling. We have applied fuzzy control methods to the flotation column and tested the performance of the design by Matlab/Simulink simulation. The simulations show that level control in the flotation column becomes smoother and more rapid with the fuzzy controller. Compared to PID control methods the overshoot in valve position, the adjustment time, and the robustness of the controller are all improved. This indicates that it is suitable to model fuzzy controllers in applications for the study of automatic control of flotation column.
文摘To get simpler operation in modified fuzzy adaptive learning control network （FALCON） in some engineering application, sigmoid nonlinear function is employed as a substitute of traditional Gaussian membership function. For making the modified FALCON learning more efficient and stable, a simulated annealing （SA） learning coefficient is introduced into learning algorithm. At first, the basic concepts and main advantages of FALCON were briefly reviewed. Subsequently, the topological structure and nodes operation were illustrated; the gradient-descent learning algorithm with SA learning coefficient was derived; and the distinctions between the archetype and the modification were analyzed. Eventually, the significance and worthiness of the modified FALCON were validated by its application to probability prediction of anode effect in aluminium electrolysis cells.
文摘The measurement and control of the molten steel level are studied, which affect the quality of strip surface in strip casting. A system of molten steel measurement with a CCD (Charge Coupled Devices) sensor is designed, real-time measured data are given and its precision is analyzed. The level fluctuation model is derived, and an adaptive fuzzy-PID controller with supervisory control (AFPS) is proposed. The stability of the system is proved using Lyapunov theorem, and the simulation results are given when the model, the casting speed and the roll gap change. It is suggested that this kind of coupled nonlinear and time varying system is stable and robust using the designed AFPS controller.
基金This project was supported by the Defense Pre-Research Project of the Tenth Five-Year-Plan’of China (51406030104DZ0120) .
文摘The control model in the course of an aircraft auto-landing is first proposed. Then, the common basic hypotheses in the design of a fuzzy logic controller axe described. The fuzzy inference system of an aircraft auto-landing fuzzy controller in the course of automatic control on landing is investigated. The auto-landing model for controlling, membership functions of state variables, inference rules in the system, algorithms for fuzzy inference and defuzzification, etc, are analyzed and devised in detail with the emphasis on optimal analysis and design of Takagi-Sugeno ALFC based on adaptive neural fuzzy inference systems. Finally, the simulation for verification and analysis of the designed schemes is made by utilizing Simulink and fuzzy logic toolbox with MATLAB. The simulated results show that the ANFIS based T-S type ALFC scheme has excellent behavior in performance.
