Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subs...Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subsystem of the affine coupled spatio-temporal chaos can be approximated by a set of fuzzy models; every fuzzy model represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Because the consequent parts of the fuzzy models have a constant bias term, it is very difficult to achieve tracking control for the affine system. Based on these fuzzy models, considering the affine constant bias term, an H∞ fuzzy tracking control scheme is proposed. A linear matrix inequality is employed to represent the feedback controller, and parameters of the controller are achieved by convex optimization techniques. The tracking control for the affine coupled spatio-temporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performances are testified by simulation examples.展开更多
This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno （T-S） fuzzy model is used to approximate the no...This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno （T-S） fuzzy model is used to approximate the nonlinear system in the synchronous d-q frame rotating with field-oriented control strategy. Then, a fuzzy state feedback controller is designed to reduce the tracking error by minimizing the disturbance level. The proposed controller is based on a T-S reference model in which the desired trajectory has been specified. The inaccessible rotor flux is estimated by a T-S fuzzy observer. The developed approach for the controller design is based on the synthesis of an augmented fuzzy model which regroups the model of induction machine, fuzzy observer, and reference model. The gains of the observer and controller are obtained by solving a set of linear matrix inequalities （LMIs）. Finally, simulation and experimental results are given to show the performance of the observer-based tracking controller.展开更多
Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） ...Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.展开更多
Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of g...Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control（FARC） strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative（PD） controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.展开更多
文摘Due to the interactions among coupled spatio-temporal subsystems and the constant bias term of affine chaos, it is difficult to achieve tracking control for the affine coupled spatiotemporal chaos. However, every subsystem of the affine coupled spatio-temporal chaos can be approximated by a set of fuzzy models; every fuzzy model represents a linearized model of the subsystem corresponding to the operating point of the controlled system. Because the consequent parts of the fuzzy models have a constant bias term, it is very difficult to achieve tracking control for the affine system. Based on these fuzzy models, considering the affine constant bias term, an H∞ fuzzy tracking control scheme is proposed. A linear matrix inequality is employed to represent the feedback controller, and parameters of the controller are achieved by convex optimization techniques. The tracking control for the affine coupled spatio-temporal chaos is achieved, and the stability of the system is also guaranteed. The tracking performances are testified by simulation examples.
基金supported by AECID Projects(Nos. A/023792/09,A/030410/10 and AP/034911/11)
文摘This paper deals with the synthesis of fuzzy controller applied to the induction motor with a guaranteed model reference tracking performance. First, the Takagi-Sugeno （T-S） fuzzy model is used to approximate the nonlinear system in the synchronous d-q frame rotating with field-oriented control strategy. Then, a fuzzy state feedback controller is designed to reduce the tracking error by minimizing the disturbance level. The proposed controller is based on a T-S reference model in which the desired trajectory has been specified. The inaccessible rotor flux is estimated by a T-S fuzzy observer. The developed approach for the controller design is based on the synthesis of an augmented fuzzy model which regroups the model of induction machine, fuzzy observer, and reference model. The gains of the observer and controller are obtained by solving a set of linear matrix inequalities （LMIs）. Finally, simulation and experimental results are given to show the performance of the observer-based tracking controller.
文摘Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.
基金supported by the National High-tech Research and Development Program of China
文摘Space robot is assembled and tested in gravity environment, and completes on-orbit service（OOS） in microgravity environment. The kinematic and dynamic characteristic of the robot will change with the variations of gravity in different working condition. Fully considering the change of kinematic and dynamic models caused by the change of gravity environment, a fuzzy adaptive robust control（FARC） strategy which is adaptive to these model variations is put forward for trajectory tracking control of space robot. A fuzzy algorithm is employed to approximate the nonlinear uncertainties in the model, adaptive laws of the parameters are constructed, and the approximation error is compensated by using a robust control algorithm. The stability of the control system is guaranteed based on the Lyapunov theory and the trajectory tracking control simulation is performed. The simulation results are compared with the proportional plus derivative（PD） controller, and the effectiveness to achieve better trajectory tracking performance under different gravity environment without changing the control parameters and the advantage of the proposed controller are verified.
