This paper presented a hybrid control scheme to vibration reduction of flexible spacecraft during rotational maneuver by using variable structure output feedback control （VSOFC） and piezoelectric materials. The cont...This paper presented a hybrid control scheme to vibration reduction of flexible spacecraft during rotational maneuver by using variable structure output feedback control （VSOFC） and piezoelectric materials. The control configuration included the attitude controller based on VSOFC method and vibration attenuator designed by constant-gain negative velocity feedback control. The attitude controller consisted of a linear feedback term and a discontinuous feedback term. With the presence of this attitude controller, an additional flexible control system acting on the flexible parts can be designed for vibration control. Compared with conventional proportional-derivative （PD） control, the developed control scheme guarantees not only the stability of the closed-loop system, but also yields better performance and robustness in the presence of parametric uncertainties and externai disturbance. Simulation results are presented for the spacecraft model to show the effectiveness of the proposed control techniques.展开更多
When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of...When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.展开更多
Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very la...Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.展开更多
基金Sponsored by Program for Young Excellent Talents in Harbin Institute of Technology(Grant No.HITQNJS.2007.001)National Natural Science Founda-tion of China(Grant No.60674101)Research Fund for the Doctoral Program of Higher Education of China(Grant No.20050213010).
文摘This paper presented a hybrid control scheme to vibration reduction of flexible spacecraft during rotational maneuver by using variable structure output feedback control （VSOFC） and piezoelectric materials. The control configuration included the attitude controller based on VSOFC method and vibration attenuator designed by constant-gain negative velocity feedback control. The attitude controller consisted of a linear feedback term and a discontinuous feedback term. With the presence of this attitude controller, an additional flexible control system acting on the flexible parts can be designed for vibration control. Compared with conventional proportional-derivative （PD） control, the developed control scheme guarantees not only the stability of the closed-loop system, but also yields better performance and robustness in the presence of parametric uncertainties and externai disturbance. Simulation results are presented for the spacecraft model to show the effectiveness of the proposed control techniques.
文摘When the parameters of the system change abruptly, a new multivariable adaptive feedforward decoupling controller using multiple models is presented to improve the transient response. The system models are composed of multiple fixed models, one free-running adaptive model and one re-initialized adaptive model. The fixed models are used to provide initial control to the process. The re-initialized adaptive model can be reinitialized as the selected model to improve the adaptation speed. The free-running adaptive controller is added to guarantee the overall system stability. At each instant, the best system model is selected according to the switching index and the corresponding controller is designed. During the controller design, the interaction is viewed as the measurable disturbance and eliminated by the choice of the weighting polynomial matrix. It not only eliminates the steady-state error but also decouples the system dynamically. The gtobel convergence is obtained and several simulation examples are presented to illustrate the effectiveness of the proposed controller.
文摘Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Traditional optimization methods cannot be directly applied due to memory restrictions. Up to now, several approaches exist for circumventing the above shortcomings and work well. Another learning algorithm, particle swarm optimization, for training SVM is introduted. The method is tested on UCI datasets.
