A cooperative navigation algorithm for a group of autonomous underwater vehicles is proposed on the basis of motion radius vector estimation.Combined the dead reckoning data with the mutual range data through an acous...A cooperative navigation algorithm for a group of autonomous underwater vehicles is proposed on the basis of motion radius vector estimation.Combined the dead reckoning data with the mutual range data through an acoustic communication network among the group members, the relative positioning problem can be solved. A novel approach for solving the relative positioning is presented by using a recursive trigonometry technique and extended Kalman filter(EKF). Simulation results verify the correctness and effectiveness of this navigation method.展开更多
Purpose–The purpose of this paper is to propose a robust control scheme for near space vehicle’s(NSV’s)reentry attitude tracking problem under aerodynamic parameter variations and external disturbances.Design/metho...Purpose–The purpose of this paper is to propose a robust control scheme for near space vehicle’s(NSV’s)reentry attitude tracking problem under aerodynamic parameter variations and external disturbances.Design/methodology/approach-The robust control scheme is composed of dynamic surface control(DSC)and least squares support vector machines(LS-SVM).DSC is used to design a nonlinear controller for HSV;then,to increase the robustness and improve the control performance of the controller.LS-SVM is presented to estimate the lumped uncertainties,including aerodynamic parameter variations and external disturbances.The stability analysis shows that all closed-loop signals are bounded,with output tracking error and estimate error of LS-SVM weights exponentially converging to small compacts.Findings-Simulation results demonstrate that the proposed method is effective,leading to promising performance.Originality/value-First,a robust control scheme composed of DSC and adaptive LS-SVM is proposed for NSV’s reentry attitude tracking problem under aerodynamic parameter variations and external disturbances;second,the proposed method can achieve more favorable tracking performances than conventional dynamic surface control because of employing LS-SVM to estimate aerodynamic parameter variations and external disturbances.展开更多
Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough ...Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.展开更多
基金Sponsored by National Natural Foundation (50979093)the High Technology Research and Development Program of China (863 Program)( 2007AA809502C)Program for New Century Excellent Talents in University (NCET-06-0877)
文摘A cooperative navigation algorithm for a group of autonomous underwater vehicles is proposed on the basis of motion radius vector estimation.Combined the dead reckoning data with the mutual range data through an acoustic communication network among the group members, the relative positioning problem can be solved. A novel approach for solving the relative positioning is presented by using a recursive trigonometry technique and extended Kalman filter(EKF). Simulation results verify the correctness and effectiveness of this navigation method.
文摘Purpose–The purpose of this paper is to propose a robust control scheme for near space vehicle’s(NSV’s)reentry attitude tracking problem under aerodynamic parameter variations and external disturbances.Design/methodology/approach-The robust control scheme is composed of dynamic surface control(DSC)and least squares support vector machines(LS-SVM).DSC is used to design a nonlinear controller for HSV;then,to increase the robustness and improve the control performance of the controller.LS-SVM is presented to estimate the lumped uncertainties,including aerodynamic parameter variations and external disturbances.The stability analysis shows that all closed-loop signals are bounded,with output tracking error and estimate error of LS-SVM weights exponentially converging to small compacts.Findings-Simulation results demonstrate that the proposed method is effective,leading to promising performance.Originality/value-First,a robust control scheme composed of DSC and adaptive LS-SVM is proposed for NSV’s reentry attitude tracking problem under aerodynamic parameter variations and external disturbances;second,the proposed method can achieve more favorable tracking performances than conventional dynamic surface control because of employing LS-SVM to estimate aerodynamic parameter variations and external disturbances.
文摘Aimed at uncertainties and model's impreciseness, nonlinearity and time-variability of depth control system in autonomous underwater vehicle (AUV), a depth predictive control method was put forward based on rough set (RS) and least squares support vector machine (LSSVM). By using RS theory, the monitor data attribute of AUV was reduced to eliminate the redundant information and to improve efficiency. Then, LSSVM model was trained by using the reduced rules, and its parameters were optimized by using chaos theory for the higher accurate control. Taken an AUV typed NPS Phoenix as an example, its depth step response, horizontal rudder and pitch change were simulated. The simulation results show that the method improves the model's accuracy and has better real-time response, fault-tolerant ability, reliability and strong anti-interfere capability.
