Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with param...Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter variations.CGAC is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay.Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC.As a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance.The new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time delay.Thus,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.展开更多
A new limit protection method based on Scheduling Command Governor(SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller(GSC) is designed for...A new limit protection method based on Scheduling Command Governor(SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller(GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities(LMIs). The SCG is an add-on control scheme which manages engine limits effectively based on reference trajectory optimization. Unlike the traditional min–max architecture with switching logic, the SCG method utilizes the Linear Parameter Varying(LPV) closed-loop model to form a prediction of future constraint violation and per instant solves a constraint-admissible reference within an approximate Maximal Output Admissible Set(MOAS).The influence of the variation of engine dynamic characteristics and equilibrium points during transient state control is handled by the design of contractive sets. Simulation results on a turbofan engine component-level model show the applicability and effectiveness of the SCG method. Compared to the traditional min–max method, the SCG method has less conservativeness. In addition,the design of contractive sets makes conservativeness tunable.展开更多
文摘Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter variations.CGAC is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay.Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC.As a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance.The new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time delay.Thus,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.
基金supported by National Science and Technology Major Project of China(No.2017-V-0004-0054)。
文摘A new limit protection method based on Scheduling Command Governor(SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller(GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities(LMIs). The SCG is an add-on control scheme which manages engine limits effectively based on reference trajectory optimization. Unlike the traditional min–max architecture with switching logic, the SCG method utilizes the Linear Parameter Varying(LPV) closed-loop model to form a prediction of future constraint violation and per instant solves a constraint-admissible reference within an approximate Maximal Output Admissible Set(MOAS).The influence of the variation of engine dynamic characteristics and equilibrium points during transient state control is handled by the design of contractive sets. Simulation results on a turbofan engine component-level model show the applicability and effectiveness of the SCG method. Compared to the traditional min–max method, the SCG method has less conservativeness. In addition,the design of contractive sets makes conservativeness tunable.
