This paper investigates a switching control strategy for the altitude motion of a morphing aircraft with variable sweep wings based on Q-learning.The morphing process is regarded as a function of the system states and...This paper investigates a switching control strategy for the altitude motion of a morphing aircraft with variable sweep wings based on Q-learning.The morphing process is regarded as a function of the system states and a related altitude motion model is established.Then,the designed controller is divided into the outer part and inner part,where the outer part is devised by a combination of the back-stepping method and command filter technique so that the’explosion of complexity’problem is eliminated.Moreover,the integrator structure of the altitude motion model is exploited to simplify the back-stepping design,and disturbance observers inspired from the idea of extended state observer are devised to obtain estimations of the system disturbances.The control input switches from the outer part to the inner part when the altitude tracking error converges to a small value and linear approximation of the altitude motion model is applied.The inner part is generated by the Q-learning algorithm which learns the optimal command in the presence of unknown system matrices and disturbances.It is proved rigorously that all signals of the closed-loop system stay bounded by the developed control method and controller switching occurs only once.Finally,comparative simulations are conducted to validate improved control performance of the proposed scheme.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61873295,61833016)the Aeronautical Science Foundation of China(No.2016ZA51011).
文摘This paper investigates a switching control strategy for the altitude motion of a morphing aircraft with variable sweep wings based on Q-learning.The morphing process is regarded as a function of the system states and a related altitude motion model is established.Then,the designed controller is divided into the outer part and inner part,where the outer part is devised by a combination of the back-stepping method and command filter technique so that the’explosion of complexity’problem is eliminated.Moreover,the integrator structure of the altitude motion model is exploited to simplify the back-stepping design,and disturbance observers inspired from the idea of extended state observer are devised to obtain estimations of the system disturbances.The control input switches from the outer part to the inner part when the altitude tracking error converges to a small value and linear approximation of the altitude motion model is applied.The inner part is generated by the Q-learning algorithm which learns the optimal command in the presence of unknown system matrices and disturbances.It is proved rigorously that all signals of the closed-loop system stay bounded by the developed control method and controller switching occurs only once.Finally,comparative simulations are conducted to validate improved control performance of the proposed scheme.
