Dear Editor,This letter investigates a low-complexity data-driven adaptive proportional-integral-derivative(APID)control scheme to address the output tracking problem of a class of nonlinear systems.First,the relation...Dear Editor,This letter investigates a low-complexity data-driven adaptive proportional-integral-derivative(APID)control scheme to address the output tracking problem of a class of nonlinear systems.First,the relationship between PID parameters is established to reduce the number of adjustable parameters to one.Then,based on the incremental triangular data model,a data-driven APID tracking control(DD-APIDTC)method is proposed to adjust only one controller parameter and one model parameter online,both of which have clear physical meaning.Subsequently,sufficient conditions are derived for the boundedness of the system tracking error.Finally,simulation results are given to illustrate the effectiveness of the proposed method.展开更多
基金supported by the National Natural Science Foundation of China(62173002,62403010,52301408)the Beijing Natural Science Foundation(L241015,4222045)+1 种基金the Yuxiu Innovation Project of NCUT(2024NCUTYXCX111)the China Postdoctoral Science Foundation(2024M750192).
文摘Dear Editor,This letter investigates a low-complexity data-driven adaptive proportional-integral-derivative(APID)control scheme to address the output tracking problem of a class of nonlinear systems.First,the relationship between PID parameters is established to reduce the number of adjustable parameters to one.Then,based on the incremental triangular data model,a data-driven APID tracking control(DD-APIDTC)method is proposed to adjust only one controller parameter and one model parameter online,both of which have clear physical meaning.Subsequently,sufficient conditions are derived for the boundedness of the system tracking error.Finally,simulation results are given to illustrate the effectiveness of the proposed method.
