Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This stu...Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.展开更多
The dynamics analysis plays an important role for the control, simulation and optimization of the parallel manipulators. Normally, the Stewart type manipulators have a platform and several legs. The inverse dynamics c...The dynamics analysis plays an important role for the control, simulation and optimization of the parallel manipulators. Normally, the Stewart type manipulators have a platform and several legs. The inverse dynamics can be solved efficiently by taking the advantage of such structural characteristics. However, for the forward dynamics analysis, this structural decomposition still faces challenges from both modeling and computation. In this paper, an efficient approach is proposed for the forward dynamics of the 6-PUS manipulator based on the platform-legs composite simulation. By composite method, the dynamics modeling of the parallel manipulator is separated into the forward dynamics of the platform and the kineto-statics of the legs. The global simulation model can be constructed by connecting the predefined platform model and leg models according to the manipulator＇s topology. Thus, the global simulation can be decomposed into the independent calculations of purely algebraic equations and ordinary differential equations （ODEs）, the computational cost can be reduced and the stability of the simulation can be improved. For the purpose of solving the manipulator＇s forward dynamics accurately, the algebraic-loop problem is discussed and a closed form algorithm is proposed. A numerical example of the 6-PUS manipulator is given to demonstrate the effectiveness of the proposed approach. The example results show that the modeling efficiency can be improved and the simulation stability can be ensured for decomposing the system equations into purely algebraic equations and ODEs.展开更多
The longitudinal dynamic flight stability of a bumblebee in forward flight is studied. The method of computational fluid dynamics is used to compute the aerodynamic derivatives and the techniques of eigenvalue and eig...The longitudinal dynamic flight stability of a bumblebee in forward flight is studied. The method of computational fluid dynamics is used to compute the aerodynamic derivatives and the techniques of eigenvalue and eigenvector analysis are employed for solving the equations of motion. The primary findings are as the following. The forward flight of the bumblebee is not dynamically stable due to the existence of one （or two） unstable or approximately neutrally stable natural modes of motion. At hovering to medium flight speed [flight speed Ue = （0-3.5）m s^-1; advance ratio J = 0-0.44], the flight is weakly unstable or approximately neutrally stable; at high speed （Ue = 4.5 m s^-1; J = 0.57）, the flight becomes strongly unstable （initial disturbance double its value in only 3.5 wingbeats）.展开更多
基金supported by the Malaysia Ministry of Higher Education under Fundamental Research Grant Scheme with Project Code:FRGS/1/2024/TK07/USM/02/3.
文摘Mobile service robots(MSRs)in hospital environments require precise and robust trajectory tracking to ensure reliable operation under dynamic conditions,including model uncertainties and external disturbances.This study presents a cognitive control strategy that integrates a Numerical Feedforward Inverse Dynamic Controller(NFIDC)with a Feedback Radial Basis Function Neural Network(FRBFNN).The robot’s mechanical structure was designed in SolidWorks 2022 SP2.0 and validated under operational loads using finite element analysis in ANSYS 2022 R1.The NFIDC-FRBFNN framework merges proactive inverse dynamic compensation with adaptive neural learning to achieve smooth torque responses and accurate motion control.A two-stage simulation evaluation was conducted.In the first stage,the controller was tested in a simulated hospital environment under both ideal and non-ideal conditions.In the second,it was benchmarked against four established controllers-Neural Network Model Reference Adaptive(NNMRA),Z-number Fuzzy Logic(Z-FL),Adaptive Dynamic Controller(ADC),and Fuzzy Logic-PID(FL-PID)—using circular and lemniscate trajectories.Across ten runs,the proposed controller achieved the lowest tracking errors under all conditions.Under ideal conditions,it achieved average improvements of 55.24%,75.75%,and 55.20%in integral absolute error(IAE),integral squared error(ISE),and mean absolute error(MAE),respectively,with coefficient of variation(CV)reductions above 55%.Under non-ideal conditions,average improvements exceeded 64%in IAE,77%in ISE,and 66%in MAE,while maintaining CV reductions above 57%.These results confirm that the NFIDC-FRBFNN controller offers superior accuracy,robustness,and consistency for real-time path tracking in healthcare robotics.
基金supported by National Natural Science Foundation of China (Grant No. 50605042)National Basic Research Program of China (973 Program, Grant No. 2006CB705400)
文摘The dynamics analysis plays an important role for the control, simulation and optimization of the parallel manipulators. Normally, the Stewart type manipulators have a platform and several legs. The inverse dynamics can be solved efficiently by taking the advantage of such structural characteristics. However, for the forward dynamics analysis, this structural decomposition still faces challenges from both modeling and computation. In this paper, an efficient approach is proposed for the forward dynamics of the 6-PUS manipulator based on the platform-legs composite simulation. By composite method, the dynamics modeling of the parallel manipulator is separated into the forward dynamics of the platform and the kineto-statics of the legs. The global simulation model can be constructed by connecting the predefined platform model and leg models according to the manipulator＇s topology. Thus, the global simulation can be decomposed into the independent calculations of purely algebraic equations and ordinary differential equations （ODEs）, the computational cost can be reduced and the stability of the simulation can be improved. For the purpose of solving the manipulator＇s forward dynamics accurately, the algebraic-loop problem is discussed and a closed form algorithm is proposed. A numerical example of the 6-PUS manipulator is given to demonstrate the effectiveness of the proposed approach. The example results show that the modeling efficiency can be improved and the simulation stability can be ensured for decomposing the system equations into purely algebraic equations and ODEs.
基金the National Natural Science Foundation of China (10732030)
文摘The longitudinal dynamic flight stability of a bumblebee in forward flight is studied. The method of computational fluid dynamics is used to compute the aerodynamic derivatives and the techniques of eigenvalue and eigenvector analysis are employed for solving the equations of motion. The primary findings are as the following. The forward flight of the bumblebee is not dynamically stable due to the existence of one （or two） unstable or approximately neutrally stable natural modes of motion. At hovering to medium flight speed [flight speed Ue = （0-3.5）m s^-1; advance ratio J = 0-0.44], the flight is weakly unstable or approximately neutrally stable; at high speed （Ue = 4.5 m s^-1; J = 0.57）, the flight becomes strongly unstable （initial disturbance double its value in only 3.5 wingbeats）.
