Dynamic models play an important role in robot control and applications.The accurate identification of dynamic models has become crucial to meeting increasing performance requirements.Owing to the inertial forces and ...Dynamic models play an important role in robot control and applications.The accurate identification of dynamic models has become crucial to meeting increasing performance requirements.Owing to the inertial forces and the joint frictions coupling,the identification first requires a parametrized friction model.However,the joint frictions are strongly nonlinear and vary with many factors including posture,velocity and temperature.Hence,all friction models have some deviation from the real values,which reduces the identification accuracy.This paper proposes an identification approach using a baseplate force sensor.It identifies the inertial parameters first and then computes the joint friction values by subtracting the inertial torques from the joint torques.This method has the advantage that it does not require a priori friction model.It can choose or construct a proper model to fit the real values and is thus expected to achieve high performance.Experiments on a 6-DoF robot were conducted to verify the proposed method.展开更多
Robotic unmanned blimps own an enormous potential for applications in low-speed and low-altitude exploration, surveillance, and monitoring, as well as telecommunication relay platforms. To make lighter-than-air platfo...Robotic unmanned blimps own an enormous potential for applications in low-speed and low-altitude exploration, surveillance, and monitoring, as well as telecommunication relay platforms. To make lighter-than-air platform a robotic blimp with significant levels of autonomy, the decoupled longitude and latitude dynamic model is developed, and the hardware and software of the flight control system are designed and detailed. Flight control and navigation strategy and algorithms for waypoint flight problem are discussed. A result of flight experiment is also presented, which validates that the flight control system is applicable and initial machine intelligence of robotic blimp is achieved.展开更多
The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore ...The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured.The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program.In this study,a radial basis function(RBF)neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskel-eton robot.A realistic friction model is used for modelling joint friction.High trajectory tracking accuracies have been obtained.Evidence of computational efficiency has been observed.The stability analysis of the developed controller is presented.Analysis of variance is used to assess the controller's resilience to parameter variation.To show the effectiveness of the developed controller,a comparative study was performe between the developed RBF network‐based controller and Sliding Mode Controller,Computed Tor-que Controller,Adaptive controller,Linear Quadratic Regulator and Model Reference Computed Torque Controller.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant No.91848106)the Program of Shanghai Academic/Technology Research Leader(Grant No.18XD1401700)。
文摘Dynamic models play an important role in robot control and applications.The accurate identification of dynamic models has become crucial to meeting increasing performance requirements.Owing to the inertial forces and the joint frictions coupling,the identification first requires a parametrized friction model.However,the joint frictions are strongly nonlinear and vary with many factors including posture,velocity and temperature.Hence,all friction models have some deviation from the real values,which reduces the identification accuracy.This paper proposes an identification approach using a baseplate force sensor.It identifies the inertial parameters first and then computes the joint friction values by subtracting the inertial torques from the joint torques.This method has the advantage that it does not require a priori friction model.It can choose or construct a proper model to fit the real values and is thus expected to achieve high performance.Experiments on a 6-DoF robot were conducted to verify the proposed method.
基金This project is supported by National Natural Science Foundation of China (No. 50405046, No. 60605028)Program for Excellent Young Teachers of Shanghai, China (No. 04Y0HB094)+1 种基金State Leading Academic Discipline Fund of China (No. Y0102)Provincial Leading Academic Discipline Fund of Shanghai, China (No. BB67).
文摘Robotic unmanned blimps own an enormous potential for applications in low-speed and low-altitude exploration, surveillance, and monitoring, as well as telecommunication relay platforms. To make lighter-than-air platform a robotic blimp with significant levels of autonomy, the decoupled longitude and latitude dynamic model is developed, and the hardware and software of the flight control system are designed and detailed. Flight control and navigation strategy and algorithms for waypoint flight problem are discussed. A result of flight experiment is also presented, which validates that the flight control system is applicable and initial machine intelligence of robotic blimp is achieved.
文摘The realisation of a model‐based controller for a robot with a higher degree of freedom requires a substantial amount of computational power.A high‐speed CPU is required to maintain a higher sampling rate.Multicore processors cannot boost the performance or reduce the execution time as the programs are sequentially structured.The neural network is a great tool to convert a sequentially structured program to an equivalent parallel architecture program.In this study,a radial basis function(RBF)neural network is developed for controlling 7 degrees of freedom of the human lower extremity exoskel-eton robot.A realistic friction model is used for modelling joint friction.High trajectory tracking accuracies have been obtained.Evidence of computational efficiency has been observed.The stability analysis of the developed controller is presented.Analysis of variance is used to assess the controller's resilience to parameter variation.To show the effectiveness of the developed controller,a comparative study was performe between the developed RBF network‐based controller and Sliding Mode Controller,Computed Tor-que Controller,Adaptive controller,Linear Quadratic Regulator and Model Reference Computed Torque Controller.
