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An arm body-building system with virtual mechanism connection is presented in this paper. Applied the cross-couple effect, proposed system will be the same as the conventional body-building mechanisms. This research includes an impedance controller and recurrent fuzzy neural networks which compensates the virtual cross-couple position error. The stability analysis and update law of the fuzzy neural...
In this paper, an adaptive impedance force control scheme for an n-link robot manipulator under unknown environment is proposed. The system dynamics of the robot manipulator is assumed that system model is not exactly known or has system uncertainty. Therefore, the traditional adaptive impedance force controller is not valid. Herein, the fuzzy neural networks are adopted to estimate the system model...
In contact operations, haptic information plays a very important role on compensating the limitation of visual information. Therefore, there is growing interests in the application of force feedback to improve the perception of the operators in virtual training systems. Based on the analysis on the load of the force feedback system, a gain-scheduling fuzzy proportional-integral-derivative (PID) controller...
Force control algorithm of the robot manipulator contacting with a constraint surface with uncertain figure and stiffness error is discussed in this paper. To accomplish the accurate parameter less-relying constraint motion, a real-time adjusting fuzzy logic of reference trajectories in impedance model is proposed. The adjustment depends on the real-time force and position feedback. Simulation experiments...
A novel position impedance controller with force estimation model is proposed to suppress the uncertainty of the position in robotic machining process. This control strategy employs a fuzzy logic controller to regulate the impedance parameters to reduce the disturbance in constrained motion and improve the effect of force control. An empirical force model taking into consideration the burrs affection...
The interaction force and the environmental uncertainties and changes are the most challenges for robotic material removal process. A self-tuning fuzzy strategy is adopted to implement the on-line compensation for the static error caused by the traditional impedance controller to improve the control performance. The fuzzy controller is adjusted by an updating factor to select the most appropriate...
The controller design is one of the major difficulties in realizing robot-aided rehabilitation program. The purpose of our study is to develop an adaptive impedance force control strategy based on dynamic recurrent fuzzy neural network to maintain the stability of the rehabilitation robot system in the case when the patient's physical condition makes a change. An on-line identification method was...
In the previous studies mechanical impedance has become well implement in the field of human-machine interaction. However, in human-vehicle interaction there is a variant environment when driving a vehicle, thus apart from interacting with human, impedance controller is needed to adapt to the variant environment. But for mechanical impedance as being a second order mass-viscosity-stiffness model with...
Robot-assisted rehabilitation therapy has been paid a lot of attention by robot researchers and therapists for its potential ability of rehabilitation training and assess. The purpose of our study is to develop a fuzzy adaptive control strategy based on traditional impedance control for providing optimal force to stroke patients. An online identification of parameters was used to estimate impaired...
This paper presents a new adaptive impedance control based on a recurrent fuzzy neural networks (RFNN). The proposed control scheme includes two elements, a RFNN impedance nominal controller (RFNNINC) and a RFNN robust compensator (RFNNRC). The RFNNINC is developed to allow the linearized system performance to approximate the set impedance model accurately. The nonlinear term error between the system...
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