In this paper, an admittance control scheme is proposed for physical human-robot interaction with human subject's intention motion as well as dynamic uncertainties of the robotic exoskeleton. Human subject's intention motion is represented by the reference trajectory when the exoskeleton manipulator is complying with the external interaction force. Online estimation of the stiffness is employed to deal with the variable impedance property of the exoskeleton manipulator. An admittance control approach is firstly presented based on the measurable force in order to generate a differentiable reference trajectory in interaction tasks. Then a stability criterion can be obtained due to the proposed control method. The designed controller includes linearly parameterization and estimation for the unknown items of the dynamics. Bounded and convergent error is shown in the tracking process while the robustness of the variable stiffness control method is guaranteed. The control approach is then verified on a robotic exoskeleton interacting with human via experiments. The results show that the presented approach can make for an effective pHRI performance.

In this paper, two upper limbs of an exoskeleton robot are operated within a constrained region of the operational space with unidentified intention of the human operator’s motion as well as uncertain dynamics including physical limits. The new human-cooperative strategies are developed to detect the human subject’s movement efforts in order to make the robot behavior flexible and adaptive. The motion intention extracted from the measurement of the subject’s muscular effort in terms of the applied forces/torques can be represented to derive the reference trajectory of his/her limb using a viable impedance model. Then, adaptive online estimation for impedance parameters is employed to deal with the nonlinear and variable stiffness property of the limb model. In order for the robot to follow a specific impedance target, we integrate the motion intention estimation into a barrier Lyapunov function based adaptive impedance control. Experiments have been carried out to verify the effectiveness of the proposed dual-arm coordination control scheme, in terms of desired motion and force tracking

This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle the uncertainty and the unknown deadzone effect, we introduce adaptive neural network (NN) control for robotic manipulators. State-feedback control is introduced first and a high-gain observer is then designed to make the proposed control scheme more practical. One radial basis function NN (RBFNN) is used to tackle the deadzone effect, and the other RBFNN is also proposed to estimate the unknown dynamics of robot. The proposed control is then verified on a two-joint rigid manipulator via numerical simulations and experiments.

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This paper has developed a coordination control method for a dual-arm exoskeleton robot based on human impedance transfer skills, where the left (master) robot arm extracts the human limb impedance stiffness and position profiles, and then transfers the information to the right (slave) arm of the exoskeleton. A computationally efficient model of the arm endpoint stiffness behavior is developed and a co-contraction index is defined using muscular activities of a dominant antagonistic muscle pair. A reference command consisting of the stiffness and position profiles of the operator is computed and realized by one robot in real-time. Considering the dynamics uncertainties of the robotic exoskeleton, an adaptive-robust impedance controller in task space is proposed to drive the slave arm tracking the desired trajectories with convergent errors. To verify the robustness of the developed approach, a study of combining adaptive control and human impedance transfer control under the presence of unknown interactive forces is conducted. The experimental results of this paper suggest that the proposed control method enables the subjects to execute a coordination control task on a dual-arm exoskeleton robot by transferring the stiffness from the human arm to the slave robot arm, which turns out to be effective