This paper investigates iterative learning control based on passivity for two-degree-of-freedom (2DOF) robot manipulators with antagonistic bi-articular muscles. Firstly, a brief summary of dynamics of 2DOF robot manipulators with antagonistic bi-articular muscles is given. Next, an error dynamics of the bi-articular manipulator for iterative learning control that has an output strictly passivity property is constructed. Then, we propose an iterative learning control law for the bi-articular manipulator. The proposed torque input does not need the parameters for the accurate models. Convergence analysis of the closed-loop system is carried out based on passivity. Finally, simulation results are presented in order to confirm the effectiveness of the proposed control law.
This paper considers vision-based motion control with the manipulator dynamics using position measurements and visual information, which we term dynamic visual feedback control. Firstly the visual feedback system of rigid body motion is described in order to derive the dynamic visual feedback system. Secondly we propose a dynamic visual feedback control law which guarantees local asymptotic stability of the overall closed-loop system using a Lyapunov function. L2-gain performance analysis for the proposed control law has been discussed using the energy function which plays the role of a storage function. Next, we show that the control law is based on passivity and the dynamic visual feedback system is constructed from two passive systems. Finally simulation results confirm the effectiveness of the dynamic visual feedback control law.
This paper considers muscle contraction dynamics during co-contraction of antagonist bi-articular muscles for tracking control of a human limb. Co-contraction of antagonist muscles play an important role for joint stiffness and stability and experimental results show the existence of co-contraction during volitional movements. This paper shows the role of co-contraction by using three pairs of bi-articular antagonistic muscles. It is indicated that the co-contraction is useful not only to compensate the stability of the joint, but also to control the output force direction. The main contribution is to model the co-contraction control of human limb by using three pairs of bi-articular antagonistic muscles. A robust integral of the sign of the error (RISE) controller is shown to yield semi-global asymptotic tracking. The tracking control performance and stability are verified in the simulation.
This paper considers RISE control for two-degree-of-freedom (2DOF) human lower limb with antagonistic bi-articular muscles. The antagonistic bi-articular muscles straddle the waist joint and the knee joint in the lower limb. Because the nonlinear model of the lower limb of the human body is uncertain, a robust control method is developed yield semiglobal asymptotic tracking. Simulation results indicate that the torques in joint 2 of the 2DOF lower limb is lower than the previous method, because of antagonistic bi-articular muscles. It is verified that the 2DOF lower limb can move to the desired position in the presence of unmodeled bounded disturbances.
This paper investigates stabilizing receding horizon control via an image space navigation function for three-dimensional (3-D) visual feedback systems. Firstly, we describe the representation of a relative rigid body motion and a camera model. Next, the visual motion error system is reconstructed in order to apply to time-varying desired motion. Then, visual motion observer-based stabilizing receding horizon control for 3-D visual feedback systems is proposed. Moreover, a path planner to be appropriate for the visual motion error system is designed through an image space navigation function to keep all features into the camera field of view. The main contribution of this paper is to show that the path planner which always remains in the camera field of view during the servoing is designed for the position-based visual feedback receding horizon control based on optimal control theory. Finally, we present simulation and actual nonlinear experimental results in order to verify control performance with visibility maintenance of the proposed control scheme.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
This paper investigates the visual feedback control by the visual motion observer with input saturation. Firstly, using standard body-attached coordinate frames (the world frame, camera frame and object frame), we present the visual motion error system which consists of the estimation error system and the pose control error system. Next, we propose the control law with the input saturation. After that, stability analysis of the closed-loop system is discussed in the sense of Lyapunov. Although the proposed control law cannot be designed based on the passivity of the visual motion error system explicitly, the skew-symmetric property of the visual motion error system plays an important role in the stability analysis. Finally, experimental results are shown in order to confirm the proposed method by using AR.Drone as a small unmanned aerial vehicle.
In this paper, we demonstrate the usefulness of the micro-manipulation system with a robotic straw proposed by the authors for the microorganism isolation task. Isolating microorganism is one of the fundamental bio-manipulation tasks. This task has been conventionally achieved with a straw connected with a rubber tube. However, some skill is required by an operator for this isolation task, because it is difficult for a naive operator to place the tip of pipette at a desired position under a microscope. We show that the microorganism isolation task is successfully achieved with the proposed system even by a naive operator of the conventional microorganism isolation task without long-term training.