This paper presents adaptive impedance control of an upper limb robotic exoskeleton using biological signals. First, we develop a reference musculoskeletal model of the human upper limb and ...experimentally calibrate the model to match the operator's motion behavior. Then, the proposed novel impedance algorithm transfers stiffness from human operator through the surface electromyography (sEMG) signals, being utilized to design the optimal reference impedance model. Considering the unknown deadzone effects in the robot joints and the absence of the precise knowledge of the robot's dynamics, an adaptive neural network control incorporating with a high-gain observer is developed to approximate the deadzone effect and robot's dynamics and drive the robot tracking desired trajectories without velocity measurements. In order to verify the robustness of the proposed approach, the actual implementation has been performed using a real robotic exoskeleton and a human operator.
Due to strongly coupled nonlinearities of the grasped dual-arm robot and the internal forces generated by grasped objects, the dual-arm robot control with uncertain kinematics and dynamics raises a ...challenging problem. In this paper, an adaptive fuzzy control scheme is developed for a dual-arm robot, where an approximate Jacobian matrix is applied to address the uncertain kinematic control, while a decentralized fuzzy logic controller is constructed to compensate for uncertain dynamics of the robotic arms and the manipulated object. Also, a novel finite-time convergence parameter adaptation technique is developed for the estimation of kinematic parameters and fuzzy logic weights, such that the estimation can be guaranteed to converge to small neighborhoods around their ideal values in a finite time. Moreover, a partial persistent excitation property of the Gaussian-membership-based fuzzy basis function was established to relax the conventional persistent excitation condition. This enables a designer to reuse these learned weight values in the future without relearning. Extensive simulation studies have been carried out using a dual-arm robot to illustrate the effectiveness of the proposed approach.
This paper presents a robust six-degree-of-freedom relative navigation by combining the iterative closet point (ICP) registration algorithm and a noise-adaptive Kalman filter in a closed-loop ...configuration together with measurements from a laser scanner and an inertial measurement unit (IMU). In this approach, the fine-alignment phase of the registration is integrated with the filter innovation step for estimation correction, while the filter estimate propagation provides the coarse alignment needed to find the corresponding points at the beginning of ICP iteration cycle. The convergence of the ICP point matching is monitored by a fault-detection logic, and the covariance associated with the ICP alignment error is estimated by a recursive algorithm. This ICP enhancement has proven to improve robustness and accuracy of the pose-tracking performance and to automatically recover correct alignment whenever the tracking is lost. The Kalman filter estimator is designed so as to identify the required parameters such as IMU biases and location of the spacecraft center of mass. The robustness and accuracy of the relative navigation algorithm is demonstrated through a hardware-in-the loop simulation setting, in which actual vision data for the relative navigation are generated by a laser range finder scanning a spacecraft mockup attached to a robotic motion simulator.
Robots with coordinated dual arms are able to perform more complicated tasks that a single manipulator could hardly achieve. However, more rigorous motion precision is required to guarantee effective ...cooperation between the dual arms, especially when they grasp a common object. In this case, the internal forces applied on the object must also be considered in addition to the external forces. Therefore, a prescribed tracking performance at both transient and steady states is first specified, and then, a controller is synthesized to rigorously guarantee the specified motion performance. In the presence of unknown dynamics of both the robot arms and the manipulated object, the neural network approximation technique is employed to compensate for uncertainties. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is integrated into the control design. Effectiveness of the proposed control design has been shown through experiments carried out on the Baxter Robot.
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.
The micro/nano positioning system discussed in this paper includes a piezoelectric actuator (PEA) and flexure-hinge-based positioning mechanism. Due to the existence of the hysteretic nonlinearity in ...the PEA and the friction in the system, the accurate positioning of the piezo-actuated positioning system calls applicable control schemes for practical applications. To this end, an implementable adaptive controller is developed in the paper, where a minimized parameterization hysteresis model is employed to reduce the computational load. The formulated adaptive control law guarantees the global stability of the controlled positioning system, and the positioning error can approach to zero asymptotically. The advantages of the proposed method making on-line implementation feasible are that the traditional inversion of the hysteresis does not need to be constructed directly; the real values of the parameters of the positioning system neither need to be identified nor measured; only the parameters in the formulation of the controller are estimated online. Comparison with the feedforward plus proportional-integral feedback control scheme is conducted and experimental results show the effectiveness of the proposed method.
In this paper, we have developed a novel visual servo-based model predictive control method to steer a wheeled mobile robot (WMR) moving in a polar coordinate toward a desired target. The proposed ...control scheme has been realized at both kinematics and dynamics levels. The kinematics predictive steering controller generates command of desired velocities that are achieved by employing a low-level motion controller, while the dynamics predictive controller directly generates torques used to steer the WMR to the target. In the presence of both kinematics and dynamics constraints, the control design is carried out using quadratic programming (QP) for optimal performance. The neurodynamic optimization technique, particularly the primal-dual neural network, is employed to solve the QP problems. Theoretical analysis has been first performed to show that the desired velocities can be achieved with the guaranteed stability, as well as with the global convergence to the optimal solutions of formulated convex programming problems. Experiments have then been carried out to validate the effectiveness of the proposed control scheme and illustrate its advantage over the conventional methods.
Summary
The Orchidaceae is a diverse and ecologically important plant family. Approximately 69% of all orchid species are epiphytes, which provide diverse microhabitats for many small animals and ...fungi in the canopy of tropical rainforests. Moreover, many orchids are of economic importance as food flavourings or ornamental plants. Phalaenopsis aphrodite, an epiphytic orchid, is a major breeding parent of many commercial orchid hybrids. We provide a high‐quality chromosome‐scale assembly of the P. aphrodite genome. The total length of all scaffolds is 1025.1 Mb, with N50 scaffold size of 19.7 Mb. A total of 28 902 protein‐coding genes were identified. We constructed an orchid genetic linkage map, and then anchored and ordered the genomic scaffolds along the linkage groups. We also established a high‐resolution pachytene karyotype of P. aphrodite and completed the assignment of linkage groups to the 19 chromosomes using fluorescence in situ hybridization. We identified an expansion in the epiphytic orchid lineage of FRS5‐like subclade associated with adaptations to the life in the canopy. Phylogenetic analysis further provides new insights into the orchid lineage‐specific duplications of MADS‐box genes, which might have contributed to the variation in labellum and pollinium morphology and its accessory structure. To our knowledge, this is the first orchid genome to be integrated with a SNP‐based genetic linkage map and validated by physical mapping. The genome and genetic map not only offer unprecedented resources for increasing breeding efficiency in horticultural orchids but also provide an important foundation for future studies in adaptation genomics of epiphytes.
Smart actuators employed in micropositioning are known to exhibit strong hysteresis nonlinearities, which may be asymmetric and could adversely affect the positioning accuracy. In this paper, the ...analytical inverse of a generalized Prandtl-Ishlinskii model is formulated to compensate for hysteresis nonlinearities of smart actuators. The generalized model was modified to ensure its continuity, and its validity in characterizing different hysteresis properties is briefly demonstrated by comparing the model responses with the measured data for the magnetostrictive, shape memory alloys (SMA), and piezo micropositioning actuators. Since the proposed generalized model is a mere extension of the analytically invertible classical Prandtl-Ishlinskii model, an inverse of the generalized model is formulated using the inverse of the classical model together with those of the envelope functions of the generalized play operator. The effectiveness of the inverse of the generalized model in compensating for the symmetric and asymmetric saturated hysteresis effects is subsequently investigated through simulations for a magnetostrictive and a SMA actuators, and through preliminary experiments performed on a piezo micropositioning stage. The simulation results suggest that the inverse of the generalized Prandtl-Ishlinskii model can be conveniently applied as a feedforward compensator to effectively mitigate the effects of the asymmetric and saturated hysteresis in magnetostrictive and SMA actuators. The experimental results further revealed that the proposed generalized analytical inverse model can be conveniently implemented as a real-time feedforward compensator to compensate for hysteresis nonlinearities of a piezo micropositioining stage.
This article deals with an uncertain two-link rigid-flexible manipulator with vibration amplitude constraint, intending to achieve its position control via motion planning and adaptive tracking ...approach. In motion planning, the motion trajectories for the two links of the manipulator are planned based on virtual damping and online trajectories correction techniques. The planned trajectories can not only guarantee that the two links can reach their desired angles, but also have the ability to suppress vibration, which can be adjusted to meet the vibration amplitude constraint by limiting the parameters of the planned trajectories. Then, the adaptive tracking controller is designed using the radial basis function neural network and the sliding mode control technique. The developed controller makes the two links of the manipulator track the planned trajectories under the uncertainties including unmodeled dynamics, parameter perturbations, and persistent external disturbances acting on the joint motors. The simulation results verify the effectiveness of the proposed control strategy and also demonstrate the superior performance of the motion planning and the tracking controller.
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