Admittance-based controller design for physical human-robot interaction in the constrained task space

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in the constrained task space. An admittance model and a soft saturation function are employed to generate a differentiable reference trajectory to ensure that the end-effector motion of the manipulator complies with the human operation and avoids collision with surroundings. Then, an adaptive neural network (NN) controller involving integral barrier Lyapunov function (IBLF) is designed to deal with tracking issues. Meanwhile, the controller can guarantee the end-effector of the manipulator limited in the constrained task space. A learning method based on the radial basis function NN (RBFNN) is involved in controller design to compensate for the dynamic uncertainties and improve tracking performance. The IBLF method is provided to prevent violations of the constrained task space. We prove that all states of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB) by utilizing the Lyapunov stability principles. At last, the effectiveness of the proposed algorithm is verified on a Baxter robot experiment platform. Note to Practitioners-This work is motivated by the neglect of safety in existing controller design in physical human-robot interaction (pHRI), which exists in industry and services, such as assembly and medical care. It is considerably required in the controller design for rigorously handling constraints. Therefore, in this article, we propose a novel admittance-based human-robot interaction controller. The developed controller has the following functionalities: 1) ensuring reference trajectory remaining in the constrained task space: A differentiable reference trajectory is shaped by the desired admittance model and a soft saturation function; 2) solving uncertainties of robotic dynamics: A learning approach based on radial basis function neural network (RBFNN) is involved in controller design; and 3) ensuring the end-effector of the manipulator remaining in the constrained task space: different from other barrier Lyapunov function (BLF), integral BLF (IBLF) is proposed to constrain system output directly rather than tracking error, which may be more convenient for controller designers. The controller can be potentially applied in many areas. First, it can be used in the rehabilitation robot to avoid injuring the patient by limiting the motion. Second, it can ensure the end-effector of the industrial manipulator in a prescribed task region. In some industrial tasks, dangerous or damageable tools are mounted on the end-effector, and it will hurt humans and bring damage to the robot when the end-effector is out of the prescribed task region. Third, it may bring a new idea to the designed controller for avoiding collisions in pHRI when collisions occur in the prescribed trajectory of end-effector

Bayesian estimation of human impedance and motion intention for human-robot collaboration

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance

Estimation of human impedance and motion intention for constrained human-robot interaction

In this paper, a complete framework for safe and efficient physical human-robot interaction (pHRI) is developed for robot by considering both issues of adaptation to the human partner and ensuring the motion constraints during the interaction. We consider the robot’s learning of not only human motion intention, but also the human impedance. We employ radial basis function neural networks (RBFNNs) to estimate human motion intention in real time, and least square method is utilized in robot learning of human impedance. When robot has learned the impedance information about human, it can adjust its desired impedance parameters by a simple tuning law for operative compliance. An adaptive impedance control integrated with RBFNNs and full-state constraints is also proposed in our work. We employ RBFNNs to compensate for uncertainties in the dynamics model of robot and barrier Lyapunov functions are chosen to ensure that full-state constraints are not violated in pHRI. Results in simulations and experiments show the better performance of our proposed framework compared with traditional methods

[Letter] Adaptive NN impedance control for an SEA-driven robot

In this letter, we propose an adaptive active impedance control combined with passive mechanical impedance, and use neural networks (NNs) to compensate for uncertainties of compliant robot’s dynamics. Taking these into account, we propose an online adaptive law to update NN weights and a complete framework about adaptive impedance control design. Simulations show the proposed method can ensure that both the accuracy and safety can be achieved

Effect of bioaugmentation on gas production and microbial community during anaerobic digestion in a low-temperature fixed-bed reactor

Low temperature is one of the limiting factors for anaerobic digestion in cold regions. To improve the efficiency of anaerobic digestion for methane production in stationary reactors under low-temperature conditions, and to improve the structure of the microbial community for anaerobic digestion at low temperatures. We investigated the effects of different concentrations of exogenous Methanomicrobium (10, 20, 30%) and different volumes of carbon fiber carriers (0, 10, 20%) on gas production and microbial communities to improve the performance of low-temperature anaerobic digestion systems. The results show that the addition of 30% exogenous microorganisms and a 10% volume of carbon fiber carrier led to the highest daily (128.15 mL/g VS) and cumulative (576.62 mL/g VS) methane production. This treatment effectively reduced the concentrations of COD and organic acid, in addition to stabilizing the pH of the system. High-throughput sequencing analysis revealed that the dominant bacteria under these conditions were Acidobacteria and Firmicutes and the dominant archaea were Candidatus_Udaeobacter and Methanobacterium. While the abundance of microorganisms that metabolize organic acids was reduced, the functional abundance of hydrogenophilic methanogenic microorganisms was increased. Therefore, the synergistic effect of Methanomicrobium bioaugmentation with carbon fiber carriers can significantly improve the performance and efficiency of low-temperature anaerobic fermentation systems

An Approximate Estimation Approach of Fault Size for Spalled Ball Bearing in Induction Motor by Tracking Multiple Vibration Frequencies in Current

Fault size estimation is of great importance to bearing performance degradation assessment and life prediction. Until now, fault size estimation has generally been based on acoustic emission signals or vibration signals; an approach based on current signals has not yet been mentioned. In the present research, an approximate estimation approach based on current is introduced. The proposed approach is easy to implement for existing inverter-driven induction motors without complicated calculations and additional sensors, immune to external disturbances, and suitable for harsh conditions. Firstly, a feature transmission route from spall, to Hertzian forces, and then to friction torque is simulated based on a spall model and dynamic model of the bearing. Based on simulated results, the relation between spall size and the multiple characteristic vibration frequencies in friction torque is revealed. Secondly, the multiple characteristic vibration frequencies modulated in the current is investigated. Analysis results show that those frequencies modulated in the current are independent of each other, without spectrum overlap. Thirdly, to address the issue of which fault features modulated in the current are very weak, a fault-feature-highlighting approach based on reduced voltage frequency ratio is proposed. Finally, experimental tests were conducted. The obtained results validate that the proposed approach is feasible and effective for spall size estimation

Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers

Objective: To process and extract electrocardiogram (ECG, ECG, or EKG) features using a convolutional neural network (CNN) to establish an ECG-assisted diagnosis model. Methods: Coal workers who underwent physical examinations at Gequan Mine Hospital and Dongpang Mine Hospital of Hebei Jizhong Energy from July 2020 to September 2020 were selected as the study subjects. The ECG images were preprocessed. We use Python software and convolutional neural network to establish ECG images recognition and classification model.We usecalibration curve, calibration-in-the-large, Brier score, specificity, sensitivity, F1 score, Kappa value, accuracy, and area under the curve (AUC) of ROC to evaluate the performance of the model. Results: The number of abnormal ECG results was 849, and the rate of abnormal results was 25.02%. The test set accuracies of the sinus bradycardia model, nonspecific intraventricular conduction delay model, myocardial ischemia model, and sinus tachycardia model were 97.66%, 96.49%, 93.62%, and 93.02%, respectively; sensitivities were 96.63%, 96.30%, 96.88% and 95.24%, respectively; specificities were 98.78%, 96.67%, 86.67%, and 90.90%, respectively; Brier scores were 0.03, 0.07, 0.09, and 0.11, respectively; Calibration-in-the-large values were 0.026, 0.110, 0.041, and 0.098, respectively. Conclusions: The convolutional neural network model can accurately identify the main ECG abnormality types of coal workers. Additionally, the main ECG abnormalities in these coal company workers were sinus bradycardia, non-specific intraventricular conduction delay, myocardial ischemia, and sinus tachycardia

A new 1-D chain based on the trivacant monocapped Keggin arsenomolybdate and the copper complex linker: synthesis, crystal structure, and ESI-MS analyses

<div><p>A new organic–inorganic hybrid (H₂en)₂[[Cu(en)₂]As^IIIAs^VMo^VI₉O₃₄]·6H₂O (<b>1</b>), containing a 1-D helical chain based on the trivacant monocapped Keggin arsenomolybdate and the copper complex linker {[Cu(en)₂][As^IIIAs^VMo^VI₉O₃₄]}_n⁴ⁿ⁻ (en = ethylenediamine), has been synthesized and characterized by IR spectra, TG analyses, single-crystal X-ray diffraction, and high-resolution electrospray ionization mass spectrometry (ESI-MS). Large voids are observed and a 1-D chain containing repeated (H₂O)₈ water units from lattice water molecules is formed along the <i>a</i> axis in the crystal structure. The high-resolution ESI-MS shows that the intact framework [Cu(en)₂][As^IIIAs^VMo^VI₉O₃₄]⁴⁻ exists in solution.</p></div