Human-robot cooperation is the major challenges in robot manipulator control, as the controller has to couple the complicated motion of the human arm and the robot end-effectors. To improve the humanrobot coordination, this paper proposed a novel robust finite-time trajectory control based on the nonsingular fast terminal sliding mode and the high-order sliding mode. The proposed method is able to quickly reach the global convergence and minimize motion errors. Based on the nonsingular fast terminal sliding surface, the proposed control method employed a super-twisting algorithm to eliminate the chattering issues to enhance the control robustness. Also, the simplified robust control term does not require the first derivative of the sliding variable. To validate the proposed controller, theoretical analysis and simulation were conducted and the results demonstrated the effectiveness of the proposed method.INDEX TERMS Finite-time trajectory control, nonsingular fast terminal sliding mode, high-order sliding mode, human-robot cooperation.
Development of a coalbed methane (CBM) field in its early stage is often plagued by the lack of well control and scarcity of geological data over a large geographical area. Therefore, constructing a representative static model to estimate the in-place-volume presents a formidable challenge. In this paper we proposed a workflow to overcome this challenge and applied it to a CBM field in the northern Bowen Basin of Australia.This workflow may be considered as a best practice for the following reasons. First, it makes use of data from various sources including cores, well logs, seismic interpretation, and topography. Second, it performs rigorous quality control on these data, such as depth shift and log normalization. Third, coal ply division and correlation and subsequent structural modeling are based on three types of correlation: well-to-well, well-to-seismic and, well-seismic-Graphic Information System. Fourth, it establishes the low, base and high trends for the most important reservoir properties. Fifth, it constructs a base case static model by combining the aforementioned structural and reservoir property models. Sixth, it uses sensitivity analysis, which varies one reservoir parameter at one time, to rank the impact of reservoir parameters on in-place-volume. Seventh, it uses uncertainty analysis which varies all reservoir parameters simultaneously to arrive at the P10, P50 and P90 in-place-volumes and their corresponding static models which can then be used for reservoir simulations to estimate the recoverable volumes.
The lower limb exoskeleton is a wearable human–robot interactive equipment, which is tied to human legs and moves synchronously with the human gait. Gait tracking accuracy greatly affects the performance and safety of the lower limb exoskeletons. As the human–robot coupling systems are usually nonlinear and generate unpredictive errors, a conventional iterative controller is regarded as not suitable for safe implementation. Therefore, this study proposed an adaptive control mechanism based on the iterative learning model to track the single leg gait for lower limb exoskeleton control. To assess the performance of the proposed method, this study implemented the real lower limb gait trajectory that was acquired with an optical motion capturing system as the control inputs and assessment benchmark. Then the impact of the human–robot interaction torque on the tracking error was investigated. The results show that the interaction torque has an inevitable impact on the tracking error and the proposed adaptive iterative learning control (AILC) method can effectively reduce such error without sacrificing the iteration efficiency.
The
isobaric heat capacity of dimethyl ether and 1,1-difluoroethane
in liquid phase were measured at temperatures from 305 K to 365 K
and pressures up to 5 MPa. A heat conduction calorimeter was applied
in the measurements and a total of 81 data points for dimethyl ether
and 78 data points for 1,1-difluoroethane were obtained. The uncertainties
of measured heat capacity were estimated to be 2.1% for dimethyl ether
and 1.8% for 1,1-difluoroethane, respectively. To reproduce the experimental
data, a uniform equation was proposed. The equation showed average
absolute deviations of 0.17 % for dimethyl ether and 0.30 % for 1,1-difluoroethane
from the experimental values. Saturated liquid heat capacities for
both experimental subjects were also obtained by extrapolating the
equation to saturated pressures. Finally, comparisons have been done
between our experimental data and literature data and fundamental
equation of states.
Lower limb exoskeletons (LLEs) are sets of mechanical devices used to support the action of human lower limbs. This recently developed technology has unprecedented potential in the construction industry by increasing the strength, endurance, and other physical capabilities of construction workers. For safety considerations, LLEs need reliable and responsive controllers to closely match their mechanical operation with human gait in a synchronous manner. This research proposes the use of physical human–robot interactive (pHRI) controllers that are suitable for construction tasks. The proposed pHRI integrates a gait trajectory‐based musculoskeletal model with iterative control algorithms. To minimize the trajectory tracking error between LLEs and human lower limbs, the gait dynamic was modeled as a spring damping and impedance model for supporting and swing phases. An iterative adaptive controller was developed for trajectory tracking and predication. To validate the proposed model, an in‐lab experiment to simulate typical construction activities was conducted, allowing us to assess tracking error. The experiment results suggest that the proposed model can minimize the trajectory tracking error to a level acceptable for safe operation. The iterative controllers allow fast error convergence for different construction scenarios with proper calibration. Therefore, the proposed pHRI iterative controllers are reliable and suitable for complicated activities within the dynamic working conditions intrinsic to construction sites.
Unpredictable disturbances and chattering are the major challenges of the robot manipulator control. In recent years, trajectory-tracking-based controllers have been recognized by many researchers as the most promising method to understand robot dynamics with uncertainties and improve robot control. However, reliable trajectory-tracking-based controllers require high model precision and complexity. To develop an agile and straightforward method to mitigate the impact caused by uncertain disturbance and chattering, this study proposed an adaptive neural network sliding mode controller based on the super-twisting algorithm. The proposed model not only can minimize the tracking error but also improve the system robustness with a simpler structure. Moreover, the proposed controller has the following two distinctive features: (1) the weights of the radial basis function (RBF network) are designed to be adjusted in real-time and (2) the prior knowledge of the actual robot system is not required. The analytical model of the proposed controller was proved to be stable and ensured by the Lyapunov theory. To validate the proposed model, this study also conducted a comparative simulation on a two-link robot manipulator system with the conventional sliding mode controller and the model-based controller. The results suggest the proposed model improved the control accuracy and had fewer chattering.
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