Bifurcation and chaos analysis of a gear pair system with multi-clearance

In order to investigate the characteristics of bifurcation and chaos for a spur gear pair system, a three-degree-of-freedom nonlinear dynamic model with multi-clearance is established, in which time-varying meshing stiffness, static transmission error, gear backlash and bearing clearance are comprehensively taken into account. Through introducing a relative generalized coordinate, the dimensionless dynamic equations of motion of system are derived and then solved by using Runge-Kutta numerical integration method. And the bifurcation and chaos features of gear pair are systematically analyzed and discussed from bifurcation diagrams with meshing frequency, gear backlash, bearing clearance and damping ratio as control parameters under different loaded conditions. Meantime, with the help of Poincaré map and phase diagram, the motion forms of system are accurately identified. The analysis results reveal that as meshing frequency increases, the system shows various types of motion states which contain periodic motion, quasi-periodic motion and chaotic motion. Similarly, with the increasing of gear backlash, the system undergoes complex motion forms under lightly loaded condition, whereas it is only in period-one motion state under heavily loaded condition. Furthermore, the system motion state is gradually switched from chaos to periodic or quasi-periodic motion under lightly loaded condition when bearing clearance changes. However, under heavily loaded condition, the bearing clearance has a weak effect on dynamic behavior of the gear system. Apparently, the system tends to be more stable under heavily loaded condition than that under lightly loaded condition. In addition, the growing damping ratio can effectively suppress the chaotic behavior and control nonlinear vibration of gear system. The research results provide useful guidance for dynamic design and vibration control for gear set

Hyperconjugated side chained benzodithiophene and 4,7-di-2-thienyl-2,1,3-benzothiadiazole based polymer for solar cells

A novel donor-acceptor (D-A) copolymer (P3TBDTDTBT), including hyperconjugated side chained benzodithiophene as a donor and 4,7-di-2-thienyl-2,1,3-benzothiadiazole (DTBT) as an acceptor, was designed and synthesized. Due to the introduction of the hyperconjugated side chain, the resultant polymer exhibited good thermal stability with a high decomposition temperature of 437 degrees C, a low band-gap of 1.67 eV with an absorption onset of 742 nm in the solid film, and a deep highest occupied molecular orbital (HOMO) energy level of -5.26 eV. Finally, the polymer solar cell (PSC) device based on this polymer and [6,6]-phenyl-C-61-butyric acid methyl ester (PCBM) showed the best power conversion efficiency (PCE) of 3.57% with an open-circuit voltage (V-oc) of 0.78 V, a short-circuit current density (J(sc)) of 8.83 mA cm(-2) and a fill factor (FF) of 53%

Task Space Trajectory Tracking Control of Robot Manipulators with Uncertain Kinematics and Dynamics

To improve the tracking precision of robot manipulators’ end-effector with uncertain kinematics and dynamics in the task space, a new control method is proposed. The controller is based on time delay estimation and combines with the nonsingular terminal sliding mode (NTSM) and adaptive fuzzy logic control scheme. Kinematic parameters are not exactly required with the consideration of kinematic uncertainties in the controller. No dynamic models or numerous parameters of the robot manipulator system are required with the use of TDE. Thus, the controller is simple structure and suitable for practical applications. Furthermore, errors caused by time delay estimation are compensated by the adaptive fuzzy nonsingular terminal sliding mode scheme. The simulation is performed on a 2-DOF robot manipulator with three cases in the task space. The results show that the proposed controller provides faster convergence rate and higher tracking precision than TDE based NTSM and improved TDE based NTSM controller

Workspace Description and Evaluation of Master-Slave Dual Hydraulic Manipulators

Nuclear power plant emergency robots are robots used to respond to significant public safety incidents, such as uncontrolled radioactive sources and nuclear catastrophe leaks. However, there are no standardized evaluation criteria for the optimal design of the robots. We offer a quantitative analytic algorithm for optimizing nuclear power plant emergency robots to address this issue. The method optimizes the structural parameters of the robot in accordance with the workspace by analyzing, comparing, and evaluating the workspace. The approach comprises constructing a kinematic model of the mechanical arm and proposing an optimization algorithm based on the alpha shape to accurately describe the manipulator workspace; employing the proposed convex hull algorithm to quantitatively analyze and evaluate the workspace generated by different solutions in terms of area, volume, task demand, Structural Length Index and Global Conditioning Index; and determining the robotic arm joint parameters by selecting the optimum workspace design solution. Using the suggested algorithm, we optimize the design of the master and slave robotic arms of the nuclear power plant emergency robots. Theoretical calculations and simulation results demonstrate that the method is an effective and practical evaluation technique that not only accurately describes the workspace but also optimizes the design of the nuclear power plant emergency robots

Machine learning assisted vector atomic magnetometry

Multiparameter sensing such as vector magnetometry often involves complex setups due to various external fields needed in explicitly connecting one measured signal to one parameter. Here, we propose a paradigm of indirect encoding for vector atomic magnetometry based on machine learning. We encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals associated with the optical rotation of a laser beam traversing the atomic sample. The map between the recorded signals and the vectorial field information is established through a pre-trained deep neural network. We demonstrate experimentally a single-shot all optical vector atomic magnetometer, with a simple scalar-magnetometer design employing only one elliptically-polarized laser beam and no additional coils. Magnetic field amplitude sensitivities of about 100 fT/√Hz and angular sensitivities of about 100∼200μrad/√Hz (for a magnetic field of around 140 nT) are derived from the neural network. Our approach can reduce the complexity of the architecture of vector magnetometers, and may shed light on the general design of multiparameter sensing