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The utilization of the Delta parallel robot in high-speed and high-precision applications has been extensive, with motion stability being a critical performance measure. To address the inherent inaccuracies of the model and minimize the impact of external disturbances on motion stability, we propose an adaptive backstepping fractional-order non-singular terminal sliding mode control (ABF-NTSMC). Initially, by employing a backstepping algorithm, we select the virtual control for subsystems as the state variable function in joint space while incorporating a calculus operator to enhance fractional-order sliding mode control (SMC). Subsequently, we describe factors such as model uncertainty and external disturbance using a lumped uncertainty function and estimate its upper bound through an adaptive control law. Ultimately, we demonstrate system stability for our proposed control approach and provide an analysis of finite convergence time. The effectiveness of this presented scheme is demonstrated through simulation and experimental research.

Delta parallel robots have been widely used in precision processing, handling, sorting, and the assembly of parts, and their high efficiency and motion stability are important indexes of their performance.Corners created by small line segments in trajectory planning cause abrupt changes in a tangential discontinuous trajectory, and the vibration and shock caused by such changes seriously affect the robot’s high-speed and high-precision performance. In this study, a trajectory-planning method combining Cartesian space and joint space is proposed. Firstly, the vector method and microelement integration method were used to establish the complete kinematic and dynamic equations of a delta parallel robot, and an inverse kinematic/dynamic model-solving program was written based on the MATLAB software R2020a. Secondly, the end-effector trajectory of the delta parallel robot was planned in Cartesian space, and the data points and inverse control points of the end effector’s trajectory were obtained using the normalization method. Finally, the data points and control points were mapped to the joint space through the inverse kinematic equation, and the fifth-order B-spline curve was adopted for quadratic trajectory planning, which allowed the high-order continuous smoothing of the trajectory planning to be realized. The simulated and experimental results showed that the trajectory-smoothing performance in continuous high-order curvature changes could be improved with the proposed method. The peak trajectory tracking error was reduced by 10.53%, 41.18%, and 44.44%, respectively, and the peak torque change of the three joints was reduced by 3.5%, 11.6%, and 1.6%, respectively.

In recent years, construction engineering technology has been developing rapidly, and the application of curtain walls inside modern buildings is also increasing. However, with the increasing number of curtain wall orders, most factories are facing challenges in the market due to low productivity caused by a low balance rate of curtain wall production lines. This paper is useful in improving the balance rate of the curtain wall production line; firstly, we use the improved genetic algorithm to obtain the optimal sequencing scheme of the curtain wall production line. Then, the optimization plan is validated through FLEXSIM simulation, and the results show that the device utilization rate of each workstation reaches 80%. Finally, this paper designs an intelligent production factory for curtain walls, and then builds an intelligent production line for curtain wall columns for experiments. The experimental results show that the workstation operation time has been reduced from 360 s to 300 s; the production line balance rate has increased from 57.04% to 91.60%. Therefore, it can be concluded that the modified genetic algorithm is valid in improving the balance rate of curtain wall production lines and raising the production efficiency of enterprises.

Thin-walled structures have problems such as low stiffness, large deflection, and vibration. The layout of rib reinforcement in thin-walled structures plays a vital role in providing structural strength and rigidity and reducing structural weight. A multi-scale bionic topology optimization method with a cyclic variable load is proposed in this paper to optimize dynamic flexibility by simulating the growth law of leaf vein formation and distribution. A material interpolation method is adopted to penalize the material attributes of rib reinforcement according to their thickness, based on polynomial interpolation. Combined with the layout of rib reinforcement and SIMP, the mathematical model of rib reinforcement layout optimization with cyclic variable loading is proposed, and the sensitivity of thin-walled dynamic flexibility to the rib reinforcement thickness is analyzed. Two typical examples of thin-walled structures are presented to validate the proposed method. Considering the impact effect of multi-scale cyclic loads such as wind speed, pressure, and raindrops acting on the leaf vein, the natural frequencies of bionic topological structures of heart-shaped and elliptical leaf veins are increased by 63.44% and 47.2%, respectively. Considering the change in radial thickness, the mass of the automotive door inner panel with a bionic topological structure increased by 3.2%, the maximum stress value was reduced by 1.4% and 36.8%, and deformation was reduced by 37.6% and 27.1% under the anti-concave and sinking conditions, respectively. Moreover, the first-order natural frequency of the automotive door’s inner panel with a bionic topological structure increased to 30.45%, 3.7% higher than the original.

Enhancing underwater images presents a challenging problem owing to the influence of ocean currents, the refraction, absorption and scattering of light by suspended particles, and the weak illumination intensity. Recently, different methods have relied on the underwater image formation model and deep learning techniques to restore underwater images. However, they tend to degrade the underwater images, interfere with background clutter and miss the boundary details of blue regions. An improved image fusion and enhancement algorithm based on a prior dark channel is proposed in this paper based on graph theory. Image edge feature sharpening, and dark detail enhancement by homomorphism filtering in CIELab colour space are realized. In the RGB colour space, the multi-scale retinal with colour restoration (MSRCR) algorithm is used to improve colour deviation and enhance colour saturation. The contrast-limited adaptive histogram equalization (CLAHE) algorithm defogs and enhances image contrast. Finally, according to the dark channel images of the three processing results, the final enhanced image is obtained by the linear fusion of multiple images and channels. Experimental results demonstrate the effectiveness and practicality of the proposed method on various data sets.

Sensing the deformation of soft sensor elastomer can realize the flexible operation of soft robot and enhance the perception of human-computer interaction. The structural configuration of elastomer and its elastic deformation force transfer path are crucial for decoupling sensing and studying the sensing performance of three-dimensional force soft sensor. In this article, we present a theoretical method for soft sensor with three-dimensional force decoupling-sensing. First, the constraint types of parallel manipulator with three translational motion characteristics are analyzed and used to set the constraint conditions for topology optimization. In addition, the differential kinematic modeling method is adopted to establish the differential kinematic equation of the three translations parallel manipulator, which is used as a pseudo-rigid body model for sensor information perception. Second, combining the kinematic Jacobi matrix with solid isotropic material with penalization the (SIMP), the topological model is built for designing of sensor elastomer. We optimized the composition of the material and evaluate the model’s sensing capabilities. The results validate a elastomer of soft sensor for unity between structural stiffness and perceived sensitivity.

Considering that the Jacobian matrix maps the velocity of the joint space of the robot to the end velocity of the Cartesian space, a novel topology optimization approach is proposed in this article for the design of three-translational degree-of-freedom spatial compliant mechanisms by combining the optimized Jacobian mapping matrix with the solid isotropic material with penalization topological method. First, by using the condition number method, the structural parameters of Universal-Prismatic-Universal (UPU)-type parallel prototype manipulator are optimized, and then, the differential Jacobian mapping matrix is calculated by using equivalent infinitesimal method. Second, comparing with the driver configuration and twist/wrench constraint conditions of the UPU-type parallel prototype manipulator, the topological algorithm combining solid isotropic material with penalization with optimized Jacobian mapping matrix is proposed. Finally, a novel spatial compliant mechanism with three-translational degree-of-freedom is derived, and numerical simulation results are reported to demonstrate the effectiveness of the proposed method.

In this study, we consider the global stabilization of an axially moving system under the condition of input saturation nonlinearity and external perturbation. Based on Lyapunov redesign method, observer backstepping, and high-gain observers, an output feedback control strategy with an auxiliary system is constructed to eliminate the input saturation constraint effect and suppress the string system vibration, and a boundary disturbance observer is exploited to cope with the external disturbance. The stability of the controlled system is analyzed and proven based on Lyapunov stability without simplifying or discretizing the infinite dimensional dynamics. The presented simulation results show the effectiveness of the derived control.

A novel topology optimization approach is proposed in this paper for the design of three rotational degree-of-freedom (DOF) spatially compliant mechanisms, combining the Jacobian isomorphic mapping matrix with the solid isotropic material with penalization (SIMP) topological method. In this approach, the isomorphic Jacobian matrix of a 3-UPC (U: universal joint, P: prismatic joint, C: cylindrical joint) type parallel prototype manipulator is formulated. Subsequently, the orthogonal triangular decomposition and differential kinematic method is applied to uncouple the Jacobian matrix to construct a constraint for topology optimization. Firstly, with respect to the 3-UPC type parallel prototype manipulator, the Jacobian matrix is derived to map the inputs and outputs to be used for initializing the topology optimization process. Secondly, the orthogonal triangular decomposition with the differential kinematic method is used to reconstruct the uncoupled mapping matrix to derive the 3-UPC type parallel prototype manipulator. Finally, a combination of the solid isotropic material with penalization (SIMP) method and the isomorphic mapping matrix is applied to construct the topological model. A typical three rotational DOF spatially compliant mechanism is reported as a numerical example to demonstrate the effectiveness of the proposed method.

This paper focuses on a new type of configuration design of a compliant parallel mechanism (CPM) planar continuum structure and its characteristic analysis of vibration-inherent frequency for planar motion, which can suppress the impact of random vibration in ultra-precision positioning and manufacturing equipment and improve the inherent frequency response of the mechanism. Firstly, a vector-mapping isomorphism between the fully CPM and conventional isomorphic parallel mechanism was constructed with a kinematic differential Jacobian matrix. Then, the mathematical model of topology optimization was put forward considering the compromise programming on the static stiffness and mean vibration-inherent frequency of the mechanism as the design variable and the minimization of compliance as the objective function. A constraint of volume fraction was considered and multi-objective micro displacement mechanism topology optimization based on a prismatic-revolute-revolute (3-PRR) planar nano-positioning continuum structure was performed using the solid isotropic material with penalization (SIMP) technique, which combines the criteria of the optimization algorithm and the vector isomorphic mapping method. Multi-objective topology optimization of the continuum structure micro displacement mechanism was investigated and presented by optimizations with different initial rejection rates. The simulation results show that the stiffness and vibration suppression performance of the continuum structure were improved, whereas the positioning of differential kinematics characteristics of the 3-PRR micro displacement planar fully CPM and isomorphic prototype mechanism retain the same. The modal analysis also provides a rational configuration for the micro displacement mechanism dimensional design and its optimal modal parameters. The crossover oscillation in frequency response of the continuum structure was reduced and quickly converged in the optimization iterations. The performance of the optimized mechanism was verified by the experiments on a planar fully compliant micro displacement continuum structure based on Lead Zirconate Titanate (PZT) actuator.

In time-variant reliability problems, there are a lot of uncertain variables from different sources. Therefore, it is important to consider these uncertainties in engineering. In addition, time-variant reliability problems typically involve a complex multilevel nested optimization problem, which can result in an enormous amount of computation. To this end, this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model. In this method, the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a time-independent reliability problem. Further, to solve the double nested optimization problem in hybrid reliability calculation, an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point (MPP). The limit state function is linearized at these points, and an innovative random variable is defined to solve the equivalent static reliability analysis model. The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem. [ABSTRACT FROM AUTHOR]

Heavy metal contamination in urban land has become a serious environmental problem in large cities. Visible and near-infrared spectroscopy (vis-NIR) has emerged as a promising method for monitoring copper (Cu), which is one of the heavy metals. When using vis-NIR spectroscopy, it is crucial to consider sample similarity. However, there is limited research on studying sample similarities and determining their relative importance. In this study, we compared three types of similarities: spectral, compositional, and spatial similarities. We collected 250 topsoil samples (0–20 cm) from Shenzhen City in southwest China and analyzed their vis-NIR spectroscopy data (350–2500 nm). For each type of similarity, we divided the samples into five groups and constructed Cu measurement models. The results showed that compositional similarity exhibited the best performance (Rp2 = 0.92, RPD = 3.57) and significantly outperformed the other two types of similarity. Spatial similarity (Rp2 = 0.73, RPD = 1.88) performed slightly better than spectral similarity (Rp2 = 0.71, RPD = 1.85). Therefore, we concluded that the ranking of the Cu measurement model’s performance was as follows: compositional similarity > spatial similarity > spectral similarity. Furthermore, it is challenging to maintain high levels of similarity across all three aspects simultaneously.

Heavy mental contamination in urban land is a serious environmental issue for large cities. Visible and near-infrared spectroscopy has been rapidly developed as a new method for estimating copper (Cu) levels, which is one of the heavy metals. Spectral pretreatment is essential for reducing noise and enhancing analysis. In the traditional method, the entire spectrum is uniformly pretreated. However, in reality, the influence of pretreatment on the spectrum may vary depending on the wavelengths. Limited research has been conducted on breaking down the entire spectrum into distinct parts for individualized pretreatment, an innovative method called piecewise pretreatment. This study gathered 250 topsoil samples (0–20 cm) in Shenzhen City, southwest China, and obtained their vis-NIR spectra (350–2500 nm) in the laboratory. This study divided the spectrum into three parts, each processed by six commonly used spectral pretreatments. The number of pretreated parts varied from 1 to 3, resulting in 342 PLSR models being built. Compared to the traditional method, piecewise pretreatment showed an increase in mean residual predictive deviation (RPD) from 1.55 to 1.71 and an increase in the percentage of positive outcomes in ∆RPD from 33.33% to 55.56%. Thus, we concluded that piecewise pretreatment generally outperforms the traditional method. Furthermore, piecewise pretreatment aims to choose the most effective pretreatment method for each part to optimize the Cu estimation model.