This paper presents the design, implementation and control of a new piezoelectrically actuated compliant micromanipulator dedicated to multiscale, precision and reliable operations. To begin with, the manipulator is devised to obtain multi degrees of freedom and large workspace ranges. Two-stage amplification mechanisms (consists of the leverage and the rocker mechanisms) and composite parallelogram mechanisms are combined to construct the lower microstage. Meanwhile, the structure design of the upper dual-driven microgripper is based on the bridge-type mechanism and the unilateral parallelogram mechanism. Through finite-element analysis, the structural parameters of the micromanipulator are optimized and the structural interaction performances are examined. Moreover, a cooperative control strategy is proposed to achieve the synchronous control of the motion trajectory, the gripper position and the contact force. Precision motion control in terms of the hysteresis phenomenon and system disturbances is ensured by using an adaptive sliding mode control (SMC). In particular, an improved nonsymmetrical Bouc–Wen model and a fuzzy regulator are proposed in the SMC. Several experimental investigations are conducted to validate the effectiveness of the developed micromanipulator by performing transferring operations of a micro-object. Experimental results demonstrate that the micromanipulator presents good characteristics, and precision and robust operation can be acquired using the cooperative controller.

Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

This article proposes a two-degree-of-freedom actuator based on the stick-slip motion principle to meet performance requirements of macrostroke, nanoaccuracy, and few coupling in the micro/nanooperation field. Unlike existing 2-DOF stick-slip actuators, it exhibits a compact decoupled parallel guiding structure in macrostroke. The actuator mainly consists of decoupling, driving, and preloading units. The decoupling unit uses parallel kinematic chains with guided rails and connectors to decouple macromovements, the driving unit utilizes bridge mechanisms to amplify single-step displacement, and the preloading unit adopts flexible hinges to adjust contact friction, thus performing well. Then, theoretical analysis and electromechanical simulation are performed. A prototype is finally fabricated, and motion performance tests are conducted. Experimental results show that the net single-step displacements in the x - and y -direction are 37.08 and 36.63 μ m, the coupling ratios in the two directions are 0.75% and 0.77%, and the displacement resolutions are 6.4 and 7.3 nm, respectively. Meanwhile, the motion stroke can be up to 13 mm × 13 mm. Also, the actuator has a maximum speed of 13.97 mm/s and a maximum vertical load capacity of 50 N at a drive voltage of 120 V. Experiments verify the superior performance of the 2-DOF decoupled stick-slip actuator.

This paper reports a new stick-slip piezoelectric linear actuator with large load density, compact size, and high resolution. It is devised using a dual-arch bridge-type driving mechanism (DBDM) and an integrated elliptical preload mechanism(IEPM). The DBDM can generate a coupling motion on the driving point, which provides clamping and releasing actions during stick-slip motion. Also, the DBDM can magnify the clamping force with large amplification ratios, thus improving driving force and load-carrying capacity. The IEPM uses compact flexure hinges to adjust preload force, reducing structural dimension. Then, theoretical analysis and simulations are conducted, and a prototype is fabricated. A modified driving voltage with smooth turning points is designed to improve motion properties further. Experiments show that load density is 0.037 g/mm $^{\mathbf{3}}$ under the driving voltage of 100 V. The actuator has an overall size of 33.6 mm $\times$ 20 mm $\times$ 8 mm and a displacement resolution of 22 nm. Note to Practitioners —Most research on linear actuators focused on motion displacement improvement, and more research needs to be done on improving load-carrying capacity. Therefore, this paper designs a force amplification linear actuator with a compact structure using the DBDM. In the experimental stage, the sudden change of the driving signal affects the output performance, and a modified sawtooth voltage is used to improve the output speed and load-carrying capacity. The experimental results show that under the modified sawtooth wave voltage, the maximum speed of the slider is 1.4 mm/s, and the speed is increased by 17% compared with the traditional wave. When a 200 g load is applied to the slider, the linear actuator operates at a stable speed of 1.2 $\mu $ m/s, 33% faster than conventional sawtooth waves. As a result, the designed actuator has superior performance at higher loads.

Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.