Integrated Power and Attitude Control Design of Satellites Based on a Fuzzy Adaptive Disturbance Observer Using Variable-Speed Control Moment Gyros

To satisfy the requirements for small satellites that seek agile slewing with peak power, this paper investigates integrated power and attitude control using variable-speed control moment gyros (VSCMGs) that consider the mass and inertia of gimbals and wheels. The paper also details the process for developing the controller by considering various environments in which the controller may be implemented. A fuzzy adaptive disturbance observer (FADO) is proposed to estimate and compensate for the effects of equivalent disturbances. The algorithms can simultaneously track attitude and power. The simulation results illustrate the effectiveness of the control approach, which exhibits an improvement of 80 percent compared with alternate approaches that do not employ a FADO

Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning

In recent years, the explosion of web videos makes text-video retrieval increasingly essential and popular for video filtering, recommendation, and search. Text-video retrieval aims to rank relevant text/video higher than irrelevant ones. The core of this task is to precisely measure the cross-modal similarity between texts and videos. Recently, contrastive learning methods have shown promising results for text-video retrieval, most of which focus on the construction of positive and negative pairs to learn text and video representations. Nevertheless, they do not pay enough attention to hard negative pairs and lack the ability to model different levels of semantic similarity. To address these two issues, this paper improves contrastive learning using two novel techniques. First, to exploit hard examples for robust discriminative power, we propose a novel Dual-Modal Attention-Enhanced Module (DMAE) to mine hard negative pairs from textual and visual clues. By further introducing a Negative-aware InfoNCE (NegNCE) loss, we are able to adaptively identify all these hard negatives and explicitly highlight their impacts in the training loss. Second, our work argues that triplet samples can better model fine-grained semantic similarity compared to pairwise samples. We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs. The proposed TPM-CL designs an adaptive token masking strategy with cross-modal interaction to model subtle semantic differences. Extensive experiments demonstrate that the proposed approach outperforms existing methods on four widely-used text-video retrieval datasets, including MSR-VTT, MSVD, DiDeMo and ActivityNet.Comment: Accepted by ACM MM 202

Experiment on vibration control of a two-link flexible manipulator using an input shaper and adaptive positive position feedback

This article presents a novel approach for actively suppressing the vibration within a two-link flexible manipulator to adapt the variation in the model parameters, which is composed of an input shaper and multimode adaptive positive position feedback. Input shaper is applied to shape the command to avoid the flexible vibration in the manoeuvre motion, and the residual vibration can be suppressed by a piezo actuator with the adaptive positive position feedback approach. To demonstrate the approach, two sets of piezoelectric actuator/stain gauge sensor pairs are bonded to the surface of the two-link flexible manipulator; slewing of the flexible link induces vibrations in the link that persist long after the motors stop moving. Vibration suppression is achieved through a combined scheme of input shaper–based motor motion control and an adaptive positive position feedback–based piezo actuator controller. Experimental results show the effectiveness of the proposed approach and its suitability for implementation in an existing robot

An improved recursive least square–based adaptive input shaping for zero residual vibration control of flexible system

In this article, a promoted method of adaptive input shaping based on recursive least square with forgetting factor is proposed to achieve zero residual vibration of time-varying flexible systems. First, the zero residual vibration condition of the flexible system is reviewed. Then, the mathematical analysis of recursive least square–based adaptive input shaping is presented; it can be seen that the traditional recursive least square method could calculate the least square solution with all historical I/O data. That is to say, with the increase in time and larger amount of I/O data, the current data could hardly affect the results of the updated input shapers’ coefficients; thus, the problems of insufficient adaptability and noise accumulation occur. So, a forgetting factor is introduced in the recursive calculation to give less calculation weight on historical data and improve the sensitivity of the current data; thus, the above-mentioned problem could be significantly avoided. At last, the verification experiments of adaptive input shaping are implemented on a two-link flexible manipulator, which is a classical flexible system with severely time-varying dynamics; the results validate the effectiveness of the improved adaptive input-shaping method for the vibration control of these flexible systems

IP Controller Design for Uncertain Two-Mass Torsional System Using Time-Frequency Analysis

With the development of industrial production, drive systems are demanded for larger inertias of motors and load machines, whereas shafts should be lightweight. In this situation, it will excite mechanical vibrations in load side, which is harmful for industrial production when the motor works. Because of the complexity of the flexible shaft, it is often difficult to calculate stiffness coefficient of the flexible shaft. Furthermore, only the velocity of driving side could be measured, whereas the driving torque, the load torque, and the velocity of load side are immeasurable. Therefore, it is inconvenient to design the controller for the uncertain system. In this paper, a low-order IP controller is designed for an uncertain two-mass torsional system based on polynomial method and time-frequency analysis (TFA). IP controller parameters are calculated by inertias of driving side and load side as well as the resonant frequency based on polynomial method. Therein, the resonant frequency is identified using the time-frequency analysis (TFA) of the velocity step response of the driving side under the open-loop system state, which can not only avoid harmful persistent start-stop excitation signal of the traditional method, but also obtain high recognition accuracy under the condition of weak vibration signal submerged in noise. The effectiveness of the designed IP controller is verified by groups of experiments. Experimental results show that good performance for vibration suppression is obtained for uncertain two-mass torsional system in a medium-low shaft stiffness condition

Iterative learning control of high-acceleration positioning table via sensitivity identification

Through the convergence analysis of the iterative learning control, it can be seen that the inverted model of the sensitivity can be used as the update law of iterative learning controller. However, due to the little knowledge in modeling uncertainty and in feedback controllers embedded in drivers with hard-to-obtain parameters, it is difficult to directly calculate the transfer function of the sensitivity. To solve this problem, this article presents sensitivity identification–based iterative learning controller for a high-acceleration positioning table actuated by the voice coil motors. In the sensitivity identification process, a frequency sweep signal was exerted to the closed-loop system as the equivalent disturbance, and the perturbation of the tracking error was gathered as output data. Then, the autoregressive model of the sensitivity was gained conveniently. Finally, the proposed sensitivity identification–based iterative learning controller was implemented in the experimental setup with the maximum acceleration of 5.3 g. The experimental results show that the tracking error of the X - and Y -axes decreased from 530 and 2480 µm to 6 and 18 µm, respectively, after the third iteration of the proposed iterative learning controller method, and the identified sensitivity can be easily designed in the iterative learning controller to greatly improve the tracking performance

MOTION CONTROL OF ELECTRICAL DRIVEN FREE-FLOATING SPACE MANIPULATOR

Abstract. In this paper, motion control of electrical driven free floating space manipulator is considered.The dynamics of the free-floating space manipulator are derived based on Virtual Manipulator Approach(VMA),which allows us to develop controller in joint space. Then a proportional plus derivative feedback control system with disturbance observer(DOB)are employed to compensate joint coupling,unmodeled uncertainties, as well as external disturbances herein both manipulator dynamics and motor dynamics. In addition, a feed-forward control of a simple predictor was added to improve performance further. At last,simulation results from a six-link electrical driven space manipulator show that the developed controller achieves superior performance with less tracking error, especially when internal model uncertainties and large external distur-bances are present

TGF-β1 Inhibits Multiple Caspases Induced by TNF-α in Murine Osteoblastic MC3T3-E1 Cells

Tumor necrosis factor α (TNF-α) is a proinflammatory cytokine that induces apoptosis in a number of cell systems, including osteoblasts. Transforming growth factor β1 (TGF-β1) is an abundant growth factor that is known to stimulate bone formation. This study was designed to examine the role of TGF-β1 on TNF-α-induced apoptosis in murine osteoblastic MC3T3-E1 cells. Total RNA was extracted from MC3T3-E1 cells treated with 20 ng/ml of TNF-α, 10 ng/ml of TGF-β1, or combination, for 6 h. TNF-α exerted a variety of effects on the apoptotic gene expression in osteoblasts. Ribonuclease protection assays (RPA) revealed that TNF-α upregulated the mRNA levels of caspase-1, -7, -11, -12, and FAS. Western blot analysis showed enhanced processing of caspase-1, -7, -11, and -12, with the appearance of their activated enzymes 24 h after TNF-α treatment. In addition, caspase-3-like activity was significantly activated following TNF-α treatment. Levels of cleaved poly(ADP-ribose) polymerase and FAS protein were also elevated by TNF-α. Finally, Hoechst staining, terminal deoxynucleotidyl-transferase nick-end labeling (TUNEL) assay, and oligonucleosome ELISA all indicated that TNF-α induced apoptosis. In contrast, the addition of TGF-β1 attenuated all of the aforementioned effects of TNF-α. Our results demonstrate that TGF-β1 can decrease TNF-α-induced apoptosis in murine osteoblasts at least in part by attenuating TNF-α-induced caspase gene expression