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Controlling and maintaining the orientation of the balloon-borne gondola for high-altitude flight is a prerequisite for ensuring the pointing control of observation instruments. When the balloon-borne gondola is flying in the stratosphere of the atmosphere, the existing external interferences will be converted into the coupling moment to the azimuth control system. Meanwhile, those uncertain factors and the frictional nonlinearity of the control system will also cause a certain magnitude of coupling moment. The existence of such coupling moment largely impacts on the accuracy and stability of the orientation control for the angular momentum exchange devices of the balloon-borne gondola. To address such an issue, this paper proposes and implements a novel type of integrated decoupler device. With this decoupler adopted, the aziDmuth control system could sense the existence of coupling torque and azimuth fluctuations quickly and suppress the influences of external interference, uncertain factors, and system structure nonlinearity on the azimuth control effectively, thereby improving the control accuracy of the azimuth control system. Both simulations and experiments are conducted to verify the effectiveness of the proposed device. The results show that the integration of the decoupler and the controller of the azimuth control system provide the azimuth control of the balloon-borne gondola with high accuracy and stability. Such a decoupler device design has a broad potential and could not only be used for balloon-borne gondola control but also could be applied onto other control systems using angular momentum exchange devices as actuators.

As a key feature of networked control systems (NCSs), the time delays induced by communication medium sharing and data exchange over the system components could largely degrade the NCS performances or may even cause system instability, and thus, it is of critical importance to reduce time delays within NCSs. This paper studies the time-delay reduction problem in distributed NCSs and presents a dual-way data scheduling mechanism for time-delay reductions in delay-bounded NCSs with time-varying delays. We assess the time delays and their influences on the NCSs first with various delay factors being considered and then describe a one-way scheduling mechanism for network-delay reductions in NCSs. Based upon such a method, a dual-way scheduling algorithm is finally proposed for distributed NCSs with different types of transmitted data packets. Experiments are conducted on a remote teaching platform to verify the effectiveness of the proposed dual-way scheduling mechanism. Results demonstrate that, with the stability time-delay bound considered within the scheduling process, the proposed mechanism is effective for NCS time-delay reductions while addressing the stability, control accuracy, and settling time issues efficiently. Such a proposed mechanism could also be implemented together with some other existing control algorithms for time-delay reductions in NCSs. Our work could provide both useful theoretical guidance and application references for stable tracking control of delay-bounded NCSs. Published version

As one of the key techniques determining the overall system performances, efficient and reliable algorithms for improving the classification accuracy of motor imagery (MI) based electroencephalography (EEG) signals are highly desired for the development of brain-computer interface (BCI) systems. In this study, we propose, for the first time to the best of our knowledge, a novel data adaptive empirical wavelet transform (EWT) based signal decomposition method for improving the classification accuracy of MI based EEG signals. Specifically, to reduce the system complexity and execution time, the proposed method selects 18 electrodes out of 118 to analyze the non-stationary and nonlinear EEG signal behaviors. Meanwhile, the method adopts the Welch power spectral density (PSD) analysis method for single mode selection out of the total 10 for each channel, and the Hilbert transform (HT) method for both instantaneous amplitude (IA) and instantaneous frequency (IF) signal components extraction for each selected mode. With seven commonly used machine-learning classifiers adopted, extensive experiments were conducted with the benchmark dataset IVa from BCI competition III to evaluate the performance of the proposed method. Results show that with the IA and IF component features being tested using the least-square support vector machine (LS-SVM) classifier, the EWT method achieves an average classification accuracy of 95.2% and 94.6% respectively, which is higher as compared with the existing methods. While for every participant, a classification accuracy of at least 80% could be achieved by employing a single feature only. Results also show that a combination of EWT and higher order statistics features, which contain both kurtosis and skewness of the extracted instantaneous components, help achieve a higher success rate. The better performances of EWT over those of the existing methods demonstrate the effectiveness and great potential of EWT for BCI system applications.

As key components of robot hydraulic drive control system, the hydraulic pipelines (HPs) largely determine the robot performances, and thus, the selection of such components is of critical importance to robot design. In practice, however, there exists a trade-off between the respective requirements on the HPs internal structures and their stiffness properties, since a control system typically requires HPs to be as light as possible, while the breaking stresses like vibrations and pulsed pressures require HPs to be with high stiffness and strength. To help achieve a balance between such HPs requirements, we evaluate the overall stresses experienced by a simply-supported robot HP and estimate the HP fatigue life under the hybrid excitation of water-hammer waves and external vibrations for the first time, to the best of our knowledge. Specifically, we propose a non-steady flowing fluid mathematical model to describe the HP pressures based on water-hammer wave theory and solve it using the method of characteristic (MoC) first, and then present another mathematical model to integrate the external robot link vibrations into such a model. The equivalent stresses for a single-axis pipeline is calculated and its fatigue life is also estimated with this proposed model. Finally, experiments on the pipeline fracture and damage mechanics are conducted to verify the effectiveness of those models. Results show that the practical operation life of the robot HP matches well with the theoretically estimated fatigue life under the hybrid excitation of water-hammer waves and external vibrations.

This paper proposes a new robust adaptive cerebellar model articulation controller (CMAC) neural network-based multisliding mode control strategy for a class of unmatched uncertain nonlinear systems. Specifically, by employing a stepwise recursion-based multisliding mode method, such a proposed strategy is able to obtain the virtual variables and the actual control inputs of each order first, and then it reduces the conservativeness for controller parameter design by adopting the CMAC neural network to learn both system uncertainties and virtual control variable derivatives of each order online. Meanwhile, with the hyperbolic tangent function being chosen to replace the sign function in the variable structured control components, the proposed strategy is able to avoid the chattering effects caused by the discontinuous inputs. The stability analysis shows that the proposed control strategy ensures that both the system tracking errors and the sliding modes of each order could converge exponentially to any saturated layer being set. The control strategy was also applied onto a passive electrohydraulic servo loading system for verifications, and simulation results show that such a proposed control strategy is robust against all system nonlinearities and external disturbances with much higher control accuracy being achieved.

With rapid advancements in artificial intelligence and mobile robots, some of the tedious yet simple jobs in modern libraries, like book accessing and returning (BAR) operations that had been fulfilled manually before, could be undertaken by robots. Due to the limited accuracies of the existing positioning and navigation (P&amp, N) technologies and the operational errors accumulated within the robot P&amp, N process, however, most of the current robots are not able to fulfill such high-precision operations. To address these practical issues, we propose, for the first time (to the best of our knowledge), to combine the binocular vision and Quick Response (QR) code identification techniques together to improve the robot P&amp, N accuracies, and then construct an autonomous library robot for high-precision BAR operations. Specifically, the binocular vision system is used for dynamic digital map construction and autonomous P&amp, N, as well as obstacle identification and avoiding functions, while the QR code identification technique is responsible for both robot operational error elimination and robotic arm BAR operation determination. Both simulations and experiments are conducted to verify the effectiveness of the proposed technique combination, as well as the constructed robot. Results show that such a technique combination is effective and robust, and could help to significantly improve the P&amp, N and BAR operation accuracies, while reducing the BAR operation time. The implemented autonomous robot is fully-autonomous and cost-effective, and may find applications far beyond libraries with only sophisticated technologies employed.

Organic semiconductors have potentials for a broad range of applications; however, it is difficult to be integrated with traditional inorganic material to meet the need of further application. Based on low-temperature silicon nitride (SiNx) deposition technique, here we demonstrate a hybrid structure fabricated by directly depositing high-quality SiNx on organic polymer film Poly[2-(2',5'-bis(2"-ethylhexyloxy)- phenyl) -1,4-phenylene vinylene] (BEHP-PPV). Stacked BEHP-PPV/SiNx hybrid structures with different periods are obtained and their optical properties are systematically characterized. Moreover, a group of BEHP/PPV embedded SiNx micro-disk is fabricated and amplification of spontaneous emission (ASE) is observed under optical pumping, further confirming that the gain properties of BEHP/PPV are well preserved. Our technique offers a platform to fabricate organic/inorganic hybrid optical devices compatible with integrated components., 6 pages, 4 figures

High-quality SiNx films with controllable low stress and low optical loss are deposited at ultra-low temperature (75 °C) using inductively coupled plasma chemical vapor deposition (ICP-CVD). Two kinds of integrated photonic structures have been demonstrated that exemplify its viability as a photonic integration platform. A microcavity consists of two distributed Bragg reflectors (DBR) formed by alternating a total of 49 layers of SiNx and SiO2 with a total thickness of about 11.5 μm is grown without any cracks, confirming the excellent stress control in the process. Microring resonators are also fabricated in as-deposited planar SiNx waveguide layer using electron-beam lithography (EBL) and plasma etching. Average waveguide loss of 0.79 ± 0.22 dB/cm has been achieved in the range of 1550-1600 nm for ring radii larger than 40 μm. The ultra-low temperature grown SiNx with properties of low loss and low stress is therefore a promising photonic integration platform for various photonic integration applications.