Development of Sliding Mode Controller Based on Internal Model Controller for Higher Precision Electro-Optical Tracking System

The electro-optical tracking system (ETS) on moving platforms is affected by the vibration of the moving carrier, the wind resistance torque in motion, the uncertainty of mechanisms and the nonlinear friction between frames and other disturbances, which may lead to the instability of the electro-optical tracking platform. Sliding mode control (SMC) has strong robustness to system disturbances and unknown dynamic external signals, which can enhance the disturbance suppression ability of ETSs. However, the strong robustness of SMC requires greater switching gain, which causes serious chattering. At the same time, the tracking accuracy of SMC has room for further improvement. Therefore, in order to solve the chattering problem of SMC and improve the tracking accuracy of SMC, an SMC controller based on internal model control (IMC) is proposed. Compared with traditional SMC, the proposed method can be used to suppress the strongest disturbance with the smallest switching gain, effectively solving the chattering problem of the SMC, while improving the tracking accuracy of the system. In addition, to reduce the adverse influence of sensor noise on the control effect, lifting wavelet threshold de-noising is introduced into the control structure to further improve the tracking accuracy of the system. The simulation and experimental results verify the superiority of the proposed control method.

Optimal System for Improved Internal Model Control of Argon-Oxygen Decarburization Process Based on the Piecewise Linear Model and Time Constant of Filter Optimization

This paper presents an improved internal model control system to raise the efficiency of refining low-carbon ferrochrome. This control system comprises of a piecewise linearized transfer function and an improved internal model controller based on optimized time constant of the filter. The control system is mainly used to control the oxygen supply rate during the argon-oxygen refining for controlling the smelting temperature. The regulatory performance and servo of two closed-loop control schemes are compared between the improved internal model controller based on the optimized filter time 0000-0002-7606-6546and the internal model controller based on the fixed filter time constant. The simulation analysis shows that the piecewise linearized model and the optimization of the time constant of the filter improves the response time, stability, and anti-interference ability of the controller. Then, the proposed improved internal model controller is used to adjust the gas supply flow in 5 ton AOD furnace to control the smelting temperature. Ten production tests performed the effectiveness of the controlling refining optimal system. The analysis of the experimental data shows that the improved internal model control system can shorten the melting time and improve the melting efficiency. Thus, the application of the improved internal model control system in low-carbon ferrochrome refining is an interesting potential direction for future research.

Increased upper-limb sensory attenuation with age

The pressure of our own finger on the arm feels differently than the same pressure exerted by an external agent: the latter involves just touch, whereas the former involves a combination of touch and predictive output from the internal model of the body. This internal model predicts the movement of our own finger and hence the intensity of the sensation of the finger press is decreased. A decrease in intensity of the self-produced stimulus is called sensory attenuation. It has been reported that, due to decreased proprioception with age and an increased reliance on the prediction of the internal model, sensory attenuation is increased in older adults. In this study, we used a force-matching paradigm to test if sensory attenuation is also present over the arm and if aging increases sensory attenuation. We demonstrated that, while both young and older adults overestimate a self-produced force, older adults overestimate it even more showing an increased sensory attenuation. In addition, we also found that both younger and older adults self-produce higher forces when activating the homologous muscles of the upper limb. While this is traditionally viewed as evidence for an increased reliance on internal model function in older adults because of decreased proprioception, proprioception appeared unimpaired in our older participants. This begs the question of whether an age-related decrease in proprioception is really responsible for the increased sensory attenuation observed in older people.

Internal model control for rocket launcher position servo system based on improved wavelet neural network

This paper proposes an improved wavelet neural network-internal model controller (WNN-IMC) for the rocket launcher position servo system. Due to complex nonlinearities and uncertainties of external disturbances in the rocket launcher position servo system, it is vitally challenging to establish its accurate model by the mechanical modeling technique. A wavelet neural network (WNN) identification method is proposed to determine the system mathematical model through test datum, which optimized by the hybrid algorithm of differential evolution (DE) and particle swarm optimization (PSO). Then, the proposed method is applied to identify the semi-physical simulation platform of the rocket launcher velocity servo system. The results demonstrate that the validity of the DEPSO-WNN method is better than that of the WNN and PSO-WNN methods. Finally, compared with the WNN-IMC controller and the ADRC controller, the effectiveness of the improved WNN-IMC controller is verified by the semi-physical simulation experiments.

Interne Modellvariabilität von Ensemblesimulationen des gekoppelten Wellen- und Atmosphärenmodells GCOAST

<p>Ziel dieser Studie (Wiese et al., 2020) ist, die Signifikanz des Einflusses des Wellenmodells auf das regionale Atmosphärenmodell und die interne Modellvariabilität sowohl des Atmosphärenmodells, als auch des gekoppelten Systems bestehend aus Wellen- und Atmosphärenmodell zu bestimmen. In einer vorhergehenden Studie wurde gezeigt, dass die Rauigkeit, die im Wellenmodell berechnet wird, größer ist, als die Rauigkeit, die im Atmosphärenmodell approximiert wird, was zu Unterschieden im Atmosphärenmodell führt (Wiese et al. 2019). Hier soll nun untersucht werden, ob diese Unterschiede im Atmosphärenmodell signifikant sind.  Dazu werden Ensemblesimulation mit einem Referenz Setup (das Atmosphärenmodell sendet den Wind an das Wellenmodell) und dem gekoppelten Setup (zusätzlich zum Windaustausch, sendet das Wellenmodell die Rauigkeitslänge über dem Meer zurück an das Atmosphärenmodell) durchgeführt. Bei der Analyse der internen Modellvariabilität zwischen beiden Ensembles zeigt sich, dass die interne Modellvariabilität im gekoppelten Ensemble gegenüber dem Referenzensemble reduziert ist. Dieser Effekt tritt während Extremereignissen am stärksten auf, ist aber auch bei einer generellen Analyse der internen Modellvariabilität über den gesamten Zeitraum sichtbar. Außerdem können die Effekte der Kopplung von der internen Modellvariabilität unterschieden werden, da die Effekte der Kopplung größer sind, als die interne Modellvariabilität. Diese Studie zeigt daher das Potential sowohl in operationellen Systemen als auch Systemen für Klimastudien die Unsicherheit zu reduzieren, wenn das Wellenmodell mit dem Atmosphärenmodell gekoppelt wird. Hinzu kommt, dass die Effekte der Kopplung klar von der internen Modellvariabilität unterschieden werden können, wodurch außerdem eine verbesserte Übereinstimmung des gekoppelten Systems gegenüber dem Referenzensemble mit Beobachtungsdaten erzielt werden kann. In einem nächsten Schritt soll nun zusätzlich der Ozean gekoppelt und die Auswirkungen auf das gesamte System untersucht werden.</p> <p> </p> <p>Literatur:</p> <p>Wiese A, Stanev E, Koch W, Behrens A, Geyer B and Staneva J (2019) The Impact of the Two-Way Coupling between Wind Wave and Atmospheric Models on the Lower Atmosphere over the North Sea. Atmosphere. 10(7):386. doi: 10.3390/atmos10070386</p> <p>Wiese A, Staneva J, Ho-Hagemann HTM, Grayek S, Koch W and Schrum C (2020) Internal Model Variability of Ensemble Simulations With a Regional Coupled Wave-Atmosphere Model GCOAST. Front. Mar. Sci. 7:596843. doi: 10.3389/fmars.2020.596843</p>

Pendeteksi Pengenalan Emosi Pada Manusia Menggunakan Hidden Markov Model Dan Bidirectional Associative Memory Dengan Suara

Sistem pengendalian emosi seseorang melalui suara dapat menggunakan algoritma Hidden Markov Model (HMM). Namun, untuk melihat kinerja algoritma HMM dalam sistem aplikasi sudah optimal atau belum, diperlukan suatu perbandingan agar memperoleh hasil yang lebih maksimal. Maka dari itu peneliti melakukan unjuk kerja pengenalian emosi seseorang dengan menggunakan algoritma HMM dan algoritma Bidirectional Associative Memory (BAM) melalui suara. Hidden Markov Model (HMM) terdiri dari rantai markov pada bagian pertama yang menyembunyikan state, oleh karena itu perilaku internal model tetap tidak terlihat. Sedangkan algoritma BAM dapat memproses input yang tidak lengkap, karena adanya hubungan timbal balik antara dari lapisan output ke lapisan input. Pada algoritma BAM, nilai suara pengujian dan nilai sampel suara pelatihan yang diperoleh akan dicari nilai vektornya menggunakan pencarian nilai bobot yang dilakukan dengan cara mengubah matriks biner ke dalam matriks bipolar. Pada penelitian ini akan membuat sebuah sistem aplikasi yang dapat mendeteksi suara dalam bentuk emosi marah, bahagia, dan netral. Dan database yang digunakan adalah suara dari rekaman film. Penelitian ini dilakukan untuk menghasilkan sistem yang dapat mengenali probabilitas emosi pada kategori marah, bahagia dan netral, yaitu dengan menunjukkan unjuk kerja dari kedua metode sehingga kita dapat mengetahui metode mana menghasilkan output yang maksimal.