An implicit discretization-based adaptive reaching law for discrete-time sliding mode control systems

In conventional reaching law approaches, the disturbance suppression is achieved at the cost of high-frequency chattering or increasing the complexity of algorithm such as adding a high-order disturbance compensator. This paper presents the design and analysis of a novel implicit discretization-based adaptive reaching law for discrete-time sliding mode control systems. First, the implicit Euler technique is introduced into the design of discrete reaching laws, and it is proved to be able to eliminate numerical chattering completely. By using a self-adaptive power term, the newly designed reaching law can obtain an arbitrarily small boundary layer of sliding surface, and at the different phases of sliding mode motion, the adaptive power parameter can automatically regulate its value to guarantee globally fast convergence without destroying the accuracy of sliding variable. Then, based on a predefined trajectory of sliding variable, the discrete-time sliding mode control law is developed to realize high control accuracy without additional design. Compared with previous methods, the main contribution of proposed reaching law lies in that it can acquire high-precision sliding mode motion and simultaneously eliminate numerical chattering in spite of complex uncertainties only by adjusting the adaptive power parameter. Finally, a simulation example on the piezomotor-driven linear stage is provided to verify the theoretical results.

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

Metode Power Control sebagai Manajemen Interferensi pada Komunikasi Device to Device

Teknologi komunikasi yang sedang berkembang saat ini sudah memasuki generasi ke-5 (5G), dengan salah satu solusi yang ditawarkan adalah komunikasi device to device (D2D). Skema yang digunakan pada makalah ini adalah cooperative D2D dengan Mobile User Equipment (MUE) yang berada jauh dari eNodeB (eNB). D2D User Equipment (DUE) berperan sebagai relay yang membantu MUE dalam meningkatkan kualitas layanan. Efek yang ditimbulkan komunikasi D2D adalah interferensi, sehingga untuk mengatasinya dilakukan metode power control. Makalah ini menggunakan tiga simulasi perbandingan, yaitu Tanpa Power Control, skema Power Control 1, dan Power Control 2. Skema yang digunakan pada Power Control 1 adalah skema fixed power control, sedangkan skema Power Control 2 menggunakan adaptive power control. Dengan menggunakan Cumulative Distribution Function (CDF), diperoleh hasil bahwa skema Power Control 1 mampu memperbaiki SINR sebesar 0,124 dB untuk downlink dan 0,0814 dB untuk uplink, sedangkan skema Power Control 2 mampu menaikkan SINR 0,0316 dB untuk downlink dan 0,0627 dB untuk uplink. Berdasarkan hasil akhir terkait SINR, throughput, dan CDF, metode Power Control 1 memiliki hasil yang lebih baik daripada metode Power Control 2.

A RFID-Integrated Framework for Tag Anti-Collision in UAV-Aided VANETs

In this paper, we investigate tags in anti-collision applications of radio frequency identification (RFID) technology in unmanned aerial vehicle (UAV)-aided vehicular ad hoc networks (VANETs). The integration of RFID technology in UAV-aided VANETs can provide reliable traffic-related services for vehicles. However, multiple tags’ simultaneous responses to a reader mounted on a UAV, denoted as tag collision, gravely affect the correct tag detection on each vehicle. Therefore, in order to decrease the collision probability and improve the throughput, we propose a multi-frequency tag identification method. In the proposed scheme, we devise a tag grouping method based on adaptive power control to make the reader dynamically match the optimal frame length. Based on the above matching results, we introduce a tag estimation method using the optimal weight to improve the accuracy of tag estimation. We theoretically analyze the closed-form expression of the security outage probability expression. Finally, our simulation results demonstrate that the proposed tag anti-collision scheme achieved significant performance superiority in terms of the throughput and identification time slots.