Engineering Commons^™

Reinforcement Learning Output Feedback Nn Control Using Deterministic Learning Technique, Bin Xu

Bin Xu

In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor–critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarantee that the partial persistent excitation condition of internal states is satisfied during tracking control to a periodic reference orbit. The uniformly ultimate boundedness of closed-loop signals is …

Output Feedback Adaptive Neural Control Without Seeking Spr Condition, Yongping Pan, Meng Joo Er, Rongjun Chen, Haoyong Yu

Yongping Pan

For output-feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low-pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output-feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output-feedback control law is introduced based on a state-feedback indirect ANC structure. An adaptive neural network (NN) observer is …

Eavesdropping Attacks In Wireless Ad Hoc Networks Under A Shadow Fading Environment, Xuran Li, Hong-Ning Dai, Qinglin Zhao, Qiu Wang

Hong-Ning Dai

This paper concerns the eavesdropping attacks in wireless ad hoc networks under a shadow fading environment. Most of previous studies have only concentrated on protecting the confidential communications at both the transmitters and the receivers and little attention has been paid on investigating the eavesdropping behaviors done by the malicious nodes. We therefore investigate the eavesdropping success rate of eavesdroppers equipped with various antenna models under various channels conditions, such as the path loss effect and the shadow fading effect. In particular, we formulate the eavesdropping attack problem with consideration of both simplistic directional antenna models and realistic antenna models. …

An Analytical Model On Eavesdropping Attacks In Wireless Networks, Xuran Li, Hong-Ning Dai, Qinglin Zhao

Hong-Ning Dai

This paper concerns the eavesdropping problem from the eavesdroppers’ perspective, which is new since most of previous studies only concentrate on the good nodes. We propose an analytical framework to investigate the eavesdropping attacks, taking account into various channel conditions and antenna models. Our extensive numerical results show that the probability of eavesdropping attacks heavily depends on the shadow fading effect, the path loss effect and the antenna models; particularly, they imply that using directional antennas at eavesdroppers can increase the probability of eavesdropping attacks when the path loss effect is less notable. This study is helpful for us to …

Compact Lna Drives 2.5-Ghz Base Stations, Chin-Leong Lim

Chin-Leong Lim

Fluctuations In Transient Response Of Adsorption-Based Plasmonic Sensors, Zeljko D. Cupic

Zeljko D Cupic

The basic parameters of a sensor element defining its ultimate performance are sensitivity and intrinsic noise. In plasmonic gas sensors both are determined by refractive index changes due to adsorption and desorption (a–d) of target analyte particles to the sensor active area. In this paper we present a general model that can be simultaneously used to determine sensitivity and intrinsic noise of a plasmonic sensor both during transients and in steady-state and is valid for multi-analyte environments. The model utilizes the conventional probabilistic approach. It is derived without any assumptions about the stochastic nature of the fundamental (a–d) process. It …

Comparison Of An Analytical Hierarchy Process And Fuzzy Axiomatic Design For Selecting Appropriate Photovoltaic Modules For Onboard Vehicle Design, Mahmoud M. Abdelhamid

Mahmoud M Abdelhamid

In this paper, the authors present an overview of the available commercial photovoltaic (PV) module options for powering onboard vehicle applications. The authors used two decision-making methodologies to determine the evaluation factors and the decision-making criteria necessary for assessing the suitability of a particular PV module type. In both the analytical hierarchy process (AHP) and the fuzzy axiomatic design (AD), the authors used at the input stage quality function deployment (QFD) to determine customer requirements for a vehicle with PV module capabilities. This approach is innovative in that evaluation depended upon data collected from PV manufacturers’ datasheets. This approach is …