Real-time control of geometry and stiffness in adaptive structures

Adaptive structures are those which can adjust their geometry, stiffness and damping on demand to] meet the changes in their loading environment. With the advents in the piezo-electric device and composite material technologies, such structures are already being considered seriously in aero-space industry. The success of these structures depends largely on the effective control of the structure through the actuators, and the sensors embedded in their load carrying members. This work outlines the basic theory for the geometry, stiffness and damping control. Necessary and sufficient conditions for stress free geometry control in statically determinate and indeterminate adaptive discrete structures are given. In particular, for the subset of discrete adaptive structures viz. adaptive truss structures the equations similar to the forward and inverse kinematic equations of open loop mechanical manipulators are derived. These equations describe the large geometry control under show motion (i.e., no inertia forces) assumption. Two criteria for choosing the optimum control from among the possible ones are established. A fast algorithm based on variable order variable step multistep method is given that can compute the controls for a large maneuver in real-time. Numerical results from the algorithm are presented. As an example of damping and stiffness alteration on demand, the vibration control in adaptive trusses by means of elongations and elongation-rates of the active elements is also given.     

Bandwidth and frequency agile MIMO antenna for cognitive vehicular communications

A reconfigurable MIMO antenna for heterogeneous vehicular networks is reported in this paper. The frequency and bandwidth characteristics of the MIMO antenna can be reconfigured to meet multi-standard and multi-frequency requirements in automobiles. The antenna element evolved from an edge-chamfered ultra-wideband (UWB) antenna operating from 2.1 to >15 GHz. The bandwidth reconfiguration is achieved through the selection of excitation paths connecting the feed and radiator. The feedline selection is performed using PIN diodes, making the antenna operate in three distinct modes, namely, UWB mode (Mode 1: 2.1–>15 GHz), industrial, scientific and medical/Internet of Things (ISM/IoT) mode (Mode 2: 2.45 GHz), and wireless local area network (WLAN) mode (Mode 3: 5–6 GHz). The feed path corresponding to Mode 2 and Mode 3 is incorporated with a suitable filtering network to shape the frequency response of the antenna based on the user's requirements. Owing to the requirement of cognitive selection of frequency bands, the frequency tunability in Mode 2 is realized using varactor diodes. The varactor-incorporated feed path reconfigures the center frequency between 2.45 and 3.5 GHz. The proposed MIMO antenna offers gain and total efficiency greater than 2.94 dBi and 76%, respectively. The prototype of the 4-port MIMO antenna is being fabricated to test its functionality in real time.     

Equality between Functionals in the Presence of Coproducts

We consider the lambda calculus obtained from the simply typed calculus by adding products, coproducts, and a terminal type. We prove the following theorem: The equations provable in this calculus are precisely those true in any set-theoretic model with an infinite base type.     

A knowledge-based architecture for distributed fault analysis in power networks

Power industry around the world is facing several changes since deregulation with constant pressure put on improving security, reliability and quality of the power supply. Computational fault analysis and diagnosis of power networks have been active research topics with several theories and algorithms proposed. This paper proposes a distributed diagnostic algorithm for fault analysis in power networks. Distributed architecture for power network fault analysis (DAPFA) is an intelligent, model-based diagnostic algorithm that incorporates a hierarchical power network representation and model. The architecture is based on the industry’s substation automation implementation standards. The structural and functional model is a multi-level representation with each level depicting a more complex grouping of components than its predecessor in the hierarchy. The distributed functional representation contains the behavioral knowledge related to the components of that level in the structural model.The diagnostic algorithm of DAPFA is designed to perform fault analysis in pre-diagnostic and diagnostic levels. Pre-diagnostic phase provides real-time analysis while the diagnostic phase provides the final diagnostic analysis. The diagnostic algorithm incorporates knowledge-based and model-based reasoning mechanisms with one of the model levels represented as a network of neural nets. The relevant algorithms and techniques are discussed. The resulting system has been implemented on a New Zealand sub-system and the results are analyzed.     

Comparison of RBF and MLP neural networks to solve inverse kinematic problem for 6R serial robot by a fusion approach

In this paper, a fusion approach to determine inverse kinematics solutions of a six degree of freedom serial robot is proposed. The proposed approach makes use of radial basis function neural network for prediction of incremental joint angles which in turn are transformed into absolute joint angles with the assistance of forward kinematics relations. In this approach, forward kinematics relations of robot are used to obtain the data for training of neural network as well to estimate the deviation of predicted inverse kinematics solution from the desired solution. The effectiveness of the fusion process is shown by comparing the inverse kinematics solutions obtained for an end-effector of industrial robot moving along a specified path with the solutions obtained from conventional neural network approaches as well as iterative technique. The prominent features of the fusion process include the accurate prediction of inverse kinematics solutions with less computational time apart from the generation of training data for neural network with forward kinematics relations of the robot.     

A class-E power amplifier with high efficiency and high power-gain for wireless sensor network

In the literature, a number of two-stage class-E power amplifiers have been reported for wireless sensor network (WSN) applications. However, they suffer from the requirement of larger silicon area, inductors with high quality factor, large number of off-chip decoupling capacitors, high input power, voltage stress handling capability and efficiency degradation due to finite on-resistance and surplus capacitance of switching transistors. In order to overcome these limitations and to enhance the power added efficiency, two novel two-stage class-E power amplifiers denoted as PA1 and PA2 are proposed in this paper. Both the amplifiers use a driver amplifier with capacitive feedback and pi-matching at the input. PA1 uses a main amplifier with negative-capacitance cascode topology. PA2 uses a diode connected NMOS auxiliary transistor with RC source degeneration in the driver amplifier, negative-capacitance cascode configuration with a parallel LC-tuning circuit in the main amplifier. To evaluate the efficacy of these circuits, the proposed power amplifiers are implemented in UMC 0.18-µm standard RFCMOS process with the supply voltage of 3.0 V and the operating frequency of 2.45 GHz and studied through post-layout simulation using Cadence Virtuoso (IC616) Analog Design Environment. From this study, it is found that the proposed power amplifiers have the power added efficiency of (45.02 %, 54.87 %), the saturated output power at 1-dB compression point (P1-dB) of (21.52 dBm, 23.17 dBm), the power gain of (27.29 dB, 28.74 dB) and the output referred intercept point (OIP3) of (19.41 dBm, 22.67 dBm), respectively. Both of these power amplifiers have higher figure of merit (FoM) of (53.80, 57.98) when compared to other reported works. It is observed that the proposed power amplifiers are suitable to operate under low input power of -8 dBm and hence it meets the requirement of WSN applications.