Optimal control of grinding mill circuit using model predictive static programming: A new nonlinear MPC paradigm

The recently developed reference-command tracking version of model predictive static programming (MPSP) is successfully applied to a single-stage closed grinding mill circuit. MPSP is an innovative optimal control technique that combines the philosophies of model predictive control (MPC) and approximate dynamic programming. The performance of the proposed MPSP control technique, which can be viewed as a `new paradigm' under the nonlinear MPC philosophy, is compared to the performance of a standard nonlinear MPC technique applied to the same plant for the same conditions. Results show that the MPSP control technique is more than capable of tracking the desired set-point in the presence of model-plant mismatch, disturbances and measurement noise. The performance of MPSP and nonlinear MPC compare very well, with definite advantages offered by MPSP. The computational speed of MPSP is increased through a sequence of innovations such as the conversion of the dynamic optimization problem to a low-dimensional static optimization problem, the recursive computation of sensitivity matrices and using a closed form expression to update the control. To alleviate the burden on the optimization procedure in standard MPC, the control horizon is normally restricted. However, in the MPSP technique the control horizon is extended to the prediction horizon with a minor increase in the computational time. Furthermore, the MPSP technique generally takes only a couple of iterations to converge, even when input constraints are applied. Therefore, MPSP can be regarded as a potential candidate for online applications of the nonlinear MPC philosophy to real-world industrial process plants. (C) 2014 Elsevier Ltd. All rights reserved.

Small signal Nonquasi-Static Model for Common Double-Gate MOSFETs Adapted to Gate Oxide Thickness Asymmetry

We present a physics-based closed form small signal Nonquasi-static (NQS) model for a long channel Common Double Gate MOSFET (CDG) by taking into account the asymmetry that may prevail between the gate oxide thickness. We use the unique quasi-linear relationship between the surface potentials along the channel to solve the governing continuity equation (CE) in order to develop the analytical expressions for the Y parameters. The Bessel function based solution of the CE is simplified in form of polynomials so that it could be easily implemented in any circuit simulator. The model shows good agreement with the TCAD simulation at-least till 4 times of the cut-off frequency for different device geometries and bias conditions.

Study of motion around a static black hole in Einstein and Lovelock gravity

We study motion around a static Einstein and pure Lovelock black hole in higher dimensions. It is known that in higher dimensions bound orbits exist only for a pure Lovelock black hole in all even dimensions, D = 2N + 2, where N is the degree of Lovelock polynomial action. In particular, we compute periastron shift and light bending, and the latter is given by one of the transverse spatial components of the Riemann curvature tensor. We also consider the pseudo-Newtonian potentials and Kruskal coordinates.

Spatio-temporal correlations in aqueous systems: computational studies of static and dynamic heterogeneity by 2D-IR spectroscopy

Local heterogeneity is ubiquitous in natural aqueous systems. It can be caused locally by external biomolecular subsystems like proteins, DNA, micelles and reverse micelles, nanoscopic materials etc., but can also be intrinsic to the thermodynamic nature of the aqueous solution itself (like binary mixtures or at the gas-liquid interface). The altered dynamics of water in the presence of such diverse surfaces has attracted considerable attention in recent years. As these interfaces are quite narrow, only a few molecular layers thick, they are hard to study by conventional methods. The recent development of two dimensional infra-red (2D-IR) spectroscopy allows us to estimate length and time scales of such dynamics fairly accurately. In this work, we present a series of interesting studies employing two dimensional infra-red spectroscopy (2D-IR) to investigate (i) the heterogeneous dynamics of water inside reverse micelles of varying sizes, (ii) supercritical water near the Widom line that is known to exhibit pronounced density fluctuations and also study (iii) the collective and local polarization fluctuation of water molecules in the presence of several different proteins. The spatio-temporal correlation of confined water molecules inside reverse micelles of varying sizes is well captured through the spectral diffusion of corresponding 2D-IR spectra. In the case of supercritical water also, we observe a strong signature of dynamic heterogeneity from the elongated nature of the 2D-IR spectra. In this case the relaxation is ultrafast. We find remarkable agreement between the different tools employed to study the relaxation of density heterogeneity. For aqueous protein solutions, we find that the calculated dielectric constant of the respective systems unanimously shows a noticeable increment compared to that of neat water. However, the `effective' dielectric constant for successive layers shows significant variation, with the layer adjacent to the protein having a much lower value. Relaxation is also slowest at the surface. We find that the dielectric constant achieves the bulk value at distances more than 3 nm from the surface of the protein.

Energy Sharing for Multiple Sensor Nodes With Finite Buffers

We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting (EH) source. Sensor nodes periodically sense the random field and generate data, which is stored in the corresponding data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in an energy buffer. Sensor nodes receive energy for data transmission from the EH source. The EH source has to efficiently share the stored energy among the nodes to minimize the long-run average delay in data transmission. We formulate the problem of energy sharing between the nodes in the framework of average cost infinite-horizon Markov decision processes (MDPs). We develop efficient energy sharing algorithms, namely Q-learning algorithm with exploration mechanisms based on the epsilon-greedy method as well as upper confidence bound (UCB). We extend these algorithms by incorporating state and action space aggregation to tackle state-action space explosion in the MDP. We also develop a cross entropy based method that incorporates policy parameterization to find near optimal energy sharing policies. Through simulations, we show that our algorithms yield energy sharing policies that outperform the heuristic greedy method.

Part I: High-Voltage MOS Device Design for Improved Static and RF Performance

In this paper, for the first time, the key design parameters of a shallow trench isolation-based drain-extended MOS transistor are discussed for RF power applications in advanced CMOS technologies. The tradeoff between various dc and RF figures of merit (FoMs) is carefully studied using well-calibrated TCAD simulations. This detailed physical insight is used to optimize the dc and RF behavior, and our work also provides a design window for the improvement of dc as well as RF FoMs, without affecting the breakdown voltage. An improvement of 50% in R-ON and 45% in RF gain is achieved at 1 GHz. Large-signal time-domain analysis is done to explore the output power capability of the device.

Emergent Intelligence Based QoS Routing in MANET

Mobile Ad hoc Networks (MANETs) are self-organized, infrastructureless, decentralized wireless networks consist of a group of heterogeneous mobile devices. Due to the inherent characteristics of MANE -Ts, such as frequent change of topology, nodes mobility, resource scarcity, lack of central control, etc., makes QoS routing is the hardest task. QoS routing is the task of routing data packets from source to destination depending upon the QoS resource constraints, such as bandwidth, delay, packet loss rate, cost, etc. In this paper, we proposed a novel scheme of providing QoS routing in MANETs by using Emergent Intelligence (El). The El is a group intelligence, which is derived from the periodical interaction among a group of agents and nodes. We logically divide MANET into clusters by centrally located static agent, and in each cluster a mobile agent is deployed. The mobile agent interacts with the nodes, neighboring mobile agents and static agent for collection of QoS resource information, negotiations, finding secure and reliable nodes and finding an optimal QoS path from source to destination. Simulation and analytical results show that the effectiveness of the scheme. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.ore/licenscs/by-nc-nd/4.0/). Peer-review under responsibility of the Conference Program Chairs

A novel index for identification of weak nodes for reactive compensation to improve voltage stability

In this study, a new reactive power loss index (RPLI) is proposed for identification of weak buses in the system. This index is further used for determining the optimal locations for placement of reactive compensation devices in the power system for additional voltage support. The new index is computed from the reactive power support and loss allocation algorithm using Y-bus method for the system under intact condition and as well as critical/severe network contingencies cases. Fuzzy logic approach is used to select the important and critical/severe line contingencies from the contingency list. The inherent characteristics of the reactive power in system operation is properly addressed while determining the reactive power loss allocation to load buses. The proposed index is tested on sample 10-bus equivalent system and 72-bus practical equivalent system of Indian southern region power grid. The validation of the weak buses identification from the proposed index with that from other existing methods in the literature is carried out to demonstrate the effectiveness of the proposed index. Simulation results show that the identification of weak buses in the system from the new RPLI is completely non-iterative, thus requires minimal computational efforts as compared with other existing methods in the literature.

A Study of Pressure-Dependent Squeeze Film Stiffness as a Resonance Modulator Using Static and Dynamic Measurements

We report on the resonant frequency modulation of inertial microelectromechanical systems (MEMS) structures due to squeeze film stiffness over a range of working pressures. Squeeze film effects have been studied extensively, but mostly in the context of damping and Q-factor determination of dynamic MEMS structures, typically suspended over a fixed substrate with a very thin air gap. Here, we show with experimental measurements and analytical calculations how the pressure-dependent air springs (squeeze film stiffness) change the resonant frequency of an inertial MEMS structure by as much as five times. For capturing the isolated effect of the squeeze film stiffness, we first determine the static stiffness of our structure with atomic force microscope probing and then study the effect of the air spring by measuring the dynamic response of the structure, thus finding the resonant frequencies while varying the air pressure from 1 to 905 mbar. We also verify our results by analytical and Finite Element Method calculations. Our findings show that the pressure-dependent squeeze film stiffness can affect a rather huge range of frequency modulation (>400%) and, therefore, can be used as a design parameter for exploiting this effect in MEMS devices. 2014-0310]

Performance evaluation of mobile agent based resource management protocol for MANETs

The nodes with dynamicity, and management without administrator are key features of mobile ad hoc networks (1VIANETs). Increasing resource requirements of nodes running different applications, scarcity of resources, and node mobility in MANETs are the important issues to be considered in allocation of resources. Moreover, management of limited resources for optimal allocation is a crucial task. In our proposed work we discuss a design of resource allocation protocol and its performance evaluation. The proposed protocol uses both static and mobile agents. The protocol does the distribution and parallelization of message propagation (mobile agent with information) in an efficient way to achieve scalability and speed up message delivery to the nodes in the sectors of the zones of a MANET. The protocol functionality has been simulated using Java Agent Development Environment (JADE) Framework for agent generation, migration and communication. A mobile agent migrates from central resource rich node with message and navigate autonomously in the zone of network until the boundary node. With the performance evaluation, it has been concluded that the proposed protocol consumes much less time to allocate the required resources to the nodes under requirement, utilize less network resources and increase the network scalability. (C) 2015 Elsevier B.V. All rights reserved.

Electromechanical analysis of piezoelectric bimorph actuator in static state considering the nonlinearity at high electric field

This article contains electromechanical analysis of a piezoelectric bimorph actuator at high electric field by incorporating second-order constitutive equations of piezoelectric material. Tip deflection, block force, block moment, block load, output strain energy, output energy density, input electrical energy, and energy efficiency are analytically derived for the actuator at high electric field. The analysis shows that output energy and energy density increase more rapidly at high electric field, compared to the prediction by the linear model. The analysis shows energy efficiency depends on electric field. Some analytical results are validated with the published experimental results.

Campaigning in Heterogeneous Social Networks: Optimal Control of SI Information Epidemics

We study the optimal control problem of maximizing the spread of an information epidemic on a social network. Information propagation is modeled as a susceptible-infected (SI) process, and the campaign budget is fixed. Direct recruitment and word-of-mouth incentives are the two strategies to accelerate information spreading (controls). We allow for multiple controls depending on the degree of the nodes/individuals. The solution optimally allocates the scarce resource over the campaign duration and the degree class groups. We study the impact of the degree distribution of the network on the controls and present results for Erdos-Renyi and scale-free networks. Results show that more resource is allocated to high-degree nodes in the case of scale-free networks, but medium-degree nodes in the case of Erdos-Renyi networks. We study the effects of various model parameters on the optimal strategy and quantify the improvement offered by the optimal strategy over the static and bang-bang control strategies. The effect of the time-varying spreading rate on the controls is explored as the interest level of the population in the subject of the campaign may change over time. We show the existence of a solution to the formulated optimal control problem, which has nonlinear isoperimetric constraints, using novel techniques that is general and can be used in other similar optimal control problems. This work may be of interest to political, social awareness, or crowdfunding campaigners and product marketing managers, and with some modifications may be used for mitigating biological epidemics.

Optimal Guidance for Accurate Lunar Soft Landing with Minimum Fuel Consumption using Model Predictive Static Programming

In this paper the soft lunar landing with minimum fuel expenditure is formulated as a nonlinear optimal guidance problem. The realization of pinpoint soft landing with terminal velocity and position constraints is achieved using Model Predictive Static Programming (MPSP). The high accuracy of the terminal conditions is ensured as the formulation of the MPSP inherently poses final conditions as a set of hard constraints. The computational efficiency and fast convergence make the MPSP preferable for fixed final time onboard optimal guidance algorithm. It has also been observed that the minimum fuel requirement strongly depends on the choice of the final time (a critical point that is not given due importance in many literature). Hence, to optimally select the final time, a neural network is used to learn the mapping between various initial conditions in the domain of interest and the corresponding optimal flight time. To generate the training data set, the optimal final time is computed offline using a gradient based optimization technique. The effectiveness of the proposed method is demonstrated with rigorous simulation results.