Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Automatic synthesis of analog layout : a survey

A review of recent research in the automatic synthesis of physical geometry for analog integrated circuits is presented. On introduction, an explanation of the difficulties involved in analog layout as opposed to digital layout is covered. Review of the literature then follows. Emphasis is placed on the exposition of general methods for addressing problems specific to analog layout, with the details of specific systems only being given when they surve to illustrate these methods well. The conclusion discusses problems remaining and offers a prediction as to how technology will evolve to solve them. It is argued that although progress has been and will continue to be made in the automation of analog IC layout, due to fundamental differences in the nature of analog IC design as opposed to digital design, it should not be expected that the level of automation of the former will reach that of the latter any time soon

The Hubble Space Telescope Cluster Supernova Survey: V. Improving the Dark Energy Constraints Above z>1 and Building an Early-Type-Hosted Supernova Sample

We present ACS, NICMOS, and Keck AO-assisted photometry of 20 Type Ia supernovae SNe Ia from the HST Cluster Supernova Survey. The SNe Ia were discovered over the redshift interval 0.623 < z < 1.415. Fourteen of these SNe Ia pass our strict selection cuts and are used in combination with the world's sample of SNe Ia to derive the best current constraints on dark energy. Ten of our new SNe Ia are beyond redshift $z=1$, thereby nearly doubling the statistical weight of HST-discovered SNe Ia beyond this redshift. Our detailed analysis corrects for the recently identified correlation between SN Ia luminosity and host galaxy mass and corrects the NICMOS zeropoint at the count rates appropriate for very distant SNe Ia. Adding these supernovae improves the best combined constraint on the dark energy density \rho_{DE}(z) at redshifts 1.0 < z < 1.6 by 18% (including systematic errors). For a LambdaCDM universe, we find \Omega_\Lambda = 0.724 +0.015/-0.016 (68% CL including systematic errors). For a flat wCDM model, we measure a constant dark energy equation-of-state parameter w = -0.985 +0.071/-0.077 (68% CL). Curvature is constrained to ~0.7% in the owCDM model and to ~2% in a model in which dark energy is allowed to vary with parameters w_0 and w_a. Tightening further the constraints on the time evolution of dark energy will require several improvements, including high-quality multi-passband photometry of a sample of several dozen z>1 SNe Ia. We describe how such a sample could be efficiently obtained by targeting cluster fields with WFC3 on HST.Comment: 27 pages, 11 figures. Submitted to ApJ. This first posting includes updates in response to comments from the referee. See http://www.supernova.lbl.gov for other papers in the series pertaining to the HST Cluster SN Survey. The updated supernova Union2.1 compilation of 580 SNe is available at http://supernova.lbl.gov/Unio

Multidisciplinary and Multi-Objective Optimal Design of a Cascade Control System for a Flexible Wing with Embedded Control Surfaces Having Actuator Dynamics

A multidisciplinary and multi-objective optimization approach that integrates the design of the control surfaces’ sizes, active control systems, and estimator for an aircraft’s wing with three control surfaces is developed. Due to its attractive stability robustness properties, a control system based on the LQR (Linear Quadratic Regulator) is built for each control surface. The geometrical parameters of the control surfaces such as the span wise and chord lengths, the design details of the LQR penalty matrices, and the locations of the estimator poles are tuned by a widely used multi-objective optimization algorithm called NSGA-II (Non-dominated Sorting Genetic Algorithm). Four objectives are considered: minimizing impacts of external gust loads, maximizing stability robustness and extending flutter boundaries, reducing control energy consumption, and minimizing the Frobenius norm of the estimator gains. The solution of the multi-objective optimization problem is a set called Pareto set and the set of the corresponding function evaluation is called Pareto front. The solution set contains various geometrical configurations of the control surfaces with different feedback gains, which represent different degrees of optimal compromises among the design objectives. The optimization results demonstrate the competing relationship between the design objectives and necessity of handling the design problem in a multidisciplinary and multi-objective context. Three major results are obtained from inspecting the profiles of the closed-loop eigenvalues at various airspeeds 1) a unique control gain can be designed for the entire flight envelope, 2) the flutter boundaries can be infinitely extended, and 3) a unique observer gain can be designed for the entire flight envelope. The third chapter of this thesis presents a multi-objective and multidisciplinary optimal design of a cascade control system for an aircraft wing with four aerodynamic ailerons actuated by four identical brushless DC motors. The design of the control system is broken into a secondary and primary control algorithm. The primary control algorithm is designed based on the concept of LQR and then applied to mathematical model of the wing and its control surfaces to calculate their required deflections. The output of the primary controller serves as set-point for the secondary control loop which consists of the dynamic of the DC motor and Proportional Velocity (PV) based controller. Then, an optimal design of the control algorithms is carried out in multi-objective and multidisciplinary settings. Three objectives are considered: 1) the speed of response of the secondary controlled system must be faster than that of the primary one, 2) the controlled system must be robust against external disturbances affecting both control layers, and 3) optimal energy consumption. The decision variables of the primary as well as secondary control algorithms and the sizing elements of the control surfaces form the design parameter space of the optimization problem. Both geometrical and dynamic constraints are applied on the setup parameters. The multi-objective optimization problem (MOP) is solved by NSGA-II, which is one of the popular algorithms in solving MOPs. The solution of the MOP is a set of optimal control algorithms that represent the conflicts among the design objectives. Numerical simulations show that the design goals are achieved, the secondary control is always fast enough to prevent the propagation of disturbances to the primary loop, the inner and outer control algorithms are robust against disturbance inputs, and the primary control loop stays stable when the air stream velocity varies from 80 to 1000 (⁄) even at its worst relative stability value. The presented study may become the basis for multi-objective and multidisciplinary optimal design for aeroelastic structure having actuator dynamics

A framework for fine-grain synthesis optimization of operational amplifiers

This thesis presents a cell-level framework for Operational Amplifiers Synthesis (OASYN) coupling both circuit design and layout. For circuit design, the tool applies a corner-driven optimization, accounting for on-chip performance variations. By exploring the process, voltage, and temperature variations space, the tool extracts design worst case solution. The tool undergoes sensitivity analysis along with Pareto-optimality to achieve required specifications. For layout phase, OASYN generates a DRC proved automated layout based on a sized circuit-level description. Morata et al. (1996) introduced an elegant representation of block placement called sequence pair for general floorplans (SP). Like TCG and BSG, but unlike O-tree, B*tree, and CBL, SP is P-admissible. Unlike SP, TCG supports incremental update during operation and keeps the information of the boundary modules as well as their relative positions in the representation. Block placement algorithms that are based on SP use heuristic optimization algorithms, e.g., simulated annealing where generation of large number of sequence pairs are required. Therefore a fast algorithm is needed to generate sequence pairs after each solution perturbation. The thesis presents a new simple and efficient O(n) runtime algorithm for fast realization of incremental update for cost evaluation. The algorithm integrates sequence pair and transitive closure graph advantages into TCG-S* a superior topology update scheme which facilitates the search for optimum desired floorplan. Experiments show that TCG-S* is better than existing works in terms of area utilization and convergence speed. Routing-aware placement is implemented in OASYN, handling symmetry constraints, e.g., interdigitization, common centroid, along with congestion elimination and the enhancement of placement routability

Dye laser traveling wave amplifier

Injection locking was applied to a cavity-dumped coaxial flashlamp pumped dye laser in an effort to obtain nanosecond duration pulses which have both high energy and narrow-linewidth. In the absence of an injected laser pulse, the cavity-dumped dye laser was capable of generating high energy (approx. 60mJ) nanosecond duration output pulses. These pulses, however, had a fixed center wavelength and were extremely broadband (approx. 6nm FWHM). Experimental investigations were performed to determine if the spectral properties of these outputs could be improved through the use of injection-locking techniques. A parametric study to determine the specific conditions under which the laser could be injection-locked was also carried out. Significant linewidth reduction to 0.0015nm) of the outputs was obtained through injection-locking but only at wavelengths near the peak lasing wavelength of the dye. It was found, however; that by inserting weakly dispersive tuning elements in the laser cavity, these narrow-linewidth outputs could be obtained over a wide (24nm) tuning range. Since the tuning elements had low insertion losses, the tunability of the output was obtained without sacrificing output pulse energy

Power-Aware Planning and Design for Next Generation Wireless Networks

Mobile network operators have witnessed a transition from being voice dominated to video/data domination, which leads to a dramatic traffic growth over the past decade. With the 4G wireless communication systems being deployed in the world most recently, the fifth generation (5G) mobile and wireless communica- tion technologies are emerging into research fields. The fast growing data traffic volume and dramatic expansion of network infrastructures will inevitably trigger tremendous escalation of energy consumption in wireless networks, which will re- sult in the increase of greenhouse gas emission and pose ever increasing urgency on the environmental protection and sustainable network development. Thus, energy-efficiency is one of the most important rules that 5G network planning and design should follow. This dissertation presents power-aware planning and design for next generation wireless networks. We study network planning and design problems in both offline planning and online resource allocation. We propose approximation algo- rithms and effective heuristics for various network design scenarios, with different wireless network setups and different power saving optimization objectives. We aim to save power consumption on both base stations (BSs) and user equipments (UEs) by leveraging wireless relay placement, small cell deployment, device-to- device communications and base station consolidation. We first study a joint signal-aware relay station placement and power alloca- tion problem with consideration for multiple related physical constraints such as channel capacity, signal to noise ratio requirement of subscribers, relay power and network topology in multihop wireless relay networks. We present approximation schemes which first find a minimum number of relay stations, using maximum transmit power, to cover all the subscribers meeting each SNR requirement, and then ensure communications between any subscriber and a base station by ad- justing the transmit power of each relay station. In order to save power on BS, we propose a practical solution and offer a new perspective on implementing green wireless networks by embracing small cell networks. Many existing works have proposed to schedule base station into sleep to save energy. However, in reality, it is very difficult to shut down and reboot BSs frequently due to nu- merous technical issues and performance requirements. Instead of putting BSs into sleep, we tactically reduce the coverage of each base station, and strategi- cally place microcells to offload the traffic transmitted to/from BSs to save total power consumption. In online resource allocation, we aim to save tranmit power of UEs by en- abling device-to-device (D2D) communications in OFDMA-based wireless net- works. Most existing works on D2D communications either targeted CDMA- based single-channel networks or aimed at maximizing network throughput. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize total power consumption while meeting user data rate requirements. We propose to solve it using a joint optimization approach by presenting two effective and efficient algorithms, which both jointly determine mode selection, channel allocation and power assignment. In the last part of this dissertation, we propose to leverage load migration and base station consolidation for green communications and consider a power- efficient network planning problem in virtualized cognitive radio networks with the objective of minimizing total power consumption while meeting traffic load demand of each Mobile Virtual Network Operator (MVNO). First we present a Mixed Integer Linear Programming (MILP) to provide optimal solutions. Then we present a general optimization framework to guide algorithm design, which solves two subproblems, channel assignment and load allocation, in sequence. In addition, we present an effective heuristic algorithm that jointly solves the two subproblems. Numerical results are presented to confirm the theoretical analysis of our schemes, and to show strong performances of our solutions, compared to several baseline methods

California coast nearshore processes study

There are no author-identified significant results in this report