10,916 research outputs found
CMOS design of chaotic oscillators using state variables: a monolithic Chua's circuit
This paper presents design considerations for monolithic implementation of piecewise-linear (PWL) dynamic systems in CMOS technology. Starting from a review of available CMOS circuit primitives and their respective merits and drawbacks, the paper proposes a synthesis approach for PWL dynamic systems, based on state-variable methods, and identifies the associated analog operators. The GmC approach, combining quasi-linear VCCS's, PWL VCCS's, and capacitors is then explored regarding the implementation of these operators. CMOS basic building blocks for the realization of the quasi-linear VCCS's and PWL VCCS's are presented and applied to design a Chua's circuit IC. The influence of GmC parasitics on the performance of dynamic PWL systems is illustrated through this example. Measured chaotic attractors from a Chua's circuit prototype are given. The prototype has been fabricated in a 2.4- mu m double-poly n-well CMOS technology, and occupies 0.35 mm/sup 2/, with a power consumption of 1.6 mW for a +or-2.5-V symmetric supply. Measurements show bifurcation toward a double-scroll Chua's attractor by changing a bias current
Investigation on energetic optimization problems of stochastic thermodynamics with iterative dynamic programming
The energetic optimization problem, e.g., searching for the optimal switch-
ing protocol of certain system parameters to minimize the input work, has been
extensively studied by stochastic thermodynamics. In current work, we study
this problem numerically with iterative dynamic programming. The model systems
under investigation are toy actuators consisting of spring-linked beads with
loading force imposed on both ending beads. For the simplest case, i.e., a
one-spring actuator driven by tuning the stiffness of the spring, we compare
the optimal control protocol of the stiffness for both the overdamped and the
underdamped situations, and discuss how inertial effects alter the
irreversibility of the driven process and thus modify the optimal protocol.
Then, we study the systems with multiple degrees of freedom by constructing
oligomer actuators, in which the harmonic interaction between the two ending
beads is tuned externally. With the same rated output work, actuators of
different constructions demand different minimal input work, reflecting the
influence of the internal degrees of freedom on the performance of the
actuators.Comment: 14 pages, 7 figures, communications in computational physics, in
pres
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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
Genetic learning particle swarm optimization
Social learning in particle swarm optimization (PSO) helps collective efficiency, whereas individual reproduction in genetic algorithm (GA) facilitates global effectiveness. This observation recently leads to hybridizing PSO with GA for performance enhancement. However, existing work uses a mechanistic parallel superposition and research has shown that construction of superior exemplars in PSO is more effective. Hence, this paper first develops a new framework so as to organically hybridize PSO with another optimization technique for “learning.” This leads to a generalized “learning PSO” paradigm, the *L-PSO. The paradigm is composed of two cascading layers, the first for exemplar generation and the second for particle updates as per a normal PSO algorithm. Using genetic evolution to breed promising exemplars for PSO, a specific novel *L-PSO algorithm is proposed in the paper, termed genetic learning PSO (GL-PSO). In particular, genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. By performing crossover, mutation, and selection on the historical information of particles, the constructed exemplars are not only well diversified, but also high qualified. Under such guidance, the global search ability and search efficiency of PSO are both enhanced. The proposed GL-PSO is tested on 42 benchmark functions widely adopted in the literature. Experimental results verify the effectiveness, efficiency, robustness, and scalability of the GL-PSO
Anticipating and Coordinating Voltage Control for Interconnected Power Systems
This paper deals with the application of an anticipating and coordinating feedback control scheme in order to mitigate the long-term voltage instability of multi-area power systems. Each local area is uniquely controlled by a control agent (CA) selecting control values based on model predictive control (MPC) and is possibly operated by an independent transmission system operator (TSO). Each MPC-based CA only knows a detailed local hybrid system model of its own area, employing reduced-order quasi steady-state (QSS) hybrid models of its neighboring areas and even simpler PV models for remote areas, to anticipate (and then optimize) the future behavior of its own area. Moreover, the neighboring CAs agree on communicating their planned future control input sequence in order to coordinate their own control actions. The feasibility of the proposed method for real-time applications is explained, and some practical implementation issues are also discussed. The performance of the method, using time-domain simulation of the Nordic32 test system, is compared with the uncoordinated decentralized MPC (no information exchange among CAs), demonstrating the improved behavior achieved by combining anticipation and coordination. The robustness of the control scheme against modeling uncertainties is also illustrated
Quantum dynamics of long-range interacting systems using the positive-P and gauge-P representations
We provide the necessary framework for carrying out stochastic positive-P and
gauge-P simulations of bosonic systems with long range interactions. In these
approaches, the quantum evolution is sampled by trajectories in phase space,
allowing calculation of correlations without truncation of the Hilbert space or
other approximations to the quantum state. The main drawback is that the
simulation time is limited by noise arising from interactions.
We show that the long-range character of these interactions does not further
increase the limitations of these methods, in contrast to the situation for
alternatives such as the density matrix renormalisation group. Furthermore,
stochastic gauge techniques can also successfully extend simulation times in
the long-range-interaction case, by making using of parameters that affect the
noise properties of trajectories, without affecting physical observables.
We derive essential results that significantly aid the use of these methods:
estimates of the available simulation time, optimized stochastic gauges, a
general form of the characteristic stochastic variance and adaptations for very
large systems. Testing the performance of particular drift and diffusion gauges
for nonlocal interactions, we find that, for small to medium systems, drift
gauges are beneficial, whereas for sufficiently large systems, it is optimal to
use only a diffusion gauge.
The methods are illustrated with direct numerical simulations of interaction
quenches in extended Bose-Hubbard lattice systems and the excitation of Rydberg
states in a Bose-Einstein condensate, also without the need for the typical
frozen gas approximation. We demonstrate that gauges can indeed lengthen the
useful simulation time.Comment: 19 pages, 11 appendix, 3 figure
Event-triggered gain scheduling of reaction-diffusion PDEs
This paper deals with the problem of boundary stabilization of 1D
reaction-diffusion PDEs with a time- and space- varying reaction coefficient.
The boundary control design relies on the backstepping approach. The gains of
the boundary control are scheduled under two suitable event-triggered
mechanisms. More precisely, gains are computed/updated on events according to
two state-dependent event-triggering conditions: static-based and dynamic-based
conditions, under which, the Zeno behavior is avoided and well-posedness as
well as exponential stability of the closed-loop system are guaranteed.
Numerical simulations are presented to illustrate the results.Comment: 20 pages, 5 figures, submitted to SICO
Solving Incremental Optimization Problems via Cooperative Coevolution
Engineering designs can involve multiple stages, where at each stage, the design models are incrementally modified and optimized. In contrast to traditional dynamic optimization problems where the changes are caused by some objective factors, the changes in such incremental optimization problems are usually caused by the modifications made by the decision makers during the design process. While existing work in the literature is mainly focused on traditional dynamic optimization, little research has been dedicated to solving such incremental optimization problems. In this work, we study how to adopt cooperative coevolution to efficiently solve a specific type of incremental optimization problems, namely, those with increasing decision variables. First, we present a benchmark function generator on the basis of some basic formulations of incremental optimization problems with increasing decision variables and exploitable modular structure. Then, we propose a contribution based cooperative coevolutionary framework coupled with an incremental grouping method for dealing with them. On one hand, the benchmark function generator is capable of generating various benchmark functions with various characteristics. On the other hand, the proposed framework is promising in solving such problems in terms of both optimization accuracy and computational efficiency. In addition, the proposed method is further assessed using a real-world application, i.e., the design optimization of a stepped cantilever beam
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