Analog circuit design by nonconvex polynomial optimization: Two design examples

We present a framework for synthesizing low‐power analog circuits through global optimization over generally nonconvex multivariate polynomial objective function and constraints. Specifically, a nonconvex optimization problem is formed, which is then efficiently solved through convex programming techniques based on linear matrix inequality (LMI) relaxation. The framework allows both polynomial inequality and equality constraints, thereby facilitating more accurate device modelings and parameter tuning. Compared to traditional nonlinear programming (NLP), the proposed methodology exhibits superior computational efficiency, and guarantees convergence to a globally optimal solution. As in other physical design tasks, circuit knowledge and insight are critical for initial problem formulation, while the nonconvex optimization machinery provides a versatile tool and systematic way to locate the optimal parameters meeting design specifications. Two circuit design examples are given, namely, a nested transconductance(G_m)–capacitance compensation (NGCC) amplifier and a delta–sigma (ΔΣ) analog‐to‐digital converter (ADC), both of them being the key components in many electronic systems. Copyright © 2008 John Wiley & Sons, Ltd.     

Automated design of image recognition in capturing environment

This study aims at automating the design of the image‐recognition algorithm and the image‐acquisition environment for an industrial picking system. Here for the image‐recognition algorithm, a preprocessing image parameter and a discriminator using local features in images are targeted. For the image‐acquisition environment, the camera distance from the target objects and the illumination strength of each RGB color are considered. The problem is formulated as an optimization problem, and a method is proposed to derive solutions using a two‐phase random multistart local optimization for the image‐acquisition environment and the image‐recognition algorithm. In addition, experiment‐based optimization is made to deal with the uncertainty of the capturing environment. Furthermore, positions and angles are considered in a robot coordinate system to simplify the image‐acquisition process. The three evaluation experiments targeting objects with different surface characteristics are conducted. The results show that the proposed system successfully designed parameter sets for the image‐acquisition environment and the image‐recognition algorithm that suited the characteristics of the target objects. The object recognition rate, that is, F measure , is 1 for all objects in all the three experiments.     

MR CAT scan: a modular approach for hybrid imaging

In this study, a modular concept for NMR hybrid imaging is presented. This concept essentially integrates different imaging modules in a sequential fashion and is therefore called CAT (combined acquisition technique). CAT is not a single specific measurement sequence, but rather a sequence design concept whereby distinct acquisition techniques with varying imaging parameters are employed in rapid succession in order to coverk-space. The power of the CAT approach is that it provides a high flexibility toward the acquisition optimization with respect to the available imaging time and the desired image quality. Important CAT sequence optimization steps include the appropriate choice of thek-space coverage ratio and the application of mixed bandwidth technology. Details of both the CAT methodology and possible CAT acquisition strategies, such as FLASH/EPIRARE/EPI-and FLASH/BURST-CAT are provided. Examples from imaging experiments in phantoms and healthy volunteers including mixed bandwidth acquisitions are provided to demonstrate the feasibility of the proposed CAT concept.     

Analytical comparison of frequency compensation techniques in three‐stage amplifiers

In this paper, design equations of the most common Nested Miller topologies are derived. Moreover, a coherent and comprehensive analytical comparison among the different topologies is also presented. In particular, after deriving design equations, following the approach previously proposed by the authors that have the phase margin as the main design parameter, the different solutions are compared by evaluating a novel figure of merit that expresses a trade‐off between gain‐bandwidth product, load capacitance and total transconductance, for equal values of phase margin. It is shown that there is no unique optimal solution as this depends on the load condition and the relative magnitude of the transconductance of each stage. From this point of view, the proposed comparison also provides useful design guidelines for the optimization of small‐signal performance. Simulations confirming the effectiveness of the comparison are also given. Copyright © 2006 John Wiley & Sons, Ltd.     

Time delay approach for PSS and SSSC based coordinated controller design using hybrid PSO–GSA algorithm

In this work we present a novel approach in order to improve the power system stability, by designing a coordinated structure composed of a power system stabilizer and static synchronous series compensator (SSSC)-based damping controller. In the design approach various time delays and signal transmission delays owing to sensors are included. This is a coordinated design problem which is treated as an optimization problem. A new hybrid particle swarm optimization and gravitational search algorithm (hPSO–GSA) algorithm is used in order to find the controller parameters. The performance of single-machine infinite-bus power system as well as the multi-machine power systems are evaluated by applying the proposed hPSO–GSA based controllers (PSS and damping controller). Various results are shown here with different loading condition and system configuration over a wide range which will prove the robustness and effectiveness of the above design approach. From the results it can be observed that, the proposed hPSO–GSA based controller provides superior damping to the power system oscillation on a wide range of disturbances. Again from the simulation based results it can be concluded that, for a multi-machine power system, the modal oscillation which is very dangerous can be easily damped out with the above proposed approach.     

机器人关节空间B样条轨迹设计的混沌优化

为了研究机器人关节空间轨迹时间最短的优化计算问题,依据混沌优化理论,采用改进的基于混沌变量优化算法,以时间最短为性能指标对轨迹进行优化求解.研究了均匀非周期四阶B样条曲线的优化,每一段B样条曲线的运行时间作为优化参数,优化问题模型包括关节角速度、角加速度、角加加速度及力矩4种约束.给出了PUMA560前三铰B样条轨迹优化算例,优化结果明显优于采用复合形法或有约束随机搜索方法的优化结果;该算法简单,易于实现,求解速度快,任给一组初值得到优化结果的可靠性达90%以上,逼近约束条件的误差几乎为零,进一步减少了运行时间,从而有效地实现了机器人在关节空间的轨迹优化.     

An electronically tunable RC active filter

An RC active filter with embedded FET switches is presented for the realization of a general second order filter. The filter consists of minimal number of capacitors, two differential input dual output operational amplifiers, fixed resistors and resistors connected as potential dividers. The network has low Q and ω₀ sensitivities; further, the finite bandwidth of the operational amplifiers has negligible effect on the Q of the filter. The introduction of FET switches in the network results in frequency scaling and consequently, the possibility of electronically tuning it. In addition, by switching the FETs by pulses of very low duty cycle, it is possible to obtain subaudio filters using resistive and capacitive components of smaller values than required for a conventional filter. Experimental results pertaining to the band-pass case are also included.     

A globally convergent direct adaptive pole‐placement controller for nonminimum phase systems with relaxed excitation assumptions

In this paper, we propose the first solution to the long‐standing problem of designing a globally convergent direct adaptive pole‐placement controller for linear, time‐invariant discrete‐time systems with arbitrary zeros that does not rely on persistency of excitation assumptions. As is well known, the main difficulty of this design is that it involves the estimation of parameters that enter nonlinearly in the regression model. This problem can be overcome introducing an overparameterized representation of the system, which imposes very strict persistency of excitation conditions to prove the parameter convergence. The latter is avoided here using a new version of the dynamic regressor extension and mixing parameter estimator recently proposed in the literature. The main feature of this estimator is that it generates, out of an m‐dimensional vector regression, m scalar regression models. This property allows us to estimate only the controller parameters of interest for the adaptive implementation, whose convergence is ensured under assumptions that are strictly weaker than the classical persistency of excitation requirement. Simulation results that illustrate the performance of the proposed scheme are also presented.     

采用PSO算法对低压断路器的低能耗优化设计

在低压断路器的低能耗设计中,参数设置通常采用经验方式选取,因而很难实现能耗最低化。为此,在分析HSW6低压断路器能耗的数学物理模型的基础上,提出一种采用粒子群算法(PSO)进行低能耗参数优化设计的方法。首先对断路器的额定电流和内部功耗、铜耗、触头数、触头电阻等影响因素进行分析与建模,并转换成求解这一组参数的最优值问题;然后采用自适应PSO算法进行全局寻优得到参数最优解。实际应用表明,该文提出的基于PSO算法的低能耗参数优化设计方法不仅能实现断路器低能耗设计要求,而且提高了设计效率。     

Tackling SNR at low-field: a review of hardware approaches for point-of-care systems

Objective

To review the major hardware components of low-field point-of-care MRI systems which affect the overall sensitivity.

Methods

Designs for the following components are reviewed and analyzed: magnet, RF coils, transmit/receive switches, preamplifiers, data acquisition system, and methods for grounding and mitigating electromagnetic interference.

Results

High homogeneity magnets can be produced in a variety of different designs including C- and H-shaped as well as Halbach arrays. Using Litz wire for RF coil designs enables unloaded Q values of ~ 400 to be reached, with body loss representing about 35% of the total system resistance. There are a number of different schemes to tackle issues arising from the low coil bandwidth with respect to the imaging bandwidth. Finally, the effects of good RF shielding, proper electrical grounding, and effective electromagnetic interference reduction can lead to substantial increases in image signal-to-noise ratio.

Discussion

There are many different magnet and RF coil designs in the literature, and to enable meaningful comparisons and optimizations to be performed it would be very helpful to determine a standardized set of sensitivity measures, irrespective of design.

     

High‐bandwidth design of hard disk control system using H∞ control with feedback‐type uncertainty

In hard disk drives, it is important to enlarge the control bandwidth in order to shorten the track pitch for larger data capacity. However, it is difficult for the H_∞ control method to increase the control bandwidth if the mechanical resonance modes have uncertainty. This is because the robustness of the H_∞ control method is assured by the small‐gain theorem for additive or multiplicative perturbation and the control bandwidth is limited by the uncertainty. In this study, we propose an H_∞ control method for high‐bandwidth design by introducing a new uncertainty model with a feedforward and a feedback path in order to reduce the conservatism of robust design. The effectiveness is shown by numerical simulations. © 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 173(4): 54–62, 2010; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21025     

Power converter design optimization

This article present GA-based design approach to optimization of power electronics circuits is shown to be a very effective and powerful tool for obtaining improved solutions compared to traditional design procedures. In the GA procedure, each design is represented using a "gene string" for the converter design problem, each gene string is used to represent the set of electrical components that define one possible converter design. The design of power electronics systems involves a large number of design variables and the application of knowledge from several different engineering fields.     

Pole placement approach for robust optimum design of PSS and TCSC-based stabilizers using reinforcement learning automata

Power system stability enhancement via robust optimum design of power system stabilizers (PSSs) and thyristor controlled series capacitor (TCSC)-based stabilizers is thoroughly investigated in this paper. The design problem of PSS and TCSC-based stabilizers is formulated as an optimization problem where a reinforcement learning automata-based optimization algorithm is applied to search for the optimal setting of the proposed PSS and CSC parameters. A pole placement based objective function is considered to shift the dominant system eigenvalues to the left in the s-plane. For evaluation of the effectiveness and robustness of the proposed stabilizers, their performances have been examined on a weakly connected power system subjected to different disturbances, loading conditions, and system parameter variations. The nonlinear simulation results and eigenvalues analysis demonstrate the high performance of the proposed stabilizers and their ability to provide efficient damping of low frequency oscillations. In addition, it is observed that the proposed CSC has greatly improved the voltage profile of system under severe disturbances.     

Adaptive regulation of MIMO linear systems against unknown sinusoidal exogenous inputs

This paper deals with the adaptive regulation problem in linear multi‐input multi‐output systems subject to unknown sinusoidal exogenous inputs, where the frequencies, amplitudes, and phases of the sinusoids are unknown and where the number of sinusoids is assumed to be known. The design of an adaptive regulator for the system under consideration is performed within a set of Q‐parameterized stabilizing controllers. To facilitate the design of the adaptive regulator, triangular decoupling is introduced in part of the closed‐loop system dynamics. This is achieved through the proper selection of the controller state feedback gain and the structure of the Q parameter. Regulation conditions are then presented for the case where the sinusoidal exogenous input properties are known. For the case where the sinusoidal exogenous input properties are unknown, an adaptation algorithm is proposed to tune the Q parameter in the expression of the parameterized controller. The online tuning of the Q parameter allows the controller to converge to the desired regulator. Convergence results of the adaptation algorithm are presented. A simulation example involving a retinal imaging adaptive optics system is used to illustrate the performance of the proposed adaptive system. Copyright © 2008 John Wiley & Sons, Ltd.     

Dual high‐gain‐based adaptive output‐feedback control for a class of nonlinear systems

We propose an adaptive output‐feedback controller for a general class of nonlinear triangular (strict‐feedback‐like) systems. The design is based on our recent results on a new high‐gain control design approach utilizing a dual high‐gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time‐varying design of the high‐gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time‐invariant dynamic output‐feedback globally asymptotically stabilizing solution for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary. Copyright © 2007 John Wiley & Sons, Ltd.     

Continuum modeling of large networks

This paper is concerned with the modeling and simulation of extremely large networks. We derive a time‐dependent diffusion–convection partial differential equation, the solution of which captures the global characteristics of a stochastic network model. Continuum modeling provides a powerful way to deal with the number of components in large networks and opens up the use of highly sophisticated mathematical tools such as adaptive finite element methods. This, in turn, makes it possible to carry out—with reasonable computational burden even for very large systems—network performance evaluation and prototyping, network design, systematic parameter studies, and optimization of network characteristics. Copyright © 2007 John Wiley & Sons, Ltd.     

Optimal analog active filter design using craziness‐based particle swarm optimization algorithm

Because of the manufacturing constraints, the optimal selection of passive component values for the design of analog active filter is very critical. As the search on possible combinations in preferred values for capacitors and resistors is an exhaustive process, it has to be automated with high accuracy within short computation time. Evolutionary computation may be an attractive alternative for automatic selection of optimal discrete component values such as resistors and capacitors for analog active filter design. This paper presents an efficient evolutionary optimization approach for optimal analog filter design considering different topologies and manufacturing series by selecting their component values. The evolutionary optimization technique employed is craziness‐based particle swarm optimization (CRPSO). PSO is very simple in concept, easy to implement and computationally efficient algorithm with two main advantages: fast convergence and only a few control parameters. However, the performance of PSO depends on its control parameters and may be influenced by premature convergence and stagnation problem. To overcome these problems, the PSO algorithm has been modified to CRPSO and is used for the selection of optimal passive component values of fourth‐order Butterworth low‐pass analog active filter and second‐order state variable low‐pass filter, respectively. CRPSO performs the dual task of efficiently selecting the component values as well as minimizing the total design errors of low‐pass active filters. The component values of the filters are selected in such a way so that they become E12/E24/E96 series compatible. The simulation results prove that CRPSO efficiently minimizes the total design error with respect to previously used optimization techniques. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive control via parameter set estimation

An adaptive control system is examined where the traditional parameter estimator is replaced by a parameter set estimator which provides a measure of uncertainty of the estimated parameters. it is shown how to construct a parameter set estimator with the property that the true system is always in the model set, and furthermore, how to design the estimation experiment so that the set of uncertainty is as small as possible given some a priori information.     

Optimization of tubular rigid bus design [substations]

The authors summarize a rigid-bus design procedure and present a model for optimization of the rigid-bus design process. The computer program BUSOPT (developed by Ontario Hydro) can perform an optimal selection of conductor parameters, interphase spacing, and span length. The buses can be selected either from the set of standard sizes stored in the program's library or the program can compute the optimal bus dimensions. A calculator mode allows quick analysis of various aspects of the design process. The program has a user-friendly format with context-sensitive help and can be executed on IBM-compatible personal computers. The station design process is complex, and the resulting optimization problem is highly nonlinear. An efficient and robust optimization algorithm has been developed to perform the calculations. In the calculator mode, existing station designs can be checked and the parameters requiring improvements or modifications can be easily identified     

Reliable H∞ filtering for discrete time‐delay Markovian jump systems with partly unknown transition probabilities

In this paper, the reliable H_∞ filtering problem is studied for a class of discrete nonlinear Markovian jump systems with sensor failures and time delays. The transition probabilities of the jumping process are assumed to be partly unknown. The failures of sensors are quantified by a variable taking values in a given interval. The time‐varying delay is unknown with given lower and upper bounds. The purpose of the addressed reliable H_∞ filtering problem is to design a mode‐dependent filter such that the filtering error dynamics is asymptotically mean‐square stable and also achieves a prescribed H_∞ performance level. By using a new Lyapunov–Krasovskii functional and delay‐partitioning technique, sufficient delay‐dependent conditions for the existence of such a filter are obtained. The filter gains are characterized in terms of the solution to a convex optimization problem that can be easily solved by using the semi‐definite programme method. A numerical example is provided to demonstrate the effectiveness of the proposed design approach. Copyright © 2011 John Wiley & Sons, Ltd.