We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indices for the overall amplifier, such as bandwidth, gain, rise-time, delay, output signal swing, linearity, and noise performance, in a complicated and nonlinear fashion, making optimization of the feedback gains a challenging problem. In this paper we show that this problem, though complicated and nonlinear, can be formulated as a special type of optimization problem called geometric programming. Geometric programs can be solved globally and efficiently using recently developed interior-point methods. Our method therefore gives a complete solution to the problem of optimally allocating local feedback gains, taking into account a wide variety of constraints. 1 1

Abstract not found

We review the rapidly evolving efforts to analyze, model, simulate, and engineer genetic and biochemical information processing systems within living cells. We begin by showing that the fundamental elements of information processing in electronic and genetic systems are strikingly similar, and follow this theme through a review of efforts to create synthetic genetic circuits. In particular, we describe and review the “silicon mimetic ” approach, where genetic circuits are engineered to mimic the functionality of semiconductor devices such as logic gates, latched circuits, and oscillators. This is followed with a review of the analysis, modeling, and simulation of natural and synthetic genetic circuits, which often proceed in a manner similar to that used for electronic systems. We conclude by presenting examples of naturally occurring genetic and biochemical systems that recently have been conceptualized in terms familiar to systems engineers. Our review of these newly forming

Abstract. The purpose of this paper is to present a brief sketch of the evolution of modern control theory. Systems theory witnessed different stages and approaches, which will be very shortly presented. The main idea is that, at present, Control Theory is an interdisciplinary area of research where many mathematical concepts and methods work together to produce an impressive body of important applied mathematics. A general conclusion is that the main advances in Control of Systems would come both from mathematical progress and from technological development. We start with frequencydomain approach and end our historical perspective with structural-digraph approach, passing through time-domain, polynomial-matrix-domain frequential and geometric approaches. 1.

We consider the problem of optimally allocating local feedback to the stages of a multistage amplifier. The local feedback gains affect many performance indexes for the overall amplifier, such as bandwidth, gain, rise time, delay, output signal swing, linearity, and noise performance, in a complicated and nonlinear fashion, making optimization of the feedback gains a challenging problem. In this paper, we show that this problem, though complicated and nonlinear, can be formulated as a special type of optimization problem called geometric programming. Geometric programs can be solved globally and efficiently using recently developed interior-point methods. Our method, therefore, gives a complete solution to the problem of optimally allocating local feedback gains, taking into account a wide variety of constraints. Index Terms---Amplifiers, analog circuits, circuit optimization, design automation, geometric programming, sensitivity. I.

Today, the role of a control algorithm is evolving from static designs, synthesised off-line to dynamic algorithms that adapt in real-time to changes in the controlled system and its environment. Hardware realisation of such control algorithms will result in complex, dynamic real-time systems. These real-time systems are expected to adapt to the time varying nature of the environment they react with. Therefore, it expected that there will be transient overloads on the processor implementing the control algorithm. In the presence of transient overloads, it may not be possible for the processor to allocate adequate computational time to each sub-task so their deadlines can be met.

While the concept of switching between multiple controllers to achieve a control objective is not new, the available analysis to date imposes various structural and analytical assumptions on the controlled plant. The analysis presented in this thesis, which is concerned with an Estimation-based Multiple Model Switched Adaptive Control (EMM-SAC) algorithm originating from Fisher-Jeffes (2003); Vinnicombe (2004), is shown not to have such limitations. As the name suggests, the key difference between EMMSAC and common multiple model type switching schemes is that the switching decision is based on the outcome of an optimal estimation process. The use of such optimal estimators is the key that allows for a simplified, axiomatic approach to analysis. Also, since estimators may be implemented by standard optimisation techniques, their construction is feasible for a broad class of systems. The presented analysis is the first of its kind to provide comprehensive robustness and performance guarantees for a multiple model control algorithm, in terms of lp, 1 ≤ p ≤ ∞ bounds on the closed loop gain, and is applicable to the class of minimal MIMO LTI plants. A key feature of this bound is that it permits the on-line alteration of the

Abstract not found