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this paper, we first give a very brief overview of control engineering. The goal of control engineering is to improve, or in some cases ena- ble, the performance of a system by the addition of sensors, which measure various signals in the system and external command signals, control processors, which process the measu red signals to drive actuators, which affect the behav- ior of the system. A schematic diagram of a general control system is shown in Fig. 1. The use of the sensed response of the system (and not just the command signals) in the computation of the actuator signals is called feedback control, an old idea which has been developed and applied with great success in this century [1], [2]. Control engineering involves 1. Modeling or identification. The designer develops mathematical models of the relevant aspects of system to be controlled. This can be done using knowl- edge of the system (for example by applying Newton 's equations of motion to a mechanical system), and experimentally by observing responses of the Manuscript received November 30,1988; revised August 4,1989. This work was supported in part by the National Science Foundation (NSF) under ECS-85-52465, the Air Force Office of Scientific Research (AFOSR) under 89-0228, Boeing Electronics Company under LF0937, and Bell Communications Research, and the National Science and Engineering Research Council (Canada) 1967 Science and Engineering Scholarship. The authors arewith the Dept. of Electrical Engineering, Stanford University, Stanford, CA 94305, USA. IEEE Log Number 8933936. system to various excitations, a procedure known as s)zstem identification [3]. In some cases, several models are developed, varying in complexity and accu racy. 2. Control configuration: selection and placement of sensors an...

Natural time-varying images possess significant temporal correlations when sampled frame by frame by the photoreceptors. These correlations persist even after retinal processing and hence, under natural activation conditions, the signal sent to the lateral geniculate nucleus is temporally redundant or inefficient. We explore the hypothesis that the LGN is concerned, among other things, with improving efficiency of visual representation through active temporal decorrelation of the retinal signal much in the same way that the retina improves efficiency by spatially decorrelating incoming images. Using some recently measured statistical properties of time-varying images, we predict the spatio-temporal receptive fields that achieve this decorrelation. It is shown that, because of neuronal nonlinearities, temporal decorrelation requires two response types, the lagged and nonlagged, just as spatial decorrelation requires on and off response types. The tuning and response properties of the p...

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Multiband frequency domain synthesis consists in the minimization of a finite family of closed-loop transfer functions on prescribed frequency intervals. This is an algorithmically difficult problem due to its inherent nonsmoothness and nonconvexity. We extend our previous work on nonsmooth H ∞ synthesis to develop a nonsmooth optimization technique to compute local solutions to multiband synthesis problems. The proposed method is shown to perform well on illustrative examples.

Abstract—A novel multivariable laboratory process that consists of four interconnected water tanks is presented. The linearized dynamics of the system have a multivariable zero that is possible to move along the real axis by changing a valve. The zero can be placed in both the left and the right half-plane. In this way the quadruple-tank process is ideal for illustrating many concepts in multivariable control, particularly performance limitations due to multivariable right half-plane zeros. The location and the direction of the zero have an appealing physical interpretation. Accurate models are derived from both physical and experimental data and decentralized control is demonstrated on the process. Index Terms—Education, laboratory process, multivariable control, multivariable zeros. I.

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

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This paper uses the PES Pareto Method[1] and measurement techniques for isolating noise sources[2] to decompose the Position Error Signal (PES) of a Lynx II hard disk drive manufactured by Hewlett-Packard. This accomplishes three things: it demonstrates the utility of the PES Pareto Method in a practical example, it allows us to discover which noise sources are insignificant to PES, and it identifies which noise sources are significant to PES. In this particular hard disk drive, it is discovered that the two most significant sources of baseline noise at the disk's position error signal are the turbulent wind flow generated by the spinning disks (Windage) and the noise involved in the actual readback of the Position Error Signal (Position Sensing Noise). 1. Introduction Disk ? RT NRRO - f- ? PSN Dem. | {z } D(s) - PES AD ? ADC Noise | {z } C(s) - Comp. - DA ? DAC Noise - f 6 ? - PA |{z} A(s) ? PA Noise 6 K is I sense - K t - f ? Windage - 1 J - 1 s - - Res. T...

This paper and the two that accompany it will describe a method of breaking down the Position Error Signal (PES) of a magnetic disk drive to its contributing components. Once these components are identified, they can be ranked in terms of their overall e#ect on PES and thus the most critical ones can be worked on first. This method is based on three things: an understanding of how Bode's Integral Theorem[1] ties into noise measurements, a measurement methodology that allows for the isolation of individual noise sources, and a system model that allows these sources to be recombined to form the drive's Position Error Signal. We have found this method to be dramatically useful in identifying the key contributors to PES noise. 1. Introduction This paper and the two that accompany it[2, 3] will describe a method of breaking down the Position Error Signal (PES) of a magnetic disk drive to its contributing components. Once these components are identified, they can be ranked in terms of their ...