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The authors investigate the way in which open unit disk zeros of a scalar stable discrete-time transfer function influence overshoot in the time-domain step response. Feedback design implications are discussed and illustrated by a simple example. >

This paper builds on previous work which developed a robust control algorithm which used statistical confidence bounds. The key idea was to change the nominal design so as to reduce the overall variability from an a priori specified desired performance. However, due to the choice of cost function, the original design method did not necessarily guarantee stability. This paper addresses the latter problem in a mixed H/sub 2//H/sub /spl infin// framework.

There has been enormous world-wide interest in control engineering over the past 50 or more years. This is presumably a consequence of the importance of this technology to industry. Notwithstanding this interest, there is some evidence that support for control research is waning and that industry has become wary of the claimed benefits of control.

Glycophorin from human erythrocytes has been incorporated into liposomes of dimyristoylphosphatidylcholine (DMPC), dipalmitoylphosphatidylcholine (DPPC) and distearoylphosphatidylcholine (DSPC). The thermal properties of unsonicated liposomes with glycophorin/lipid molar ratios up to 4·10−3 have been studied by differential scanning calorimetry and the numbers of lipids withdrawn from participation in the gel-to-lamellar phase transition were found to be 42±22 (DMPC), 197±28 (DPPC) and 240±64 (DSPC). The initial rates of agglutination of sonicated liposomes with glycophorin/lipid molar ratios up to 4·10−3 by wheat germ agglutinin in the concentration range 0–7 μM have been measured over a range of temperature. Below the gel-to-lamellar phase transition (Tc) the rates of agglutination increase with acyl chain length in the sequence DMPC < DPPC < DSPC. Agglutination is found to be second order in liposome concentration and is completely reversed on saturation of the wheat germ agglutinin-binding sites by N-acetylglucosamine. Agglutination rates decrease with increasing temperature below Tc and are largely independent of temperature above Tc. The results are discussed in relation to the clustering of glycophorin in the phospholipid bilayers and its effect on binding and subsequent interliposomal bridge formation by wheat germ agglutinin.

This paper examines some aspects of the roles of ultimate objectives and prior knowledge in system identification. It is shown that often an ultimate objective can be achieved without unique parameter or structure determination. New results based on this approach in adaptive prediction will be presented. Recent related results in adaptive control will also be examined to illustrate the role of identifciation when minimum variance control is the ultimate objective.

This paper presents a robust adaptive control algorithm for linear timeinvariant systems based on an output error estimation method. The parameterization adopted renders the output error linear in the parameters and no positive real condition is needed . The convergence of t he algorithm is investigated and the input-output stability of the controlled system in the presence of unmodelled dynamics and of disturbances, is established. Simulation results are also presented comparing the performance of the proposed algorithm with that of an adaptive controller with the same control law but based on an equation error estimation method

The choice of input signal in an identification experiment has a significant bearing on the final result. At one extreme, an input can result in violent system disturbances and the subsequent loss of product or damage to the system. At the other extreme, an input can be so conservative as to yield little or no information about the system's dynamic behaviour. The optimal input signal achieves a compromise between these extremes. This paper describes a design procedure for synthetising optimal input signals. A state-space description of the system is used, and the optimal input is chosen to minimise the variance of the parameter and state estimates. The method applies to both linear and nonlinear systems and the case of both white and coloured output noise has been considered. The optimal input signals are shown to result in a significant improvement in the identification accuracy.

The quasilinearisation method (or, equivalently, the parameter-influence-coefficient method) is currently preferred to the adjoint-variable method in solving the identification problem because it leads to a one-point boundary-value problem. It is shown that hybrid use of the two methods greatly reduces the number of first-order differential equations to be numerically integrated.

Crosscorrelation using pseudorandom binary noise as the test signal is a well established technique for the identification of linear systems, but there are several practical difficulties. We show that two of these difficulties, namely the necessity to have fast clock rates and a long p.r.b.n. period can be eliminated by using interclock sampling and by analytically removing folding errors. Further, if a maximally orthogonal test sequence is used, bias or ripple difficulties are overcome, and the analysis is particularly simple and practically tractable.