1 This chapter shows application of a consistent set of clear physical principles to describe the beginnings of the universe by the (hot) Big Bang model. The ultimate beginning presents difficulties for the physicist, particularly when it comes to explaining why we find a very inhomogeneous cosmos, instead of a uniform, radiating gas. Stars, globular clusters, galaxies, and clusters of galaxies dot space, but we cannot at present explain their presence without invoking a "deus ex machina." We expect ultimately to rationalize the existence of these local inhomogeneities through gravitational contractions of regions of higher than average mass density that in tum arise out of fluctuations, but the case cannot yet be made completely. Meanwhile, the Big Bang (incomplete) model deals with cosmic evolution in the large, as a balance between gravitational attraction and cosmic expansion. Its relationships are described by two simple equations derived from Einstein's theory of general relativity via local conservations of mass-energy and of momentum. Unfortunately, this model does not by itself produce the observed inhomogeneities. Neither the assumption of thermodynamic fluctuations following a "smooth" beginning nor that ofa chaotic beginning can itself account for stars and galaxies. A mystery remains. -THE EDITOR This chapter describes the efforts of cosmologists (and nature?) to form galactic and stellar structures, comments on them from the viewpoint of the homeokinetic principles (Soodak and Iberall, 1978) discussed elsewhere in this volume, and pursues some parallels in the origin of life. It appears that there are no serious problems in understanding the origin of stars, given the previous formation of a galactic gas mass. A number of reasonable, even likely, paths lead to star formation. On the other hand, the origin of inhomogeneities required to initiate galaxy formation is not yet known. Appendix 1 remarks on some advances in observational and theoretical cosmology made since the original writing of this chapter HARRY SOODAK • Department of Physics, The City College of New York, New York, New York

/pdf/self-organizing-systems-iqagjbi8lb.pdf

Self-Organizing Systems

Robustness of adaptive controllers—a survey

Adaptive control is now finding its way into the marketplace after many years of effort. This paper reviews some ideas used to design adaptive control systems. It covers early ideas which primarily attempt to compensate for gain variations and more general methods like gain scheduling, model reference adaptive control, and self-tuning regulators. It is shown that adaptive control laws can be obtained using stochastic control theory. Techniques for analyzing adaptive systems are discussed. This covers stability and convergence analysis. Issues of importance for applications like parameterization, tuning, and tracking, as well as different ways of using adaptive control are also discussed. An overview of applications which includes feasibility studies as well as products based on adaptive techniques concludes the paper.

/pdf/adaptive-feedback-control-4wzqer5bdp.pdf

Adaptive feedback control

/pdf/adaptive-feedback-control-384lkskmt0.pdf

Adaptive Feedback Control

Various parameter adaptive control strategies are applied to the problem of regulating the cutting force in peripheral milling under varying cutting conditions. The dynamics of the relationship between feed rate and cutting force are represented by a first order linear model which includes the effect of spindle stiffness on the mechanics of chip formation. The relationship is further expressed in the form of the Hammerstein model to include the nonlinearities in the force behaviour, particularly at low feed rates. The performance of the corresponding nonlinear controller is analyzed. The main contribution of this work is the application of theoretically based strategies and structures for parameter adaptive control in peripheral milling which can be readily implemented using available microprocessor technology. These strategies are shown to be stable with good performance characteristics over the full range of operating conditions.

Parameter adaptive control in peripheral milling

The control objectives for Model Reference Adaptive Control Systems (MRAC) are specified in terms of a reference model. The control objectives for Stochastic Self-Tuning Regulators (S-STURE) are specified in terms of an ARMA model. These two types of adaptive control systems share a common characteristic (duality). This duality extends the one existing in the linear case with known parameters between modal control (pole-placement) and minimum variance control. Based on the duality properties, a unified presentation of the two techniques is possible; the S-STURE being interpreted as a stochastic MRAC. The underlying concepts and the structure of these adaptive control systems are described. The available theoretical results concerning the analysis of these systems are briefly reviewed. The primary domain of application of these techniques are indicated. An overview of the unsolved problems concludes the paper.

I. D. Landau

Papers

Model Reference Adaptive Controllers and Stochastic Self-Tuning Regulators—A Unified Approach