The invention of the steam engine in the late eighteenth century made it possible to replace the muscle-power of men and animals by the motive power of machines. The invention of the stored-program digital computer during the second world war made it possible to replace the lower-level mental processes of man, such as arithmetic computation and information storage, by electronic data-processing in machines. We are now coming to the stage where it is reasonable to contemplate replacing some of the higher mental processes of man, such as the ability to recognize patterns and to learn, with similar capabilities in machines. However, we lack the “steam engine” or “digital computer” which will provide the necessary technology for learning and pattern recognition by machines.

Stochastic Computing Systems

Parameter estimation for continuous-time models-A survey

The model reference adaptive control system has proved very popular on account of a ready-made, but heuristically based, rule for synthesizing the adaptive loops-the so-called "M.I.T. rule." A theoretical analysis of loops so designed is generally very difficult, but analyses of quite simple systems do show that instability is possible for certain system inputs. An alternative synthesis based on Liapunov's second method is suggested here, and is applied to the redesign of adaptive loops considered by some other authors who have all used the M.I.T, rule. Derivatives of model-system error are sometimes required, but may be avoided in gain adjustment schemes if the system transfer function is "positive real," using a lemma due to Kalman. This paper amplifies and extends the work of Butchart and Shackcloth reported at the IFAC (Teddington) Symposium, September, 1965.

Liapunov redesign of model reference adaptive control systems

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

The purpose of this paper is to develop a fairly general approach to adaptive control systems which, at the same time, can be mechanized in a reasonable fashion. The development is carried out initially for a simple system with a linear time variant physical process and then extended to the general linear time variant case. Examples are given. A combined learning model and model referenced time variant system is considered next. Finally, the extension of this adaptive concept to control systems which have nonlinear time variant physical processes is presented. Numerous practical implications of the theory are developed in such fields as aerospace vehicle guidance and control, process control, etc. For instance, in re-entry eters of the controlled vehicle vary by two orders of magnitude, while the vehicle dynamic response must remain relatively constant.

A Model Referenced Parameter Tracking Technique for Adaptive Control Systems I-The Principle of Adaptation

The purpose of this paper is to develop stability analysis results for the type of adaptive control systems described in a companion paper.1 By their very nature, such systems are in general nonautonomous since the inputs may be arbitrary functions of time. The Second Method of Lyapunov supplies the principal tools for this stability analysis, while fairly general techniques are presented for generating the required Lyapunov functions for these systems. Although the analysis is carried out in detail for certain specific types of inputs, it is applicable to other types as well.

Dale D. Donalson

Papers

A Model Referenced Parameter Tracking Technique for Adaptive Control Systems I-The Principle of Adaptation

A Model Referenced Parameter Tracking Technique for Adaptive Control Systems II-Stability Analysis by the Second Method of Lyapunov