On adaptive control systems,

At head of title: Microwave Research Institute, Polytechnic Institute of Brooklyn, Systems and Control Group, R-735, PIB-663, contract no. DA-30-069-ORD-1560.

Synthesis of flight control systems subject to vehicle parameter variations.

Mode of access: Internet.

The evaluation and application of chemically regenerative atmospheric control systems.

" Report ; no. TDR-269 (4560-50)-2)."

The air quality and economic implications of supplementary control systems in Illinois /

Bibliography: p. 90-92.

Traffic control systems in construction workzones /

"Final Report, November 1997-October 1999."

Energy monitoring and control systems (EMCS).

"January 1991."

Diversion and corridor control systems in Western Europe /

Preprint of IRF report, issued June 1977.

Sampled-data control systems /

Includes bibliography.

Control of aircraft and missile powerplants; an introduction to the analysis and design of engine control systems

Mode of access: Internet.

Convergence analysis of a discrete-time single-unit gradient ICA algorithm

We revisit the one-unit gradient ICA algorithm derived from the kurtosis function. By carefully studying properties of the stationary points of the discrete-time one-unit gradient ICA algorithm, with suitable condition on the learning rate, convergence can be proved. The condition on the learning rate helps alleviate the guesswork that accompanies the problem of choosing suitable learning rate in practical computation. These results may be useful to extract independent source signals on-line.

Relational time for systems of oscillators

Using an elementary example based on two simple harmonic oscillators, we show how a relational time may be defined that leads to an approximate Schrodinger dynamics for subsystems, with corrections leading to an intrinsic decoherence in the energy eigenstates of the subsystem.

A novel approach to modelling and exploiting uncertainty in stochastic control systems

We consider an inversion-based neurocontroller for solving control problems of uncertain nonlinear systems. Classical approaches do not use uncertainty information in the neural network models. In this paper we show how we can exploit knowledge of this uncertainty to our advantage by developing a novel robust inverse control method. Simulations on a nonlinear uncertain second order system illustrate the approach.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.