A controller display technique for a terminal air traffic control system.

"Contract no. FAA/BRD-15, Task order no. 14. Prepared for Federal Aviation Agency, Bureau of Research and Development, Systems Analysis Division."

Annual report of the State Controller, State of California, for the fiscal year ended June 30 ...

Title varies slightly.

Report of the controller of the state of Nevada

Imprint varies: Reports for 1878-1879 printed by A.L. Bancroft & Company, San Francisco

Annual report of the School Controller of the School District of Philadelphia.

Latest issue consulted: 9th (1920).

Aviation industry, a call to action : a joint FAA industry partnership to improve pilot/controller communications.

Shipping list no.: 89-297-P.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focussed recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the best choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy. (C) 2004 Elsevier B.V. All rights reserved.

Air traffic controller reactions to computer assistance

The research was instigated by the Civil Aviation Authority (CAA) to examine the implications for air traffic controllers' (ATCO) job satisfaction of the possible introduction of systems incorporating computer-assisted decision making. Additional research objectives were to assess the possible costs of reductions in ATCO job satisfaction, and to recommend appropriate task allocation between ATCOs and computer for future systems design (Chapter 1). Following a review of the literature (Chapter 2) it is argued that existing approaches to systems and job design do not allow for a sufficiently early consideration of employee needs and satisfactions in the design of complex systems. The present research develops a methodology for assessing affective reactions to an existing system as a basis for making reommendations for future systems design (Chapter 3). The method required analysis of job content using two techniques: (a) task analysis (Chapter 4.1) and (b) the Job Diagnostic Survey (JDS). ATCOs' affective reactions to the several operational positions on which they work were investigated at three levels of detail: (a) Reactions to positions, obtained by ranking techniques (Chapter 4.2); (b) Reactions to job characteristics, obtained by use of JDS (Chapter 4.3); and (c) Reactions to tasks, obtained by use of Repertory Grid technique (Chapter 4.4). The conclusion is drawn that ATCOs' motivation and satisfaction is greatly dependent on the presence of challenge, often through tasks requiring the use of decision making and other cognitive skills. Results suggest that the introduction of systems incorporating computer-assisted decision making might result in financial penalties for the CAA and significant reductions in job satisfaction for ATCOs. General recommendations are made for allocation of tasks in future systems design (Chapter 5).

A learning feed-forward current controller for linear reciprocating vapor compressors

Direct-drive linear reciprocating compressors offer numerous advantages over conventional counterparts which are usually driven by a rotary induction motor via a crank shaft. However, to ensure efficient and reliable operation under all conditions, it is essential that motor current of a linear compressor follows a sinusoidal current command with a frequency which matches the system resonant frequency. The design of a high-performance current controller for linear compressor drive presents a challenge since the system is highly nonlinear, and an effective solution must be low cost. In this paper, a learning feed-forward current controller for the linear compressors is proposed. It comprises a conventional feedback proportional-integral controller and a feed-forward B-spline neural network (BSNN). The feed-forward BSNN is trained online and in real time in order to minimize the current tracking error. Extensive simulation and experiment results with a prototype linear compressor show that the proposed current controller exhibits high steady state and transient performance. © 2009 IEEE.

The analogue computer simulation of integrated chemical plant and controller systems

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