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Situational awareness based on neural control of an autonomous helicopter during hovering manoeuvres
Abstract
This paper focuses on a critical component of the situational awareness, the neural network control of autonomous vertical flight for an unmanned aerial vehicle. Application of the proposed two stage flight strategy which uses two autonomous adaptive neural dynamical feedback controllers was carried out for a nontrivial small-scale helicopter model comprising five states, two inputs and two outputs. This control strategy for chosen helicopter model has been verified by simulation of hovering manoeuvres using software package Simulink and demonstrated good performance for fast situational awareness in real-time search-and-rescue operations.
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This paper presents a theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains. Situation awareness is presented as a predominant concern in system operation, based on a descriptive view of decision making. The relationship between situation awareness and numerous individual and environmental factors is explored. Among these factors, attention and working memory are presented as critical factors limiting operators from acquiring and interpreting information from the environment to form situation awareness, and mental models and goal-directed behavior are hypothesized as important mechanisms for overcoming these limits. The impact of design features, workload, stress, system complexity, and automation on operator situation awareness is addressed, and a taxonomy of errors in situation awareness is introduced, based on the model presented. The model is used to generate design implications for enhancing operator situation awareness and future directions for situation awareness research.
Decentralized command and control settings like those found in the military are rife with complexity and change. These settings typically involve dozens, if not hundreds to thousands, of heterogeneous players coordinating in a distributed fashion in a dynamically networked battlefield laden with sensor data, intelligence reports, communications, and plans emanating from many different perspectives. Consider the concept of team situation awareness in this setting. What does it mean for a team to be aware of a situation or, more importantly, of a critical change in a situation? Is it sufficient or necessary for all individuals on the team to be independently aware? Or is there some more holistic awareness that emerges as team members interact? We re-examine the concept of team situation awareness in decentralized systems beyond an individual-oriented knowledge-based construct by considering it as a team interaction-based phenomenon. A theoretical framework for a process-based measure called 'coordinated awareness of situations by teams' is outlined.
Illustrates a robust control scheme for application to helicopters
in vertical flight mode (both take-off and landing) to guarantee
altitude stabilisation. A nonlinear helicopter model is used to derive
the proposed control in which Lyapunov's direct method is used to
establish the overall system stability. A recursive design technique is
applied to design a nonlinear robust controller using the highly coupled
system structure. It is shown that the proposed robust controller
provides semiglobal stabilisation of uniform ultimate boundedness for
achieving the desired altitude. That is, the control design is valid for
all values of the helicopter's collective pitch angle away from zero.
The vertical flight application demonstrates a unique robust control
mechanisation for helicopter and V/STOL autopilot augmentation systems
A general introduction to adaptive systems is presented with emphasis
given to: open and closed loop adaptation; the adaptive linear combiner;
and alternative expressions of the gradient. Some theoretical
considerations in adaptive processing of stationary signals are
discussed including: the properties of the quadratic performance
surface; methods for searching the performance surface; and the effect
of gradient estimation on adaptation. Finally, the basic adaptive
algorithm and structures are described, with attention given to LMS
algorithms; the z-transform; the sequential regression algorithm; and
adaptive recursive filters. Some of the applications of adaptive signal
processing are also considered, including adaptive control systems;
adaptive interference canceling; and adaptive beam forming.