FLIGHT CONTROL SYSTEMSLecture 03 - Avionics

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Avionics System - Generaly

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Flight Control Systems Flight Control Systems

` Consists of the flight control surfaces, the respective cockpit c ntr ls c nnectin linka e and controls, connecting linkage, and necessary operating mechanisms to y p gcontrol aircraft in flight.

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Flight Control Systems Flight Control Systems

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A/C Axes A/C Axes

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Flight Control Systems Flight Control Systems ` Control yoke for roll which moves the ` Control yoke for roll which moves the

ailerons ` Control column for pitch which moves the

elevators ` Rudder pedals for yaw which moves the

ddrudder

Many aircraft use a control stick for both roll and pitch and the rudder pedals for yawand pitch, and the rudder pedals for yaw

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OBJECTIVES OBJECTIVES

` Reduce pilot workload at useful times (like on landing, or in the h er)hover)

` Remove pilot errorp

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CATEGORIES CATEGORIES ` Mechanical` Mechanical` The most basic designs

U d i l i f d l i ` Used in early aircraft and currently in small A/Cs where the aerodynamic f t iforces are not excessive

` Uses a collection of mechanical parts h d bl ll d such as rods, cables, pulleys and

sometimes chains to transmit the forces f th k it t l t th t l of the cockpit controls to the control

surfaces

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CATEGORIES CATEGORIES ` Hydromechanical` Hydromechanical` Overcomes the complexity and weight of a

mechanical flight control systemsmechanical flight control systems` has 2 parts

The mechanical circuit The hydraulic circuit M h l d f d bl ` Mechanical circuit is made of rods, cables, pulleys, and sometimes chains

` Hydraulic circuit has hydraulic pumps, pipes, valves and actuators

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CATEGORIES CATEGORIES ` Fly-by-wirey y` Replaces physical control of the aircraft with an

electrical interface` Can respond more flexibly to changing

aerodynamic conditions, by tailoring flight control surface movements so that airplane response to surface movements so that airplane response to control inputs is consistent for all flight conditions

` Less maintenance` Enhances safety` Reliable - incorporated redundant computers

and some kind of mechanical or hydraulic backup

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CATEGORIES CATEGORIES

Fl b` Fly-by-optics` Uses light as the transmission means` Uses light as the transmission means` Can transfer data at higher speedsg p` Immune to electromagnetic

interference` Has the same data interpretation ` Has the same data interpretation

mechanism as the FbW11 copyright arwin@2008

CATEGORIES CATEGORIES ` Power-by-wire` Power by wire` Eliminate the bulky and heavy

hydraulic circuitshydraulic circuits` Replaced by an electrical power

i icircuit` Controlled by the digital flight y g g

control computers` Weight savings` Weight savings` Greatly reduces maintenance costs

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CATEGORIES CATEGORIES ` Intelligentg` Intelligent Flight Control System` An extension of modern digital fly-by-wire flight g y y g

control systems` Intelligently compensate for aircraft damage and

f il d i fli ht h t ti ll i failure during flight, such as automatically using engine thrust and other avionics to compensate for severe failures such as loss of hydraulics, loss for severe failures such as loss of hydraulics, loss of rudder, loss of ailerons, loss of an engine, etc

` Enhancement is mostly a software upgrade to an existing fully computerized digital fly-by-wire flight control systems

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Intelligent Flight Control SystemIntelligent Flight Control System A computer program implements a dynamic-cell-

structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neuralDCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated errornodes is based on accumulated error.

A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used.

A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate newdetermines which nodes are used to estimate new values.

The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning the error becomes homogeneously distributedlearning, the error becomes homogeneously distributed over all nodes

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