Abstract: Computer-implemented methods and systems for selecting components to be used in an aircraft system including: a reliability evaluation function, to produce a reliability score, and a complexity evaluation function, to produce a complexity score, are provided; and a multi-objective optimisation function is performed to determine at least one set of candidate components for the aircraft system that satisfy one or more conditions. The multi-objective optimisation function includes selecting a new set of candidate components, performing at least one of the reliability evaluation function and the complexity evaluation function, and evaluating at least one of the reliability score and the complexity score according to the conditions. The new set of candidate components are stored in associated with an indication of the outcome of the evaluation. An aircraft comprising a set of components that have been selected from one or more of the stored sets of components is also provided.

Abstract: A test apparatus and method for testing a trained classifier configured to control an aircraft system. Scenario data includes operating inputs representing an operational state of an aircraft and classifier outputs for controlling the aircraft system, is obtained. Model data representing a model of the at aircraft system is obtained. Error categorisation data for each aircraft operating scenario is generated based on respective classifier outputs applied to the model.

Abstract: Techniques for monitoring a state of a machine learning classifier to determine an operational mode of an aircraft system are provided. The techniques include applying to each input node of the machine learning classifier an operational mode status of a respective component an aircraft system and a node priority determined by the order of priority of the respective series component path of the aircraft system. Varying, for an operational mode status combination, an input node state of an input node having a relatively low node priority. Indicating a normal state of the machine learning classifier where output node states do not vary in response to the varying, and indicating an altered state of the machine learning classifier where a hidden layer node state and an output node state vary in response to the varying of the input node state.

Abstract: A method of testing a system model is disclosed. The system model includes a system parameter and three or more equipment models, each equipment model includes an equipment parameter which models a parameter of a respective equipment. The method includes (a) making a series of changes of the equipment parameter of one of the equipment models so that the equipment parameter of the one of the equipment models adopts a series of test values of the equipment parameter; (b) operating the system model to determine a series of test values of the system parameter, each test value of the system parameter is determined in accordance with a respective one of the test values of the equipment parameter; (c) determining a weight of the selected one of the equipment models, the weight of the one of the equipment models indicates a sensitivity of the system parameter to the series of changes in the equipment parameter of the one of the equipment models; and (d) outputting the weight of the one of the equipment models.

Abstract: A speed determination system for an aircraft including one or more interfaces arranged to receive first speed data from a first speed measurement system and second speed data from a second speed measurement system. The first speed measurement system provides the first speed data using global positioning system data. The second speed measurement system provides the second speed data based on a second speed measurement. The speed determination system includes a processor arranged to determine whether the data received from the first speed measurement system is reliable. If global positioning data is determined to be reliable, the speed determination system determines a speed from the first speed data and determines correction values for the second speed measurement system. If global positioning data is determined to be unreliable, the speed determination system determines a speed from the second speed data and the correction values.

Abstract: A method of landing an aircraft in which the aircraft receives information from a second aircraft via a direct aircraft-to-aircraft communication from the second aircraft to the aircraft; determines a landing plan of the aircraft based on the information; and lands the aircraft based on the landing plan. The use of a direct aircraft-to-aircraft communication (i.e. a communication from the second aircraft to the first aircraft which does not travel via an intermediary such as a land station, air traffic controller or satellite) makes the method reliable because such a communication is inherently secure and difficult to hack.

Abstract: An aircraft control system (100) including an input interface (102), an output interface (114) and a processing engine (108) having a classifier (110) that applies input data (104) generated by the input interface (102) to generate output control data (112). The classifier (110) has a plurality of parameters which represent a control policy for operating the aircraft (800). The output interface (114) generates control outputs to control the aircraft (800) based on the output control data (112). A machine learning system (900) for training the classifier (110) including an environment (902), a pathway evaluation engine (904), storage (906), and a training engine (908). The machine learning system (900) generates training data (912) by selecting a pathway representing an operating procedure using the pathway evaluation engine (904). The training engine (908) trains the classifier (110) using the training data (912).

Abstract: A monitoring system 300 for an aircraft 100 including a controller 301 to determine, based on one or more conditions, one or more expected operating characteristics of a steering system of the aircraft. The one or more conditions include one or more aircraft conditions indicative of an internal condition of the aircraft and/or one or more external conditions indicative of an external influence on the aircraft. The controller compares the one or more expected operating characteristics with the one or more actual operating characteristics. The controller is configured to determine, based on the compare, whether to issue a signal indicating a condition of the aircraft. An avionics system 3000 comprising the monitoring system, an aircraft comprising the monitoring system or the avionics system, and a method 600 of monitoring an aircraft.

Abstract: A heading control system for an aircraft arranged to maintain a heading of an aircraft by controlling a nose wheel angle of the aircraft. The heading control system includes an interface arranged to receive a bias signal indicating a bias towards the port or the starboard of the aircraft and one or more processors. The one or more processors are arranged to determine, based on the bias signal, an offset angle defining an offset from a longitudinal axis of the aircraft and to perform a control process to control the nose wheel angle within an angular range based on the offset angle.

Abstract: An aircraft landing event system is disclosed including a processor, the processor being configured to receive aircraft braking performance information from a plurality of aircraft that have performed a landing event on a particular runway. The processor is configured to determine an aircraft braking performance indicator on the basis of the aircraft braking performance information of the plurality of aircraft, and communicate the aircraft braking performance indicator to an aircraft landing system of an approaching aircraft that is about to perform a landing event on the particular runway.

Abstract: An aircraft landing event system including a processor communicatively coupled with memory storing aircraft landing event data. The processor is configured to receive environment information representative of an environmental condition of a runway approached by an aircraft and receive retardation information representative of a retardation demand of the aircraft during an anticipated landing event of the aircraft on the runway. The processor is further configured to select aircraft landing event data from the memory based on the environment information and the retardation information and determine a performance indicator for the landing event based on the aircraft landing event data selected by the processor. The processor is further configured to communicate the performance indicator to a landing system of the aircraft.

Abstract: An aircraft braking controller for an aircraft, the aircraft braking controller configured to determine a position of at least a part of a landing gear of the aircraft during retraction of the landing gear into the aircraft, and control braking of a wheel of the landing gear based on the position determined.

Abstract: An aircraft system having a first set of components for performing a function of the aircraft system, and a second, alternative, set of components for performing the function of the aircraft system. The aircraft system has and a controller configured to receive scenario data indicative of a scenario during which the function of the aircraft system is to be performed, and, where each of the first and second sets of components are operational, the controller is configured to select between the first or the second set of components to perform the aircraft system function during the scenario based on the received scenario data. The controller is configured to control the selected set of components to perform the function during the scenario.

Abstract: An aircraft controller 5, aircraft braking system 13 and method for determining braking parameters. The aircraft controller 5 is configured to determine information associated with an aircraft taxiing route associated with an aircraft 1. On the basis of the information, the aircraft controller 5 determines a first braking parameter for a first main landing gear 8 of the aircraft 1 and a second braking parameter for a second main landing gear 9 of the aircraft 1 during a landing event. The first and second braking parameters are determined to result in asymmetrical braking between the first 8 and second 9 main landing gears during the landing event.

Abstract: A method of training a neural network to control an aircraft system. The method includes obtaining an operational mode data space representing a set of operational modes for the aircraft system, wherein each operational mode represents a configuration of the aircraft system where performance of an aircraft component is impaired according to an aircraft system model. Each operational mode includes probability data indicating a respective probability of the operational mode occurring, according to the model. The method includes generating a reduced operational mode data space including operational modes having a probability greater than a theoretical operational probability threshold, and generating a training operational mode data space including operational modes within the reduced operational mode data space that have a probability less than a real-world operational probability threshold. The method includes training the neural network using operational modes within the training operational mode data space.

Abstract: A controller for an aircraft system of an aircraft, the aircraft system including a first actuator and a first energy supply. The controller is configured to, when the aircraft system is configured in a first configuration, in which the first actuator is coupled to the first energy supply, determine that there is a loss of energy supplied to the first actuator from the first energy supply. The controller is configured to determine a change in performance achievable by reconfiguring the aircraft system from the first configuration to an alternative configuration, in which the first actuator is uncoupled from the first energy supply, and cause reconfiguration of the aircraft system from the first configuration to the alternative configuration, in the event that the change in performance determined by the controller is a gain in performance.

Abstract: An aircraft control system (100) including an aircraft control module (110) and a test module (120). The aircraft control module includes a memory storing a machine learning model (130) adapted, based on received live aircraft operating inputs (102a-102f), to generate live aircraft control outputs (104a-104b). The test module (120) includes a memory storing input data representing test aircraft operating inputs (102a?-102f?) and respective output data representing expected aircraft control outputs (114a, 114b) that are expected to be generated by the aircraft control module in response to an input of the respective test aircraft operating inputs (102a?-102f?). The test module (120) is adapted to write test aircraft operating inputs (102a?-102f?) to the aircraft control module, read test aircraft control outputs (104a?-104b?) of the aircraft control module (110), and remove the authority of the aircraft control module (110) to perform aircraft control.

Abstract: A method of testing a system model is disclosed. The system model includes a system parameter and three or more equipment models, each equipment model includes an equipment parameter which models a parameter of a respective equipment. The method includes (a) making a series of changes of the equipment parameter of one of the equipment models so that the equipment parameter of the one of the equipment models adopts a series of test values of the equipment parameter; (b) operating the system model to determine a series of test values of the system parameter, each test value of the system parameter is determined in accordance with a respective one of the test values of the equipment parameter; (c) determining a weight of the selected one of the equipment models, the weight of the one of the equipment models indicates a sensitivity of the system parameter to the series of changes in the equipment parameter of the one of the equipment models; and (d) outputting the weight of the one of the equipment models.

Abstract: A test apparatus (100) for testing at least part of an aircraft system. The test apparatus (100) is configured to obtain first data (210) including a plurality of input values and process the input values using a classifier (230) configured to model an operation of the at least part of an aircraft system (220). Second data is obtained, including a set of output values (240b). The output values (240b) from the second data are processed with output values (240a) generated by the classifier to identify differences between operation of the at least part of an aircraft system (220) and the operation as modelled by the classifier (230).

Abstract: An aircraft braking system for an aircraft, the aircraft including first and second brakes, and first and second energy distribution systems for delivering energy to the first and second brakes to operate the respective first and second brakes. The aircraft braking system is switchable between: a first configuration, in which the first energy distribution system is coupled to the first brake and is isolated from the second brake, and the second energy distribution system is coupled to the second brake and is isolated from the first brake; and a second configuration, in which the first energy distribution system is coupled to both the first and second brakes.