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This module aims to introduce the fundamentals of dynamics and control for Unmanned Aircraft Systems (UAS). Dynamics-wise both fixed-wing and rotary UAS are covered, including effects of aero (servo) elasticity and introduction to tilt rotors and copters. From a control viewpoint focusing on linear control theory students understand its purpose, strengths and limitations, and relevant characteristics in the context of UAS control. Complemented with a case study on UAS dynamics and control, it provides the underpinning knowledge for the “UAS Modelling and Simulation” and “UAS Autonomous Vehicle Control Systems” modules.

• Overview of dynamics of motion
• Mechanics of flight (performance requirements, forces/moments, dynamics)
• Overview of aero(servo)elasticity effects
• Introduction to tilt-rotor and copters
• Mathematical modelling of typical fixed-wing and rotary UAS
• UAS feedback control system characteristics
• UAS control system stability and performance
• Frequency response methods for UAS Flight Control Design
• Classical and state space control design for UAS

On successful completion of this module a student should be able to:

1. Distinguish the fundamentals of flight dynamics for Unmanned Aircraft Systems (UAS) and their control techniques and properties.
2. Evaluate the physical underpinning of mechanics of flight and convey mathematically dynamics of typical fixed-wing and rotary UAS.
3. Evaluate the characteristics, purposes, and design procedures of UAS control systems;

• Professor Saba Al-Rubaye

This module aims to provide you with new skills and understanding of Aeronautical communications design and overview of current approaches to line of sight and beyond line-of-sight techniques.

Overview of airborne communications:

• Radio communication systems,
• Communications standard,
• Communications technology (4G/5G).

 

Aeronautical Communication:

• Airspace integration UAS/UAM/ ATM/ATC Technologies,
• Digital Aeronautical Communications System,
• BLOS and Satellite Data Link Connectivity.

 

Communications system design:

• Analogue and Digital Modulation Schemes,
• Multiplexing and Transmission Techniques,
• Techniques (e.g., Uplink/Downlink Model, Noise, SNR),
• Link budget analysis.

 

Antennas design:

• Propagation,
• Gain and Polarization.

 

Communications Networking:

• Topology techniques,
• Mobility and ad hoc networking.

 

Case study:

• An application for UAM/Drone air to ground connectivity.

 

On successful completion of this module you should be able to:

• Distinguish the fundamental principles of airborne communication systems,
• Categorise different practices and procedures that is essential for air to ground communications,
• Estimate link budget analysis & sustainable mobility system design,
• Assess different antenna design and propagation aspect for Line-of-sight/Beyond visual LOS (LOS/BLOS),
• Evaluate security and networks techniques.

Mathematical modelling and simulation of unmanned aerial vehicles is a vital part of system development. Nowadays COTS components becoming more powerful and can multi-task and carry-out complex computations.You would need to learn the technical skills not only for the modelling and simulation but also the real-time implementation of the algorithms. 

The aims of this course is to provide you with the skills and knowledge necessary to model, simulate and then critically analyse the resultant non-linear motion of unmanned air vehicles using mainly Matlab/Simulink and target compile the algorithms on an embedded flight control system.

 

• Introduction to mathematical modelling and simulation; systems of nonlinear ODEs; equilibrium, linearisation and stability; numerical & computational tools (10 hours).
• Model building; model testing, validation and management; trimming and numerical linearisation (10 hours).
• Control implementation and testing on COTS FCS

On successful completion of this module a student should be able to:

1. Design and implement an example UAV model in terms of their aerodynamic, control, mass and inertia characteristics. Appraise and critically compare the resultant motion.
2. Distinguish the requirements for model testing, verification and validation, and demonstrate their application to an UAV model.
3. Implement and apply selected control laws and carry out simulation-in-the-loop testing. 
4. Communicate and present results of individual work.

The aim of this module is to provide an overview of sensor fusion architectures, algorithms and applications in the context of autonomous vehicles navigation, guidance and control both for linear and non-linear systems.

The module aims also to give you an understanding of the appropriate tools for error analysis, diagnostic statistics and heuristics enabling them to critically evaluate the performance of a sensor fusion architecture/algorithm. The main emphasis is on the Kalman Filter algorithm together with variants and generalisations, applied to target tracking problems.

• Statistical Analysis
• Linear Kalman Filter and Linear Kalman Smoother 
• Inertial navigation 
• Constrained filters
• Sensor Integration architectures and Multiple sensor fusion
• Non-linear filters (EKF, UKF and Particle Filters) 
• Case Study: Inertial navigation 
• Case Study: Multiple sensor fusion 

On successful completion of this module you should be able to:

1. Understand the fundamental principles in stochastic processes and in estimation theory.
2. Formulate, set up and execute the Kalman filter to linear processes and be able to assess the functional operation of the filter.
3. Formulate, set up and execute non-linear filters (Extended Kalman filter, Unscented Kalman Filter, Particle filters) to non-linear or non-Gaussian models.
4. List common motion models used in target tracking and navigation applications.
5. Design and appraise the performance of multi-sensor fusion architectures in a real-case scenario.

This module aims to provide you with fundamental understanding and knowledge of the advanced control systems and their applications to autonomous vehicles. Building upon the foundational “UAS Dynamics and Control” module, advanced control methods are presented and analysed to mitigate limitations of the conventional linear control approaches.

A key aim is for you to critically understand the properties of the system model, the fundamental working principals of the advanced control approaches together with their advantages and limitations.

Overview of autonomous vehicles performance requirement;
System identification and modelling;
Uncertainty representation;
Pole placement and gain scheduling;
Robust H-infinity control;
Adaptive and non-linear control system design and its application to autonomous vehicles;

On successful completion of this module you should be able to:

1. Appraise the nature, purpose, design procedure of advanced control systems for autonomous vehicles;
2. Evaluate the limitations of conventional control and their impact on autonomous vehicles;
3. Critically analyse adaptive control theories, their properties, and applications to autonomous vehicles;
4. Design a robust controller for autonomous aerial vehicles and critically evaluate the performance of the robust control system;
5. Analyse the stability, robustness and sensitivity of the control systems for autonomous vehicles.

• Dr Ivan Petrunin

The aim of this module is to introduce you to the Artificial Intelligence algorithms suitable for real life problems concerning the Autonomous Systems (AS): target detection, identification, recognition and tracking using multiple heterogeneous sensors from cooperating AS, including accuracy assessment and uncertainty reduction for these applications

• Introduction to AI for AS with overview of AS sensors and imaging,
• AI Algorithms: Unsupervised Learning,
• Unsupervised Learning – Lab session,
• AI algorithms: Supervised Learning – SVM and Neural Networks,
• Supervised Learning – Lab session,
• AI Algorithms: Supervised Learning – Deep Neural Networks,
• Deep Learning – Lab session,
• Automated Reasoning,
• Case Study: AI for AS.

On successful completion of this module you should be able to:

• Categorize AI methods for real-life scenarios of Autonomous Systems (AS) applications,
• Assess Applicability of Artificial Intelligence (AI) algorithms for AS,
• Set up the commonly used AI algorithms for application in the AS context,
• Evaluate performance of AI algorithms for a typical AS application in a simulation environment.

In modern autonomous systems, it is essential to design an appropriate guidance and navigation system. Therefore, this module aims to deliver not only fundamental and critical understanding of classical and advanced guidance and navigation theories, but also evaluation of their nature, purposes, pros and cons, and characteristics. This should enable you to critically select and design appropriate guidance and navigation for their specific autonomous systems.

• Introduction on navigation and guidance systems;
• Path planning for autonomous systems
• Path following for autonomous systems
• UAV (Unmanned Aerial Vehicle) guidance systems;
• Guidance approaches: conventional guidance such as PN (Proportional Navigation), geometric guidance, and optimal guidance;
• Navigation approaches: navigation systems, GNSS (Global Navigation Satellite System), terrain based navigation, SLAM (Simultaneous Localisation and Mapping);
• Cooperative guidance and collision avoidance.

On successful completion of this module you should be able to:

1. 1. Critically understand the fundamentals of the various guidance techniques and their properties.
2. 2. Describe the algorithms that are required to produce an estimate of position and attitude,
3. 3. Describe the characteristics, purposes, and design procedures of guidance and navigation systems.
4. 4. Evaluate challenging problems in the guidance and navigation approaches for autonomous systems.
5. 5. Describe the challenging issues of the cooperative guidance design and critically evaluate the cooperative guidance systems to be able to enhance the overall performance,

This module aims at providing you with the foundations of propositional logic as fundamental formal language to represent relevant knowledge. It introduces the main basic techniques and methodologies for logical reasoning to design automated reasoners and it provides the basics of Formal Modelling for engineering systems, a well-established industrial tool to check systems designs via their digital twins.

It also introduces Causal Logic linking it to the founding principles of Machine Learning.

Introduction to logic representation and reasoning

Logic Agents

Propositional Logic

Inference Algorithms

First Order Logic

Exercises and case studies

Formal Models

Formal Verifications

State machines

Exercises and case studies

Causal inference and machine learning

On successful completion of this module you should be able to:

1. 1. Define the relevant Knowledge Base (KB) to represent a given problem, and design it using an appropriate knowledge representation language.
2. 2. Apply inference to code autonomous reasoning algorithms.
3. 3. Analyse engineering systems and their processes to model them as state machines.
4. 4. Design relevant autonomous model checkers for modelled systems.
5. 5. Apply causal inference techniques to identify and work with the causal relationships between system parameters and how these techniques relate to machine learning.