Autonomous Vehicle Dynamics and Control MSc

The aim of this module is to enable AVDC MSc students to think critically and prepare them with fundamental design, technology, integration, and operational knowledge to meet emerging UAS demands. Laboratory exercises allow students to apply knowledge on a real system and practices indoor and outdoor flight.

• UAS overview
• UAS components: mechanical & electrical
• UAS power & propulsion
• UAS regulations and operations 
• UAS communication systems
• UAS networking 
• Unmanned aircraft systems integration 
• UAS flight demonstration: indoor/outdoor flight 
 

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

1) Appraise the main practical applications of Unmanned Aircraft Systems (UAS) and define operational safety .
2) Distinguish various components of UASs and obtain basic knowledge of UAS aerodynamics.
3) Evaluate the main communication systems requirements and UAS networking design.
4) Analyse qualitatively the functions and capabilities of the main subsystems of UAS.
5) Categorise system integration requirements.

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. The student 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 are to provide the student 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 the students 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 (4 lectures)
• Linear Kalman Filter and Linear Kalman Smoother (5 lectures)
• Inertial navigation (3 lectures)
• Constrained filters (1 lecture)
• Sensor Integration architectures and Multiple sensor fusion (3 lectures)
• Non-linear filters (EKF, UKF and Particle Filters) (5 lectures)
• Case Study: Inertial navigation (3 lectures)
• Case Study: Multiple sensor fusion (3 lectures)

On successful completion of this module a student 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.

The aim of this module is to introduce the AVDC MSc students 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 (2 lectures)
• AI Algorithms: Unsupervised Learning (4 lectures)
• Unsupervised Learning – Lab session (4 lectures)
• AI algorithms: Supervised Learning – SVM and Neural Networks (5 lectures)
• Supervised Learning – Lab session (3 lectures)
• AI Algorithms: Supervised Learning – Deep Neural Networks (3 lectures)
• Deep Learning – Lab session (3 lectures)
• Automated Reasoning (2 lectures)
• Case Study: AI for AS (2 lectures)

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

1. Categorize AI methods for real-life scenarios of Autonomous Systems (AS) applications.
2. Assess Applicability of Artificial Intelligence (AI) algorithms for AS.
3. Set up the commonly used AI algorithms for application in the AS context.
4. 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 students and course delegates 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 a student should be able to:

1. Critically understand the fundamentals of the various guidance techniques and their properties.
2. Describe the algorithms that are required to produce an estimate of position and attitude;
3. Describe the characteristics, purposes, and design procedures of guidance and navigation systems;
4. Evaluate challenging problems in the guidance and navigation approaches for autonomous systems;
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 to provide students 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 the students 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 state-space 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 a student 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.

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;

• Dr Marta Ceccaroni

This module aims at providing the foundations of logic and reasoning modelling to understand how reasoning can be automated. Moreover it introduces the fundamental techniques and formal language to design automated reasoners.

• Introduction to logical representation and reasoning
• Logical Agents
• Propositional Logic
• First-order Logic
• Inference Algorithms
• Engineering domain knowledge representation
• Exercises and case studies

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

1.    Analyse syntax and semantics of different problem instances using the appropriate formal language to identify relevant knowledge

2.    Building essential but complete Knowledge Bases representing a given knowledge, using Propositional and First Order Logics as representation languages.

3.    Design strategies to enable automated solving of assigned reasoning problems, detailing the execution steps to perform the given tasks.