The global market for aerial, ground, and marine autonomous vehicles has grown rapidly due to the advent of drones and driverless cars. Defence, aerospace, automotive, and marine industries seek graduates conversant in key aspects of autonomy including: dynamics and control, guidance and navigation, decision making, sensor fusion, data and information fusion, communication and networking.

Durable and transferrable skills are the foundation of this unique MSc course whose content has been based on advice from the Industrial Advisory Board, comprising industrial representatives from big primes to small and medium-sized enterprises.  


  • Start dateOctober
  • DurationMSc: Full-time one year; PgCert: Full-time up to one year; PgDip: Full-time up to one year
  • DeliveryTaught modules 40%, Individual research project 40%, Group Project 20%
  • QualificationMSc, PgDip, PgCert
  • Study typeFull-time
  • CampusCranfield campus

Who is it for?

This course provides engineering, physics or mathematics graduates with the advanced skills which can be applied to the security, defence, marine, environmental and aerospace industries.

Why this course?

This course is unique in that it offers a combination of subjects much sought after in the Autonomous Vehicle Industry and not covered in a single MSc course anywhere else. Successful graduates of our MSc course become conversant in key aspects of Autonomy which advantageously differentiates them in today's competitive employment market

The Autonomous Vehicle Dynamics and Control MSc course begins with the fundamentals of autonomous vehicle dynamics and control, and progresses to the core subjects of guidance & navigation, decision making, sensor fusion, data & information fusion, communication & and networking. A choice of optional modules allows individual tailoring of these subjects to specialise in appropriate subject areas.

The taught part of the course is followed by Individual Research Projects (IRPs) and the topic of each of the IRPs is provided by one of the member of the Industrial Advisory Board. The real-world relevance of the IRP topics is another unique feature of our MSc course and can be another effective differentiator in the job market.

Informed by Industry

The relevant, competent and pro-active Industrial Advisory Board includes:

  • BAE Systems
  • Airbus Defence & Space
  • Thales UK
  • Leonardo
  • Raytheon UK
  • Lockheed Martin UK
  • Boeing UK (Phantom Works)
  • UTC Aerospace Systems
  • QinetiQ
  • Spirent Communications
  • Tekever
  • MASS Consultants
  • Plextek
  • Stirling Dynamics
  • RaceLogic

who not only continuously advise on updating the course content but also provide topics for Individual Research Projects (IRPs). After the final oral exams in early September, all students present posters summarising their IRPs to the whole Industrial Advisory Board thus exposing their work to seasoned professionals and potential employers. The IRPs benefit from our own lab where real autonomous vehicles can be designed and tested.

Course details

The taught course element consists of lectures in three areas: dynamics, control systems, and autonomous systems and technology. The MSc consists of two equally weighted components, taught modules and an individual research project.

Course delivery

Taught modules 40%, Individual research project 40%, Group Project 20%

Individual project

Our industry partners sponsor individual research projects allowing you to choose a topic that is commercially relevant and current. Topics are chosen during the first teaching period in October and you begin work during the second half of the MSc course (May - August). The project allows you to delve deeper into an area of specific interest, taking the theory from the taught modules and joining it with practical experience.

Projects encompass various aspects of operations, not only concerned with design but including payloads, civil applications, system, sensors and other feasibility studies industry wishes to explore.

For the duration of the project, each student is assigned both a university and industry supervisor. In recent years, students have been based at sponsor companies for sections of their research and have been given access to company software/facilities.

During the thesis project all students give regular presentations to the course team and class, which provides an opportunity to improve your presentation skills and learn more about the broad range of industry sponsored projects.

Previous projects have included:


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.

Course modules

Compulsory modules
All the modules in the following list need to be taken as part of this course

Introduction to Unmanned Aircraft Systems (UAS)

    The aim of this module is to provide the AVDC MSc students with an overview of the course and to introduce the main aspects the Autonomous Systems underpinning the course, including interactive flight demonstrations of UAS. This module structure also allows it to be offered as a short course under the same title.
    • Introduction to the AVDC course
    • UAS design parameters
    • UAS composite materials & structures
    • UAS: Strength of structures
    • UAS flight demonstration: Indoor flight
    • UAS components: mechanical & electrical
    • Overview of UAS Power & Propulsion
    • Overview of UAS Guidance & Navigation
    • Overview of UAS Sensor Fusion
    • Overview of Artificial Intelligence for UAS
    • Overview of UAS Operations
    • UAS flight demonstration: Outdoor flight

Intended learning outcomes

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

1) Specify the main practical applications of Unmanned Aircraft Systems (UAS) and define their engineering subsystems.
2) Evaluate the main engineering challenges of UAS analysis and design.
3) Analyse qualitatively the functions and capabilities of the main subsystems of UAS
4) Specify ethical concerns and legal issues concerning UAS operations

UAS Modelling and Simulation

    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
Intended learning outcomes

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

1. Evaluate and implement an example UAV 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 on a commercial of the shelve flight control board and carry out simulation-in-the-loop testing.
4. Communicate and present both orally and in writing results of individual work.

Sensor Fusion

    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)
Intended learning outcomes

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.

Artificial Intelligence for Autonomous Systems

    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 (1 lecture)
    • AS sensors and imaging (4 lectures)
    • Demo: Indoor autonomous surveillance (2 lectures)
    • AI Algorithms: Supervised Learning―Neural Networks (7 lectures)
    • AI Algorithms: Supervised Learning―Lab (3 lectures)
    • AI Algorithms: Unsupervised Learning―Clustering (3 lectures)
    • Automated Reasoning: Bayesian Networks (2 lectures)
    • Relevance Filtering: Visual Attention (2 lectures)
    • Case Study: AI for AS (4 lectures)

Intended learning outcomes

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

1) Formulate the fundamental meaning and applicability of Artificial Intelligence (AI) algorithms for Autonomous Systems (AS).
2) Categorize AI methods for real-life scenarios of AS applications.
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.

Guidance and Navigation for UAS

    This module aims to provide fundamental and critical understanding of classical and advanced guidance and navigation approaches for Autonomous Systems. A key aim is for the students to critically understand the problems of guidance and navigation, the fundamental solution approaches together with their advantages and limitations.
    • 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.
Intended learning outcomes

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

1. Evaluate the fundamentals of the various guidance techniques and their properties.
2. Appraise the algorithms that are required to produce an estimate of position and attitude;
3. Critically examine the characteristics, purposes, and design procedures of guidance and navigation systems;
4. Assess the challenges of guidance and navigation approaches for autonomous systems;
5. Examine the challenges of cooperative guidance and navigation approaches for autonomous systems.

Autonomous Vehicles Control Systems

    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 Flight 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;
    • H-infinity control: loop shaping approach and LMI approach;
    • Optimal and non-linear control system design and its application to autonomous vehicles;
    • Gain scheduling

Intended learning outcomes

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.

UAS Dynamics and Control

Logic and Automated Reasoning

Module Leader
  • 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
    • Engineering domain knowledge representation
    • Exercises and case studies
Intended learning outcomes

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

1.    Analyse syntax and semantics of first-order logic and different knowledge representation using the appropriate formal language.

2.    Use logical programming to build knowledge bases.

3.    Design strategies to solve assigned reasoning problems detailing the execution steps to perform the given tasks.

Teaching team

You will be taught by Cranfield's experienced academic staff. Our staff are practitioners as well as tutors, with clients which include the members of the Industrial Advisory Board and beyond. Knowledge gained working with our clients is continually fed back into the teaching programme, to ensure provision of durable and transferrable skills practised on problems relevant to Industry. Additionally, experienced members of the Industrial Advisory Board deliver Industrial Seminars in which they share their experience and explain the research & development proprieties of their companies.

Autonomous Vehicle Dynamics and Control is a very unique course. It has a lot of interesting areas. In particular, the cutting edge technology. This course will definitely help me in my future career. Cranfield is a very good learning environment.
The overall experience has been really positive, and has provided me with the needed tools to find a job that I love and where I can put forth all the knowledge that I gained during the MSc.

Your career

The industry-led education makes Cranfield graduates some of the most desirable all over the world for recruitment by companies competing in the autonomous vehicle market including:

  • BAE Systems
  • Defence Science and Technology Laboratory
  • MBDA
  • Other companies from our Industrial Advisory Board.

Graduates from this course will be equipped with the advanced skills which could be applied to the security, defence, marine, environmental and aerospace industries. This approach offers you a wide range of career choices as an autonomous systems engineer, design engineer or in an operations role, at graduation and in the future. Others decide to continue their education through PhD studies available within Cranfield University or elsewhere.

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.