The global market for drones and autonomous aerial vehicles is rapidly growing. With a rise in applications for unmanned aerial vehicles (UAV) and uncrewed aircraft systems (UAS), the defence and aerospace 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.

Students on the MSc in Autonomous Vehicle Dynamics and Control benefit from a distinct educational experience and unique facilities, including our drone laboratory, allowing you to gain hands-on experience in the development of the autonomous flight systems and technologies of tomorrow. The course content has been designed based on advice and industry insights from our Industrial Advisory Board, comprising industrial representatives from big primes to small- and medium-sized enterprises, and is accredited by the Royal Aeronautical Society (RAeS) on behalf of the Engineering Council as meeting the requirements for Further Learning for registration as a Chartered Engineer.


  • Start dateOctober
  • DurationMSc: Full-time one year;
  • DeliveryTaught modules 40%, individual research project 40%, group project 20%
  • QualificationMSc
  • Study typeFull-time
  • CampusCranfield campus

Who is it for?

This course provides graduates from a range of disciplines with the advanced skills that can be applied to the security, defence, marine, environmental and aerospace industries. Typically, students have academic backgrounds in engineering, science, physics or applied mathematics and have a keen interest in aerospace and autonomous aerial vehicles.

Why this course?

This course is unique in that it offers a combination of subjects much sought after in the autonomous vehicle industry. Successful graduates of our MSc course become conversant in key aspects of autonomy which places them at an advantage in today's competitive employment market.

The Autonomous Vehicle Dynamics and Control MSc includes eight taught compulsory modules, which are generally delivered from October to March. The course begins with the fundamentals of autonomous vehicle dynamics and control, and progresses to the core subjects of guidance and navigation, sensor fusion, advanced control, decision making, AI for autonomous systems.

The taught segment 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 the MSc course and can be another effective differentiator in the job market.

A key feature of the MSc is the inclusion of a CAA approved UAV remote pilot competence course. The course incorporates a ground school element for flight planning – covering principles of flight, rules and regulations of the air, using aviation charts, risk assessment and meteorology – and flight training to provide basic pilot competence, including how to respond in an emergency and being able to operate safety features. Successful completion of the course allows students to fly small UAV’s in the Open Category.

Discover the unique facilities available to you as a student on this course: My subject | Cranfield University Virtual Experience

Informed by industry

This course has been designed with industry in mind, and continues to be developed and kept up to date in collaboration with our Industrial Advisory Board. This includes:

Boeing UK Connected Places Catapult
Thales Spirent
Ultra Electronics BAE Systems
Barnard Microsystems QinetiQ
FlyLogix Limited Jaguar Land Rover UK
MBDA General Atomics Aeronautical Systems UK
Blue Bear Systems Research Ltd. Rolls-Royce
TTTech Computertechnik Lockheed Martin UK
Northrop Grumman BioCarbon Engineering

Members of the Board 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 exhibiting 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 course consists of two equally weighted components, taught modules and individual research project, and a group project.

Course delivery

Taught modules 40%, individual research project 40%, group project 20%

Group project

The group design project facilitates the design, build and operation of autonomous vehicles, thus integrating and applying the knowledge students acquire in the taught modules. The group design project also aims to provide students with experience of working on a collaborative engineering project, within an industry structured team, developing transferable skills that include working in a team with members having diverse backgrounds and expertise, project management and technical presentations.

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 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 

Intended learning outcomes

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.

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. 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.

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 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 (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)
Intended learning outcomes

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

  1. Categorise 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.

Guidance and Navigation for Autonomous Systems

    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.
Intended learning outcomes

On successful completion of this module you 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.


Autonomous Vehicle 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 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;

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

    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
Intended learning outcomes

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;
Analyse, design and assess the performance of both state space and (classical) frequency domain UAS control systems

Logic and Automated Reasoning

    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
Intended learning outcomes

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.

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 deliveriIndustrial seminars in which they share their experience and explain the research and development proprieties of their companies. The Course Director for this programme is Dr Argyrios Zolotas.

This MSc course was interesting and coherent, and the cutting edge technology tailored towards aeronautical industry’s upcoming developments. I had the opportunity to experience new challenges and work on impressively ambitious projects which were highly rewarding. It provided me with the skills and knowledge I needed to start my career, and I found the job I wanted immediately after completing the MSc. Moreover, with brilliant staff and outstanding facilities, Cranfield is a wonderful place to study.

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.
I chose to study at Cranfield University due to its great reputation and the impact that students and staff play in the world. A highlight from my time at Cranfield has been being able to work with dedicated and very professional members of staff and academics. My Individual Thesis Project focused on Flight Controller Actuator Controller Development and was sponsored by Domin Fluid Power. After I finish my MSc my will be working full time in France at Safran Aircraft Engines.
I chose to study at Cranfield University as it is highly specialised in my field. A highlight from my time would have to be the Group Project, my project focused on a UAV swarm for search and rescue missions. For my Individual Research Project, I worked on a Multi-UAV Surveillance which was sponsored by Barnard Microsystems.


The Autonomous Vehicle Dynamics and Control MSc is accredited by the Royal Aeronautical Society (RAeS) on behalf of the Engineering Council as meeting the requirements for further learning for registration as a Chartered Engineer (CEng). Candidates must hold a CEng accredited BEng/BSc (Hons) undergraduate first degree to show that they have satisfied the educational base for CEng registration.

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.

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

How to apply

Click on the ‘Apply now’ button below to start your online application.

See our Application guide for information on our application process and entry requirements.