TU Delft
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2016/2017 Aerospace Engineering Master Aerospace Engineering
Aerospace Human-Machine Systems
Responsible Instructor
Name E-mail
Dr.ir. M.M. van Paassen    M.M.vanPaassen@tudelft.nl
Name E-mail
Dr.ir. C. Borst    C.Borst@tudelft.nl
Prof.dr.ir. M. Mulder    M.Mulder@tudelft.nl
Dr.ir. D.M. Pool    D.M.Pool@tudelft.nl
Contact Hours / Week x/x/x/x
Education Period
Start Education
Exam Period
Course Language
Required for
There are two follow-up courses for this course:

* AE4318 Supervisory Control. This course focuses on the cognitive aspects of human-machine interaction, using Ecological Interface Design.

* AE4319 Manual Control. This course focuses on the skill aspects of human-machine interaction, with a more in-depth study of McRuer's crossover model, and measurement of human control behaviour.
Expected prior knowledge
Control theory (stability and Bode plots); signals AE2235 Aerospace Signals,
Systems & Control, AE4301 Automatic Flight Control System Design

Stochastic signals; AE4304 Stochastic Aerospace Systems

Flight instruments and procedures; AE4302 Avionics and Operations
Course Contents
This course focuses on the various aspects of actual and future aircraft cockpit human-machine interfaces. It provides an extensive theoretical as well as practical knowledge on the specific characteristics of human behavior such as human perception, human mental processing, cognitive factors, and the role of the human pilot in manual and supervisory control tasks. The design and set-up of experiments is also discussed.
Study Goals
Overall, the student will have a working knowledge of human operator (pilot) characteristics that are relevant for the design and evaluation of human-machine systems. Specific study goals:

The student:
* Is able to classify different types of human behaviour according to Rasmussen's rule-skill and knowledge taxonomy
* Is able to predict performance and human behaviour in manual control tasks
* Knows the physiology and characteristics of human sensory systems and actuation processes (visual, vestibular, propioceptive senses and neuromuscular system), and is able to predict the implications of these properties for human perception and behaviour
* can characterize and compare 2D and 3D interfaces
* can analyze accident and incident reports, find latent and active errors and classify these with Rasmussen's SRK taxonomy and identify Reason's error shaping factor. The student understands the wider context of human error (Dekker's "new view").
* is familiar with workload and situation awareness, and knows which methods are used to measure these properties
* understands the nature of human cognition, can distinguish between different views and models of cognition and knows when these are applicable
* is knowledgeable about experimental design, can make and provide arguments for choices in experimental set-up, can choose apply common statistical methods for evaluating experimental results
Education Method
Lectures with activating elements. Week arrangement:

1. Introduction + SRK
2. Crossover model I
3. Crossover model II
4. Visual systems I (the eye and motion perception)
5. Visual systems II (display design principles)
6. Vestibular system
7. Neuromuscular system
8. Automation
9. Human Error
10. Cognition
11. Workload
12. Experimental Design I
13. Experimental Design II
14. Guest lecture (45 min) + example exam questions (45 min)

The course also includes a demonstration session in the human machine laboratory.
Written exam. The exam typically consists of 6 questions, covering a selection of the course's topics.