WM9SA-15 Intelligent Navigation and Control
Introductory description
This module addresses the challenges in automated systems and control to enable a smooth transition between automated to autonomous vehicles. It gives insight into how adaptive strategies including machine-learning based methods such as fuzzy logic could be adopted to design intelligent adaptive control systems for improved vehicle autonomy. Moreover, it discusses the challenges in vehicle path planning and decision making considering cooperative control paradigms.
Module aims
The aim of the module is to provide a comprehensive understanding and practical experience of automated systems and control within an automotive context. Developing both theoretical and practical understanding of the automated systems and adaptive, self-learning control system design by establishing effective connection with the concepts such as sensor fusion, machine-human interaction and machine learning learnt from other taught modules.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Classical control systems
Machine learning-based self-tuning/adaptive strategies for automated systems.
Observers and filters for estimation.
Design of adaptive control systems for automated vehicles.
Decision system concepts: shared control and path planning.
Learning outcomes
By the end of the module, students should be able to:
- Critically evaluate different control approaches and demonstrate understanding in the system nonlinearities and uncertainties, (AHEP4, M2, M3, M4, M5, M6, M17).
- Critically evaluate different control strategies, ranging from classical to intelligent approaches to attain increased autonomy (AHEP4, M1, M2, M3).
- Demonstrate the skills to design adaptive vehicle control systems by identifying the right control and estimation approaches (AHEP4, M2, M3, M5).
- Demonstrate the skills to develop an intelligent strategy to resolve the challenges in autonomous navigation and vehicle cooperative control (AHEP4, M4, M5, M6, M17).
Indicative reading list
Reading lists can be found in Talis
Specific reading list for the module
Subject specific skills
Understand Vehicle dynamics
Evaluating different adaptive strategies
How to design an adaptive control system for an automated vehicle.
Evaluating the challenges in implementing adaptive strategies in real-time.
Critically evaluating challenges in path planning and cooperative control
Transferable skills
Technology literacy
Dependability
Communication
Adaptability.
Developing a technical proposal and presentation.
Study time
| Type | Required |
|---|---|
| Lectures | 20 sessions of 1 hour (13%) |
| Tutorials | 10 sessions of 1 hour (7%) |
| Online learning (independent) | 12 sessions of 1 hour (8%) |
| Private study | 48 hours (32%) |
| Assessment | 60 hours (40%) |
| Total | 150 hours |
Private study description
Practising simulation tools.
Reviewing and refreshing the knowledge of linear algebra and engineering mathematics.
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Modelling and preliminary controller design | 20% | 12 hours | Yes (extension) |
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Students are expected to complete questions on modelling ( system dynamics, nonlinearity and linear models) and controller design using simulation tools. |
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Reassessment component |
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| Modelling and preliminary controller design | No | ||
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Students are expected to complete questions on modelling ( system dynamics, nonlinearity and linear models) and controller design using simulation tools. |
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Assessment component |
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| Automated Vehicle Controller Design | 80% | 48 hours | Yes (extension) |
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Design an adaptive controller for an automated vehicle. The students are expected to produce a proposal, a design report. |
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Reassessment component |
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| Automated Vehicle Controller Design | No | ||
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Design an adaptive controller for an automated vehicle. The students are expected to produce a proposal, a design report. |
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Feedback on assessment
For both assessments written feedback and overall mark.
There is currently no information about the courses for which this module is core or optional.