Course Details in 2024/25 Session
|Module Title ||LM Sensing and Control for Autonomous Systems|
|School||School of Engineering|
|Department || Elec, Elec & Sys Engineering|
|Module Code || 04 30055 |
|Module Lead ||Prof Clive Roberts|
|Level || Masters Level |
|Credits || 20 |
|Semester|| Semester 2|
|Restrictions || None |
Supervised time in studio/workshop-24 hours
Guided independent study-142 hours
Total: 200 hours
|Exclusions || |
|Description || This module will focus on the techniques and methods required for the development of autonomous systems. The module will consider:|
- How sensors are used to sense the world;
- Modern control methods that can be used to mathematically model or estimate the function of real systems in some way;
- A range of autonomous decision making approaches, including optimisation to ensure desired outcomes;
- Actuation and overall system design, including: safety integrity levels; common model failures; fault detection, diagnosis and prognosis; and fault tolerance.
The module will draw on mathematical approaches, but will be primarily practically focussed. Real systems will be considered throughout the module, with a range of systems (primarily, but not limited to, electro-mechanical sub-systems) being used as examples through the module to support the theoretical approaches and to ensure relevance.
Initially the module will consider the range of autonomous systems that are currently in use (e.g. automatically operated trains, robotic control), and are likely to come into use in the future (e.g. driverless cars, fault tolerant systems). Students will use these examples to consider the uses for autonomous systems, and common building blocks (sensing, control strategies, automation and actuation), as well as overall system design aspects.
The module will then be considered in four phases (Phase 1 – sensing, Phase 2 – control strategies, Phase 3 – automation, Phase 4 – actuation and systems).
Introduction – Use of autonomous systems (2 hours of lectures)
Phase 1 – Sensing (2 hours of lectures) – considering: (i) the range of sensors that may be used; (ii) methods for data acquisition, and issues associated with different techniques (e.g. Nyquist, noise, etc.); (iii) approaches to acquire difficult to measure parameters.
Phase 2 – Control Strategies (12 hours of lectures) – considering: (i) modelling dynamic systems using transfer functions, with a particular focus on electro-mechanical systems; (ii) model based control; (iii) stability of control systems; (iv) multiple-input-multiple-output systems; (v) state space analysis; (vi) controllability and state observability; (vii) feedback control methods using observability and parameter estimation; (viii) fuzzy control; (viii) digital control.
Phase 3 – Automation (10 hours of lectures) – considering: (i) rule based and optimisation approaches; (ii) Brute Force and enumeration; (iii) linear programming; (iv) genetic algorithms; (v) graph based approaches; (vi) dynamic programming; (vii) simulated annealing; (viii) ant colony; (ix) Tabu search; (x) other artificial intelligence approaches.
Phase 4 – Actuation and systems (4 hours) – considering: (i) timings; (ii) processing requirements; (iii) hardware development; (iv) system dependability (reliability, availability and safety); (v) fault detection, diagnosis and prognosis; (vi) fault tolerance; (vii) overall system design.
The module will be supported by two distinct laboratory exercises (that will use Matlab/Simulink to support the theory covered in the lectures (24 hours across the two exercises). The first laboratory will focus on control strategies (Phase 2), while the second laboratory will consider automation and overall system design (Phases 3 and 4).
|Learning Outcomes || By the end of the module students should be able to:|
Select appropriate sensors and data acquisition hardware to instrument electro-mechanical equipment, with a full awareness of practical constraints and real-world problems.
Be able to use model based and state based control to module systems, and carry out system analysis.
Assess stability, controllability and observability and change control systems to more useful forms.
Design fuzzy and digital controllers.
Use, and understand the benefits and dis-benefits, of a wide range of optimisation techniques to solve problems.
Be aware of the range of practical system design issues, and philosophies that can used to help design real systems.
30055-01 : Module Mark : Mixed (100%)
|Assessment Methods & Exceptions || Assessments: One 3 hour exam in the main examination period (70%) plus two design reports based on laboratory work (30%) |
|Other || |