Control and Optimisation - 2019 entry

Most technological and environmental systems need to be managed or optimised in real-time. In this module you will focus on optimisation and control for engineering systems, for example novel problems posed by integrating clean energy systems and smart grid technologies. The same concepts and tools also apply to environmental and ecological systems, for example in the regulation of complex water networks, the management of pests in food crops or invasive species in the natural environment. Key issues include the development of smart monitoring and regulation, optimality, stability and robustness. Throughout the module, ideas and tools are developed for systems modelling real-world relevant applications.

In this module you will develop expertise in modern mathematical and computational tools from control theory and its application. You will develop a general perspective on controller design for optimal, adaptive and robust control problems. You will learn how to apply these general principles in a context of input-state and input-output control systems. You will study specific examples of optimal control, for example L(inear) Q(uadratic) G(aussian) approaches; of adaptive control, for example tracking control approaches; and for robust control, for example high-gain feedback designs. You will gain hands-on experience of computational implementation of these control schemes and develop an appreciation of issues such as robustness and computational complexity.

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

Module Specific Skills and Knowledge:

1 Formulate and solve a range of control problems;

2 Understand essential trade-offs and constraints in control problems;

3 Use relevant computational tools to find/approximate solutions;

4 Understand aspects of stability, optimality and robustness and in control system design;

Discipline Specific Skills and Knowledge:

5 Communicate the importance of optimality and robustness in management and control;

6 Use a range of computational platforms/software;

Personal and Key Transferable/Employment Skills and Knowledge:

7 Communicate the value of optimisation and control to a range of end users in the energy and environmental sciences sector;

8 Engage with stakeholders from the engineering, environmental and life sciences.

The aim of this module is to make sure that the control approaches considered are modern and current and so the specific topics may vary over time. The specific topics will be covered in three-week blocks in which the approach is introduced and then applied within mini-project based work. Examples of the material to be covered include:

Optimal Control (e.g., Pontryagin’s maximum principle, Hamilton-Jacobi-Bellman equations, L(inear) Q(uadratic) G(aussian) control, Model Predictive control)

Feedback Control (e.g., PID, back-stepping, input-state linearisation)

Robust & Adaptive Control (e.g., H-infinity control, Model Reference Adaptive control, Lambda-tracking).

If a module is normally assessed entirely by coursework, all referred/deferred assessments will normally be by assignment.

If a module is normally assessed by examination or examination plus coursework, referred and deferred assessment will normally be by examination. For referrals, only the examination will count, a mark of 40% being awarded if the examination is passed. For deferrals, candidates will be awarded the higher of the deferred examination mark or the deferred examination mark combined with the original coursework mark.

Basic Reading:

ELE: http://vle.exeter.ac.uk

Reading list for this module: