Advanced Control and Optimisation

Course structure

The course will be delivered as lectures, supported by tutorials and in-class practice exercises using MATLAB and Simulink. All delegates will receive a Certificate of Attendance at the end of the course.

What you will learn

In completing this course you should be able to:

• Create theoretical and computer models of multivariable automotive systems.
• Apply different advanced control techniques to automotive control problems.
• Use MATLAB and Simulink (commercial software packages) to design control algorithms for automotive systems.
• Design state estimators for multivariable automotive control systems using established techniques.
• Judge the suitability of a given control technique to a particular application in the context of automotive control.

Core content

1. Modelling multi-variable systems
   
   
   1. Describing multi-variable systems using state-space representations
   2. Using norms to describe the sizes and behaviours of signals and systems
   3. Modelling uncertainty, noise and non-linearities
   4. The Nyquist stability criterion and robustness.
      
      
2. Using optimisation in multi-variable control
   
   
   1. Representing feedback using state-space techniques
   2. Pole-placement techniques
   3. Optimal control using the Linear-Quadratic Regulator (LQR)
   4. Introduction to Model-Predictive Control (MPC).
      
      
3. Estimator design
   
   
   1. Multi-variable estimator design using pole-placement techniques
   2. Optimal estimator design for linear systems using the Kalman Filte.
   3. Introduction to optimal control using Linear-Quadratic-Gaussian (LQG) techniques
   4. Introduction to non-linear Kalman filtering techniques.
      
      
4. Neoclassical control
   
   
   1. SISO design using the Youla parameter technique
   2. Direct shaping of S(s) and T(s) and the associated stability criteria
   3. MIMO design using the Youla parameter technique.
      
      
5. Robust control
   
   
   1. H∞ loop-shaping
   2. Estimating robust performance using the v-gap metric
   3. Shaping R(s) using two degree-of-freedom compensators.

Who should attend

This course is aimed at practising control engineers who already have a strong grasp of classical control and wish to develop their understanding of advanced multivariable control techniques based on optimisation. Although useful theory will be introduced, the emphasis will be on its application. The course is targeted at delegates from the automotive sector but the principles and methods are equally applicable in many other applications.

At the start of the course, the learner will be expected to have a good grounding in the fundamentals of control engineering.  (There will be some degree of recap, but delegates will be expected to be familiar with the key concepts of single-variable feedback control and classical frequency-domain design, e.g. gain/phase margins, PID control and lead-lag compensation.) Learners will also benefit from a conceptual understanding of optimization.  Familiarity with MATLAB and Simulink will be expected.

Speakers

• Dr Daniel Auger
• Dr Stefano Longo

Concessions

20% discount for Cranfield alumni.

10% discount when registering 3 or more delegates from the same organisation at the same time.

Accommodation options and prices

This is a non-residential course. If you would like to book accommodation on campus, please contact Mitchell Hall or Cranfield Management Development Centre directly. Further information regarding our accommodation on campus can be found here. 

Alternatively you may wish to make your own arrangements at a nearby hotel.

Location and travel

Cranfield University is situated in Bedfordshire close to the border with Buckinghamshire. The University is located almost midway between the towns of Bedford and Milton Keynes and is conveniently situated between junctions 13 and 14 of the M1.

London Luton, Stansted and Heathrow airports are 30, 90 and 90 minutes respectively by car, offering superb connections to and from just about anywhere in the world. 

For further location and travel details

Location address

Cranfield University
College Road
Cranfield
Bedford 
MK43 0AL