This course is new and was designed for control specialists or to advance the learning for those that have attended the Control Fundamentals courses. It covers a range of advanced control topics in an overview and introductory form describing some of the most important problems in control design and applications:
Professor Michael Grimble
Professor Mike Grimble understands the needs of industry well, having worked for Ciba Geigy and Associated Electrical Industries (later GEC at Rugby). At Imperial College's Industrial Automation Group, he focused on modelling for the control of cold rolling mills. He later established industrial groups at Sheffield Hallam University and the University of Strathclyde where he continues as a Research Professor. His expertise lies in designing high-performance, robust control systems for various industrial applications. His industrial background enriches the industrial training courses with valuable motivation and insights.
Dr Pawel Majecki
Dr. Pawel Majecki conducted his research at the Industrial Control Centre at Strathclyde University before joining Industrial Systems and Control Ltd (ISC), which promotes technology transfer. He has worked with major international companies, applying advanced control methods, including predictive and optimal controls. Dr. Majecki has also led control training courses in the UK, Norway, Italy, Spain, and the USA. His extensive experience with MATLAB/Simulink enhances the hands-on training, helping delegates gain deeper insights into these tools and their application in design methods.
Delegates will find both instructors delighted to answer questions and discuss industrial problems during coffee breaks, lunch breaks, and at the end of the day.
Glasgow G2 1LU
Glasgow City Centre offers a wide range of accommodation, you can find our recommendations here.
Timings may change slightly.
Day 1: Introduction to Modelling, Multivariable and Optimal Control |
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9.00 | Welcome and Introduction to the Course |
9.15 | Modelling of Dynamic Systems – Modelling of linear and nonlinear systems to be controlled, model requirements, state space models, benefits of simulation |
10.15 | TEA/COFFEE |
10.30 | Kalman Filters and Observers for State Estimation – Optimal estimation problems and Kalman filtering algorithms or observers for linear, time-varying and LPV systems, and for use in MPC control solutions |
11.30/td> | Hands-On Session: State Estimation for Systems - Powertrain Example, Stochastic Systems, State estimation using Kalman Filter, Observers |
12.30 | LUNCH |
13.30 | Introduction to Multivariable Control Design and Stability – Multivariable system models, Model Structures and Compensation, Relative Gain Array, Stability |
14.15 | TEA/COFFEE |
14.30 | Introduction to LQ and LQG Optimal Control - Optimal Control Techniques for Linear Systems, Optimal control cost-functions, LQ control, LQG control, Natural Robustness properties |
15.30 | Hands-On Session: LQ State Feedback Optimal Control and Robustness Properties - Flight Control Example, Gain & Phase Margins of state feedback solutions |
16.15 | Design Example Using LQG Control - Thickness control, Kalman filtering and LQG design example |
17.00 | CLOSE |
Day 2 - Introduction to Uncertainty and Robust Control |
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09.00 | Uncertainty in Systems and Robustness - Modelling uncertainty, control design in presence of uncertainty. |
10.00 | TEA/COFFEE |
10.15 | Hands-On Session - LQG Stochastic Control Design - Flight Control Example, Disturbance Rejection, Robustness, use of Dynamic Cost-Function Weightings |
11.15 | Introduction to Robustness and H∞ Control - H∞ cost measures, Standard system model control structures with uncertainty |
12.15 | LUNCH |
13.15 | Hands-On Session: Robustness and H∞ Robust Control System Design - F16 flight control H∞ design example |
14.15 | Design Example Using Kalman Filtering, LQG, H∞ Methods – Modelling, estimation and LQG or H∞ design stages, and results, using a positioning control design example |
15.15 | TEA/COFFEE |
15.30 | Quantitative Feedback Theory Robust Control Design Method – frequency response based robust control design method, design example remote pilotless vehicle |
16.00 | Introduction to Linear Model Predictive Control – Optimal constrained and unconstrained control, operation of basic algorithms, pros and cons |
17.00 | CLOSE |
Day 3 - Predictive and Nonlinear Control Systems Design |
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09.00 | Model Predictive Control: Use of linear parameter varying models to approximate nonlinear systems |
10.00 | TEA/COFFEE |
10.15 | Optimisation and Quadratic Programming Solvers – Convex optimisation problems, Use in MPC design |
11.00 | Hands-On Session: Introducing Model Predictive Control Design Approach and Performance Results - vehicle suspension control problem |
12.00 | LUNCH |
13.00 | Overview of Nonlinear Control Techniques – The range of nonlinear control design options available |
14.00 | Hands-On Session: Introducing Nonlinear Control System Problems and Design Methods |
15.00 | TEA/COFFEE |
15.15 | Servo System Design Study: for Nonlinear Control Design Applications – Sightline Stabilisation of Electro-Optical Devices |
16.00 | Model Based Advanced Control Methods - Summary of Important Features of Model Based Design Methods Covered in the course and Recent Developments |
16.50 | End of Course Questions and Close |
For two or more places from the same organisation, each additional place is 10% off the single place fee.
Please complete the Online Registration Form.
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