Advanced feedforward control
Price:€ 2.245,00 excl. VAT
Duration:3 consecutive days
Contact:email@example.com, +31 85 401 3600
The training focuses on techniques to improve the performance of your system by advanced feedforward and learning control. In recent years, classical feedback controllers and feedforward controllers have been further developed towards advanced feedforward. These developments include iterative learning control, which applies to industrial systems, including pick-and-place machines or batch processes, which perform the same task over and over again. When exactly the same task is performed, disturbances act on the system identically over the tasks.
Think, for instance, about a disturbance torque profile, from unbalance in an axis, or from unknown friction effects. The key idea is that iterative learning control can completely compensate for these disturbances, leading to a typical order of magnitude reduction of servo errors.
Iterative learning control is able to achieve exceptional performance for exactly repeating tasks. However, iterative learning control cannot deal with varying tasks. Even a small variation can lead to disastrous performance deterioration. Many industrial systems perform such very similar yet slightly different tasks, necessitating new concepts for advanced feedforward control, including high-order feedforward (snap, jerk, etc.), input shaping, automated tuning, etc. These new concepts have been developed in recent years, whereas most of these 'iterative learning control' (ILC) techniques have been developed in the past two decades and many successful industrial applications have been reported.
This new and extended (2015) course starts by recapitulating classical feedforward, and covers an in-depth treatment of iterative learning control and new advanced feedforward approaches. The course enables participants to:
- apply basic feedforward for motion control,
- recognize which technique is applicable to the specific application at hand (e.g., iterative learning control, repetitive control, and new advanced feed-forward algorithms),
- analyze and design ILC controllers addressing both performance and robustness (convergence), both in frequency domain and lifted domain,
- implement and analyze basis functions in ILC,
- auto tuning of advanced feedforward controllers,
- apply input shapers and rational feedforward,
- obtain hands-on experience on an industrial mechanical positioning system.
This course is intended for engineers involved in control systems who want to gain more insight into the possibilities and implementations of advanced feedforward and learning control in an industrial setting.
It is recommended that participants already have a Bachelor or Master education in electrical engineering, mechanical engineering, mechatronics, physics, or equivalent practical experience and must have some basic understanding of servo control.
This course is particularly suitable for engineers having followed the course in 'Motion control tuning'.
The following topics are treated:
- Overview application areas
- Feed-forward design for high-precision motion systems: velocity, mass, jerk, snap feedforward
- Iterative learning control: basic principles, frequency domain approach, convergence and robustness analysis, and design
- Lifted iterative learning control: basic principles, analysis, optimal design
- Basis functions in iterative learning control
- Automated feedforward tuning through identification-based approach
- Input shaping and rational feedforward
- Repetitive control: basic principles
- Practical examples
- Simulation and design of systems using Matlab and SIMULINK
- Hands-on experience with real-time implementation using SIMULINK on HP printer setup.
This course is certified by the European society for precision engineering & nanotechnology (euspen) and the Dutch Society for Precision Engineering (DSPE) and leads to the ECP2-certificate.
“Great atmosphere, good lecturers, well done!” – Thijs Kniknie (NXP)
“Most important items learned: ILC in general. Hands one experiments and experience.” – Joris van den Boom (ASML)