What we do

Smart control algorithms

What we do

  • PID
  • LQR
  • iLQR
  • Kalman filtering
  • Cross entropy method (cem) mpc
  • Model predictive path integral control (MPPI)
  • Reinforcement learning
  • dynamics modelling (deep learning)
  • Particle swarm optimization

Smart control algorithms

As control engineers we are experts in optimizing machines and processes. By closely following the latest advances in control engineering and being able to integrate the classic control algorithms with deep learning leads to incredibly smart control solutions. We deliver custom designed control solutions with an unseen performance. Smart control algorithms can be used in almost any industry, whether it is path tracking, predictive temperature control or flow control, everything is possible. Simple controllers such as PID or heuristic approaches can only optimize your process suboptimally resulting in big accumulated losses overtime. With a smart control you can squeeze out every last bit your machine/process has to offer!

Smart control algorithm (Reinforcement learning)

Dynamics modelling

Smart control dynamics modelling

Dynamics modelling

Smart control applications need a good dynamics model to operate at their full potential. Usually the dynamics of machines and processes cannot sufficiently be modelled with physical equations due to the inability to capture friction and other unknown influencing factors. We resort to a data driven method to capture the machine dynamics using machine or deep learning. This allows to capture strongly non-linear machine dynamics which are inherently hard to control. Also a combination of physics and deep learning is used when the data is scarce. Since machine dynamics continuously change during the lifetime of a machine we also allow for online dynamics optimization such that their is always an optimal control feedback.

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