In contrast to classical models, neural networks represent numerous non-linear functions and thus represent complex and multi-dimensional problems.

Any number of complex non-linear effects can be represented, with the major limitation that many process data must be available and the model is only valid for plant states present in these process data (no extrapolability).

Neural networks independently extract relevant correlations from large amounts of data. This makes them a valuable addition to physical-logical modeling, which is often inadequate or only feasible with a great deal of effort.

They use artificial intelligence to model and control complex processes and have the ability to recognize and adapt to non-linear relationships in the process. They are used in pattern recognition and predictive maintenance.

In practice, we use neural networks as so-called black-box models.

We address the following problems with neural networks

• Non-linear system identification

• State observer design

• Controller design

  • Neural adaptive control

  • Neural optimal control

  • Reinforcement learning

  • NLq stability theory

PID controller (proportional-integral-derivative controller):

• Widely used and versatile

• Consists of three components: the proportional, the integral and the differential part

• Used to control processes where fast and precise reactions are required

PID controllers are the most versatile type of controller. We adapt the controller behavior to your specific application using a variety of practical approaches:

• Intelligent min/max limits

• Situational freezing of the controller output

• Feed-forward control

• Gain scheduling for different controller dynamics

• Anti-windup for cascade controls

• Limiting controllers that are switched through to the controller output by a min/max selection

• Controller tracking when the controller is inactive for skip-free switching

Fuzzy logic controller:

• Uses fuzzy logic to control complex and non-linear systems

• Particularly useful when mathematical modeling of the system is difficult

• Frequently used in the chemical industry and in the control of combustion processes

In order to achieve optimal control performance and generate long-term added value, we also incorporate concepts from the field of fuzzy control in the sense of holistic APC solutions into the respective specific customer solutions, depending on the problem and objective.

Adaptive control

• continuously adjusts the control parameters to take account of changes in system behavior

• is suitable for processes in which the properties change over time

• is used in the automotive industry and in the control of bioprocesses