Model predictive control

Quantum mechanics

Algorithm

Control (management)

Engineering

Quantization (signal processing)

Control engineering

Digital control

Weighting

Power (physics)

Computer science

Radiology

Physics

Electronic engineering

Artificial intelligence

Medicine

Control theory (sociology)

Artificial neural network

Converters

IEEE Transactions on Industrial Electronics

In this article a finite control set model predictive control, is used to track a current reference in a power converter connected to an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$RL$</tex-math></inline-formula> load. An artificial intelligence approach is presented for real-time determination of the weighting factor that regulates the average switching frequency, independently of the operating point. The article focuses on the design, training, and digital implementation of an artificial neural network (ANN) that can be developed in a low-cost control platform. It is presented a sampling and offline ANN training procedure, together with a low-cost hardware implementation based on integer quantization of the ANN. The abovementioned approach provides a standalone application, serving as a framework for the development of ANN applications for power-converters. The main advantage of the presented approach is that the ANN inference is executed in real time. In this way, the weighting factor is automatically updated in real-time, allowing the system to quickly adapt to any reference step changes, and consequently provide the desired behavior. Executing the setup in laboratory prototype confirmed the theoretical analysis and successful tracking of the reference frequency.

An Artificial Intelligence Approach for Real-Time Tuning of Weighting Factors in FCS-MPC for Power Converters