  Robust and performant controllers are essential for industrial applications.
However, deriving controller parameters for complex and nonlinear systems is
challenging and time-consuming. To facilitate automatic controller
parametrization, this work presents a novel approach using deep reinforcement
learning (DRL) with N-dimensional B-spline geometries (BSGs). We focus on the
control of parameter-variant systems, a class of systems with complex behavior
which depends on the operating conditions. For this system class,
gain-scheduling control structures are widely used in applications across
industries due to well-known design principles. Facilitating the expensive
controller parametrization task regarding these control structures, we deploy
an DRL agent. Based on control system observations, the agent autonomously
decides how to adapt the controller parameters. We make the adaptation process
more efficient by introducing BSGs to map the controller parameters which may
depend on numerous operating conditions. To preprocess time-series data and
extract a fixed-length feature vector, we use a long short-term memory (LSTM)
neural networks. Furthermore, this work contributes actor regularizations that
are relevant to real-world environments which differ from training.
Accordingly, we apply dropout layer normalization to the actor and critic
networks of the truncated quantile critic (TQC) algorithm. To show our
approach's working principle and effectiveness, we train and evaluate the DRL
agent on the parametrization task of an industrial control structure with
parameter lookup tables.
