Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers
This work addresses the tedious tuning of PID controllers in industrial applications, offering an automated solution that is incremental as it builds on an existing framework.
The paper tackled the problem of automatically tuning multivariate PID controllers, which is tedious in practice, by extending the PILCO model-based policy search framework to tune controllers based solely on observed data, and demonstrated its effectiveness by balancing an inverted pendulum on a seven degree-of-freedom robotic arm with fast and data-efficient learning.
PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended appropriately to frame the PID policy as a static state feedback policy. This renders PID tuning possible as the solution of a finite horizon optimal control problem without further a priori knowledge. The framework is applied to the task of balancing an inverted pendulum on a seven degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast and data-efficient policy learning, even on complex real world problems.