# Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

## TL;DR

This paper introduces a model-based policy search method that automatically tunes multivariate PID controllers using data, enabling efficient and effective control of complex systems like robotic arms balancing an inverted pendulum.

## Contribution

It extends the PILCO framework to optimize multivariate PID controllers directly from data without prior knowledge, simplifying the tuning process for complex, coupled controllers.

## Key findings

- Demonstrated fast, data-efficient policy learning on a robotic arm balancing an inverted pendulum.
- Showed the method's effectiveness in tuning multivariate PID controllers automatically.
- Validated the approach on a complex real-world control task.

## Abstract

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.