On Controller Tuning with Time-Varying Bayesian Optimization

TL;DR

This paper introduces a novel time-varying Bayesian optimization method with a forgetting strategy and inequality constraints, enabling more stable and efficient online controller tuning in changing environments.

Contribution

It proposes a new TVBO approach with Uncertainty-Injection and convexity modeling, improving stability and performance over existing methods.

Findings

Outperforms state-of-the-art TVBO in numerical experiments.

Reduces regret and unstable configurations.

Models incremental changes with a Wiener process.

Abstract

Changing conditions or environments can cause system dynamics to vary over time. To ensure optimal control performance, controllers should adapt to these changes. When the underlying cause and time of change is unknown, we need to rely on online data for this adaptation. In this paper, we will use time-varying Bayesian optimization (TVBO) to tune controllers online in changing environments using appropriate prior knowledge on the control objective and its changes. Two properties are characteristic of many online controller tuning problems: First, they exhibit incremental and lasting changes in the objective due to changes to the system dynamics, e.g., through wear and tear. Second, the optimization problem is convex in the tuning parameters. Current TVBO methods do not explicitly account for these properties, resulting in poor tuning performance and many unstable controllers through over-exploration of the parameter space. We propose a novel TVBO forgetting strategy using Uncertainty-Injection (UI), which incorporates the assumption of incremental and lasting changes. The control objective is modeled as a spatio-temporal Gaussian process (GP) with UI through a Wiener process in the temporal domain. Further, we explicitly model the convexity assumptions in the spatial dimension through GP models with linear inequality constraints. In numerical experiments, we show that our model outperforms the state-of-the-art method in TVBO, exhibiting reduced regret and fewer unstable parameter configurations.