On Controller Tuning with Time-Varying Bayesian Optimization

  title={On Controller Tuning with Time-Varying Bayesian Optimization},
  author={Paul Brunzema and Alexander von Rohr and Sebastian Trimpe},
—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… 

Figures and Tables from this paper

Improving the Performance of Robust Control through Event-Triggered Learning

This work proposes an event-triggered learning algorithm that decides when to learn in the face of uncertainty in the LQR problem with rare or slow changes, and designs a statistical test for uncertain systems based on the moment-generating function of the L QR cost.

Benchmark of Bayesian Optimization and Metaheuristics for Control Engineering Tuning Problems with Crash Constraints

—Controller tuning based on black-box optimization allows to automatically tune performance-critical parameters w.r.t. mostly arbitrary high-level closed-loop control objectives. However, a

Event-Triggered Time-Varying Bayesian Optimization

This work proposes an event-triggered algorithm, ET-GP-UCB, that detects changes in the objective function online and is competitive with state-of-the-art algorithms even though it requires no knowledge about the temporal changes.



Autonomous Vehicle Control Through the Dynamics and Controller Learning

This paper provides a novel algorithm, named as time-varying controller optimization, which takes both of the dynamic model uncertainty and the controller parameters uncertainty into account, and tunes them to find a global optimal choice for minimizing the time-Varying control costs.

Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

This work proposes a purely data-driven, model-free approach for adaptive control that builds on GOOSE, an algorithm for safe and sample-efficient Bayesian optimization, and numerically demonstrates that this approach is sample efficient, outperforms constrained Bayesian optimize in terms of safety, and achieves the performance optima computed by grid evaluation.

Data-Efficient Autotuning With Bayesian Optimization: An Industrial Control Study

The proposed autotuning framework is flexible and can handle different control structures and objectives and consistently achieves better performance with a low number of experiments.

On the design of LQR kernels for efficient controller learning

This work constructs two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning.

Automatic LQR tuning based on Gaussian process global optimization

An automatic controller tuning framework based on linear optimal control combined with Bayesian optimization that shall yield improved controllers with fewer evaluations compared to alternative approaches is proposed.

Safe Bayesian Optimisation for Controller Design by Utilising the Parameter Space Approach

Analysis knowledge about robustly stable parameter configurations is gained by the parameter space approach and then incorporated within the optimisation as constraint and results show a significant performance gain compared to standard approaches.

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics

A generalized algorithm that allows for multiple safety constraints separate from the objective is presented, which enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.

Local policy search with Bayesian optimization

An algorithm utilizing a probabilistic model of the objective function and its gradient and based on the model, the algorithm decides where to query a noisy zeroth-order oracle to improve the gradient estimates, which reveals improved sample complexity and reduced variance in extensive empirical evaluations on synthetic objectives.

Bayesian optimization for learning gaits under uncertainty

Bayesian optimization, a model-based approach to black-box optimization under uncertainty, is evaluated on both simulated problems and real robots, demonstrating that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments.

Time-Varying Convex Optimization: Time-Structured Algorithms and Applications

A broad class of state-of-the-art algorithms for time-varying optimization is reviewed, with an eye to performing both algorithmic development and performance analysis, to exemplify wide engineering relevance of analytical tools and pertinent theoretical foundations.