# Performance-based Trajectory Optimization for Path Following Control Using Bayesian Optimization

arXiv: https://arxiv.org/abs/2103.15416 · HTML: https://mlsanity.com/paper/2103.15416/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2021.

## Built on by (most cited first, 8 of 8)

- [Meta-Learning Priors for Safe Bayesian Optimization](https://mlsanity.com/paper/2210.00762/) (2022)
- [A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control](https://mlsanity.com/paper/2402.02624/) (2024)
- [Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints](https://mlsanity.com/paper/2304.06104/) (2023)
- [Adaptive Bayesian Optimization for High-Precision Motion Systems](https://mlsanity.com/paper/2404.14602/) (2024)
- [Bayesian Optimization for Automatic Tuning of Torque-Level Nonlinear Model Predictive Control](https://mlsanity.com/paper/2512.03772/) (2025)
- [Iterative Tuning of Nonlinear Model Predictive Control for Robotic Manufacturing Tasks](https://mlsanity.com/paper/2512.13170/) (2025)
- [Accelerating Reinforcement Learning via MPC Solver-Gradient Guidance for Weights-varying MPC](https://mlsanity.com/paper/2609.01061/) (2026)
- [Exploiting Hierarchical Controller Structure in Contextual Parameter Learning for Humanoid Loco-Manipulation](https://mlsanity.com/paper/2610.04609/) (2026)

## Abstract (arXiv)

Accurate positioning and fast traversal times determine the productivity in machining applications. This paper demonstrates a hierarchical contour control implementation for the increase of productivity in positioning systems. The high-level controller pre-optimizes the input to a low-level cascade controller, using a contouring predictive control approach. This control structure requires tuning of multiple parameters. We propose a sample-efficient tuning algorithm, where the performance metrics associated with the full geometry traversal are modelled as Gaussian processes and used to form the global cost and the constraints in a constrained Bayesian optimization algorithm. This approach enables the trade-off between fast traversal, high tracking accuracy, and suppression of vibrations in the system. The performance improvement is evaluated numerically when tuning different combinations of parameters. We demonstrate that tuning the parameters of the MPC contour-controller achieves the best performance in terms of time, tracking accuracy, and minimization of the vibrations in the system.