# Automated Controller Calibration by Kalman Filtering

arXiv: https://arxiv.org/abs/2111.10832 · HTML: https://mlsanity.com/paper/2111.10832/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 74 references that are in the map.
- First posted on arXiv 21 Nov 2021.

## Builds on (most cited first, 14 of 14)

- [Bayesian Optimization with Gradients](https://mlsanity.com/paper/1703.04389/) (2017)
- [Reach-SDP: Reachability Analysis of Closed-Loop Systems with Neural Network Controllers via Semidefinite Programming](https://mlsanity.com/paper/2004.07876/) (2020)
- [Learning Model Predictive Control for iterative tasks. A Data-Driven Control Framework](https://mlsanity.com/paper/1609.01387/) (2016)
- [Automatic LQR Tuning Based on Gaussian Process Global Optimization](https://mlsanity.com/paper/1605.01950/) (2016)
- [Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving](https://mlsanity.com/paper/1905.00229/) (2019)
- [Data-efficient Auto-tuning with Bayesian Optimization: An Industrial Control Study](https://mlsanity.com/paper/1812.06325/) (2018)
- [Probabilistic Verification and Reachability Analysis of Neural Networks via Semidefinite Programming](https://mlsanity.com/paper/1910.04249/) (2019)
- [Learning Constraints from Locally-Optimal Demonstrations under Cost Function Uncertainty](https://mlsanity.com/paper/2001.09336/) (2020)
- [Cautious Bayesian Optimization for Efficient and Scalable Policy Search](https://mlsanity.com/paper/2011.09445/) (2020)
- [Approximate Bayesian Smoothing with Unknown Process and Measurement Noise Covariances](https://mlsanity.com/paper/1412.5307/) (2014)
- [MPC Controller Tuning using Bayesian Optimization Techniques](https://mlsanity.com/paper/2009.14175/) (2020)
- [Maximum Likelihood Methods for Inverse Learning of Optimal Controllers](https://mlsanity.com/paper/2005.02767/) (2020)
- [A tuning algorithm for a sliding mode controller of buildings with ATMD](https://mlsanity.com/paper/2012.05966/) (2020)
- [Zeroth-order optimisation on subsets of symmetric matrices with application to MPC tuning](https://mlsanity.com/paper/2106.14359/) (2021)

## Built on by (most cited first, 3 of 3)

- [Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter](https://mlsanity.com/paper/2209.03210/) (2022)
- [Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin](https://mlsanity.com/paper/2402.16645/) (2024)
- [Automatic Parameter Tuning of Self-Driving Vehicles](https://mlsanity.com/paper/2406.17757/) (2024)

## Abstract (arXiv)

This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, filter coefficients, the sliding surface of a sliding mode controller, or weights of a neural network. Hence, the proposed method can be applied to a wide range of controllers. The method uses a Kalman filter that estimates control parameters, using data of closed-loop system operation. The control parameter calibration is driven by a training objective, which encompasses specifications on the performance of the dynamical system. The performance-driven calibration method tunes the parameters online and robustly, is computationally efficient, has low data storage requirements, and is easy to implement making it appealing for many real-time applications. Simulation results show that the method is able to learn control parameters quickly, is able to tune the parameters to compensate for disturbances, and is robust to noise. A simulation study with the high-fidelity vehicle simulator CarSim shows that the method can calibrate controllers of a complex dynamical system online, which indicates its applicability to a real-world system. We also verify the real-time feasibility on an embedded platform with automotive-grade processors by implementing our method on a dSPACE MicroAutoBox-II rapid prototyping unit.