Automated Controller Calibration by Kalman Filtering

TL;DR

This paper introduces a real-time, robust, and efficient Kalman filter-based method for calibrating various control parameters online, demonstrated through simulations and automotive hardware implementation.

Contribution

It presents a versatile, performance-driven calibration approach applicable to diverse controllers, capable of online tuning in complex dynamical systems.

Findings

The method quickly learns control parameters.

It compensates for disturbances and noise.

It is feasible on automotive-grade embedded hardware.

Abstract

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.