A Sequential Learning Algorithm for Probabilistically Robust Controller Tuning

TL;DR

This paper presents a sequential learning algorithm for tuning robust controllers that probabilistically satisfy performance constraints, leveraging statistical correlations and black-box sampling, validated on diesel engine air-path control.

Contribution

Introduces a novel sequential learning algorithm for probabilistic controller tuning that exploits black-box sampling and provides theoretical guarantees and computational bounds.

Findings

Successfully tuned a model predictive controller for diesel engine air-path.

Achieved probabilistic performance guarantees with sample complexity comparable to existing methods.

Demonstrated effectiveness across a fleet of vehicles with plant uncertainties.

Abstract

We introduce a sequential learning algorithm to address a robust controller tuning problem, which in effect, finds (with high probability) a candidate solution satisfying the internal performance constraint to a chance-constrained program which has black-box functions. The algorithm leverages ideas from the areas of randomised algorithms and ordinal optimisation, and also draws comparisons with the scenario approach; these have all been previously applied to finding approximate solutions for difficult design problems. By exploiting statistical correlations through black-box sampling, we formally prove that our algorithm yields a controller meeting the prescribed probabilistic performance specification. Additionally, we characterise the computational requirement of the algorithm with a probabilistic lower bound on the algorithm's stopping time. To validate our work, the algorithm is then demonstrated for tuning model predictive controllers on a diesel engine air-path across a fleet of vehicles. The algorithm successfully tuned a single controller to meet a desired tracking error performance, even in the presence of the plant uncertainty inherent across the fleet. Moreover, the algorithm was shown to exhibit a sample complexity comparable to the scenario approach.