Controller Adaptation via Learning Solutions of Contextual Bayesian Optimization
CoRR(2024)
摘要
In this letter, we propose a framework for adapting the controller's
parameters based on learning optimal solutions from contextual black-box
optimization problems. We consider a class of control design problems for
dynamical systems operating in different environments or conditions represented
by contextual parameters. The overarching goal is to identify the controller
parameters that maximize the controlled system's performance, given different
realizations of the contextual parameters. We formulate a contextual Bayesian
optimization problem in which the solution is actively learned using Gaussian
processes to approximate the controller adaptation strategy. We demonstrate the
efficacy of the proposed framework with a simulation-to-real example. We learn
the optimal weighting strategy of a model predictive control for connected and
automated vehicles interacting with human-driven vehicles from simulations and
then deploy it in a real-time experiment.
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