Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

• URL: http://arxiv.org/abs/2101.07825v2
• Date: Tue, 2 Mar 2021 13:26:34 GMT
• Title: Safe and Efficient Model-free Adaptive Control via Bayesian Optimization
• Authors: Christopher K\"onig, Matteo Turchetta, John Lygeros, Alisa Rupenyan, Andreas Krause
• Abstract summary: We propose a purely data-driven, model-free approach for adaptive control. tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation.
• Score: 39.962395119933596
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive control approaches yield high-performance controllers when a precise system model or suitable parametrizations of the controller are available. Existing data-driven approaches for adaptive control mostly augment standard model-based methods with additional information about uncertainties in the dynamics or about disturbances. In this work, we propose a purely data-driven, model-free approach for adaptive control. Tuning low-level controllers based solely on system data raises concerns on the underlying algorithm safety and computational performance. Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization. We introduce several computational and algorithmic modifications in GoOSE that enable its practical use on a rotational motion system. We numerically demonstrate for several types of disturbances that our approach is sample efficient, outperforms constrained Bayesian optimization in terms of safety, and achieves the performance optima computed by grid evaluation. We further demonstrate the proposed adaptive control approach experimentally on a rotational motion system.

Related papers

• Safe Bayesian Optimization for Complex Control Systems via Additive Gaussian Processes [2.1757909866792313]
  We propose SafeCtrlBO to optimize multiple controllers simultaneously and safely. We simplify the exploration process in safe Bayesian optimization, reducing computational effort without sacrificing expansion capability.
  arXiv  Detail & Related papers  (2024-08-29T07:12:37Z)
• Traffic signal optimization in large-scale urban road networks: an adaptive-predictive controller using Ising models [4.408586742026574]
  We propose a control method called AMPIC that guarantees both scalability and optimality. The proposed method employs model predictive control to solve an optimal control problem at each control interval with explicit consideration of a predictive model of vehicle flow. Results show that AMPIC enables faster vehicle cruising speed with less waiting time than that achieved by classical control methods.
  arXiv  Detail & Related papers  (2024-06-06T02:20:34Z)
• Adaptive Bayesian Optimization for High-Precision Motion Systems [2.073673208115137]
  We propose a real-time purely data-driven, model-free approach for adaptive control, by online tuning low-level controller parameters. We base our algorithm on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization. We evaluate the algorithm's performance on a real precision-motion system utilized in semiconductor industry applications.
  arXiv  Detail & Related papers  (2024-04-22T21:58:23Z)
• Tuning Legged Locomotion Controllers via Safe Bayesian Optimization [47.87675010450171]
  This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. We leverage a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulation and the real system.
  arXiv  Detail & Related papers  (2023-06-12T13:10:14Z)
• Adaptive Model Predictive Control by Learning Classifiers [26.052368583196426]
  We propose an adaptive MPC variant that automatically estimates control and model parameters. We leverage recent results showing that BO can be formulated as a density ratio estimation. This is then integrated into a model predictive path integral control framework yielding robust controllers for a variety of challenging robotics tasks.
  arXiv  Detail & Related papers  (2022-03-13T23:22:12Z)
• Learning Robust Output Control Barrier Functions from Safe Expert Demonstrations [50.37808220291108]
  This paper addresses learning safe output feedback control laws from partial observations of expert demonstrations. We first propose robust output control barrier functions (ROCBFs) as a means to guarantee safety. We then formulate an optimization problem to learn ROCBFs from expert demonstrations that exhibit safe system behavior.
  arXiv  Detail & Related papers  (2021-11-18T23:21:00Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)
• Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems [81.7983463275447]
  Learning-based control algorithms require data collection with abundant supervision for training. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained optimal control with dynamics learning and feedback control.
  arXiv  Detail & Related papers  (2020-05-09T05:57:43Z)
• Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting [91.43582419264763]
  We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty. LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
  arXiv  Detail & Related papers  (2020-03-12T19:56:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.