Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty

• URL: http://arxiv.org/abs/2104.08261v1
• Date: Fri, 16 Apr 2021 17:47:02 GMT
• Title: Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
• Authors: Rohan Sinha, James Harrison, Spencer M. Richards, Marco Pavone
• Abstract summary: We propose a learning-based robust predictive control algorithm that can handle large uncertainty in the dynamics for a class of discrete-time systems. Motivated by an inability of existing learning-based predictive control algorithms to achieve safety guarantees in the presence of uncertainties of large magnitude, we achieve significant performance improvements.
• Score: 28.10549712956161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a learning-based robust predictive control algorithm that can handle large uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear dynamics component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal system. Motivated by an inability of existing learning-based predictive control algorithms to achieve safety guarantees in the presence of uncertainties of large magnitude in this setting, we achieve significant performance improvements by optimizing over a novel class of nonlinear feedback policies inspired by certainty equivalent "estimate-and-cancel" control laws pioneered in classical adaptive control. In contrast with previous work in robust adaptive MPC, this allows us to take advantage of the structure in the a priori unknown dynamics that are learned online through function approximation. Our approach also extends typical nonlinear adaptive control methods to systems with state and input constraints even when an additive uncertain function cannot directly be canceled from the dynamics. Moreover, our approach allows us to apply contemporary statistical estimation techniques to certify the safety of the system through persistent constraint satisfaction with high probability. We show that our method allows us to consider larger unknown terms in the dynamics than existing methods through simulated examples.

Related papers

• Learning to Boost the Performance of Stable Nonlinear Systems [0.0]
  We tackle the performance-boosting problem with closed-loop stability guarantees. Our methods enable learning over arbitrarily deep neural network classes of performance-boosting controllers for stable nonlinear systems.
  arXiv  Detail & Related papers  (2024-05-01T21:11:29Z)
• Online Nonstochastic Model-Free Reinforcement Learning [35.377261344335736]
  We investigate robust model robustness guarantees for environments that may be dynamic or adversarial. We provide efficient and efficient algorithms for optimizing these policies. These are the best-known developments in having no dependence on the state-space dimension in having no dependence on the state-space.
  arXiv  Detail & Related papers  (2023-05-27T19:02:55Z)
• Statistical Safety and Robustness Guarantees for Feedback Motion Planning of Unknown Underactuated Stochastic Systems [1.0323063834827415]
  We propose a sampling-based planner that uses the mean dynamics model and simultaneously bounds the closed-loop tracking error via a learned disturbance bound. We validate that our guarantees translate to empirical safety in simulation on a 10D quadrotor, and in the real world on a physical CrazyFlie quadrotor and Clearpath Jackal robot.
  arXiv  Detail & Related papers  (2022-12-13T19:38:39Z)
• Adaptive Robust Model Predictive Control via Uncertainty Cancellation [25.736296938185074]
  We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics. We optimize over a class of nonlinear feedback policies inspired by certainty equivalent "estimate-and-cancel" control laws.
  arXiv  Detail & Related papers  (2022-12-02T18:54:23Z)
• Sparsity in Partially Controllable Linear Systems [56.142264865866636]
  We study partially controllable linear dynamical systems specified by an underlying sparsity pattern. Our results characterize those state variables which are irrelevant for optimal control.
  arXiv  Detail & Related papers  (2021-10-12T16:41:47Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Closing the Closed-Loop Distribution Shift in Safe Imitation Learning [80.05727171757454]
  We treat safe optimization-based control strategies as experts in an imitation learning problem. We train a learned policy that can be cheaply evaluated at run-time and that provably satisfies the same safety guarantees as the expert.
  arXiv  Detail & Related papers  (2021-02-18T05:11:41Z)
• Improper Learning with Gradient-based Policy Optimization [62.50997487685586]
  We consider an improper reinforcement learning setting where the learner is given M base controllers for an unknown Markov Decision Process. We propose a gradient-based approach that operates over a class of improper mixtures of the controllers.
  arXiv  Detail & Related papers  (2021-02-16T14:53:55Z)
• The Impact of Data on the Stability of Learning-Based Control- Extended Version [63.97366815968177]
  We propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance. By modeling unknown system dynamics through Gaussian processes, we can determine the interrelation between model uncertainty and satisfaction of stability conditions.
  arXiv  Detail & Related papers  (2020-11-20T19:10:01Z)
• Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees [1.1086440815804224]
  We present differentiable predictive control (DPC), a method for learning constrained neural control policies for linear systems. We employ automatic differentiation to obtain direct policy gradients by backpropagating the model predictive control (MPC) loss function and constraints penalties through a differentiable closed-loop system dynamics model.
  arXiv  Detail & Related papers  (2020-04-23T14:24:44Z)
• 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.