Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty

• URL: http://arxiv.org/abs/2203.00551v2
• Date: Wed, 2 Mar 2022 21:29:41 GMT
• Title: Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
• Authors: Rel Guzman, Rafael Oliveira, Fabio Ramos
• Abstract summary: We propose an adaptive optimisation approach for tuning model predictive control (MPC) hyper- parameters. We develop a Bayesian optimisation (BO) algorithm with a heteroscedastic noise model to deal with varying noise. Experimental results demonstrate that our approach leads to higher cumulative rewards and more stable controllers.
• Score: 26.052368583196426
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performance rewards. In particular, we develop a Bayesian optimisation (BO) algorithm with a heteroscedastic noise model to deal with varying noise across the MPC hyper-parameter and dynamics model parameter spaces. Typical homoscedastic noise models are unrealistic for tuning MPC since stochastic controllers are inherently noisy, and the level of noise is affected by their hyper-parameter settings. We evaluate the proposed optimisation algorithm in simulated control and robotics tasks where we jointly infer control and dynamics parameters. Experimental results demonstrate that our approach leads to higher cumulative rewards and more stable controllers.

Related papers

• Scaling Exponents Across Parameterizations and Optimizers [94.54718325264218]
  We propose a new perspective on parameterization by investigating a key assumption in prior work. Our empirical investigation includes tens of thousands of models trained with all combinations of threes. We find that the best learning rate scaling prescription would often have been excluded by the assumptions in prior work.
  arXiv  Detail & Related papers  (2024-07-08T12:32:51Z)
• Variational Inference of Parameters in Opinion Dynamics Models [9.51311391391997]
  This work uses variational inference to estimate the parameters of an opinion dynamics ABM. We transform the inference process into an optimization problem suitable for automatic differentiation. Our approach estimates both macroscopic (bounded confidence intervals and backfire thresholds) and microscopic ($200$ categorical, agent-level roles) more accurately than simulation-based and MCMC methods.
  arXiv  Detail & Related papers  (2024-03-08T14:45:18Z)
• Improve Noise Tolerance of Robust Loss via Noise-Awareness [60.34670515595074]
  We propose a meta-learning method which is capable of adaptively learning a hyper parameter prediction function, called Noise-Aware-Robust-Loss-Adjuster (NARL-Adjuster for brevity) Four SOTA robust loss functions are attempted to be integrated with our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its noise tolerance and performance.
  arXiv  Detail & Related papers  (2023-01-18T04:54:58Z)
• On the Effectiveness of Parameter-Efficient Fine-Tuning [79.6302606855302]
  Currently, many research works propose to only fine-tune a small portion of the parameters while keeping most of the parameters shared across different tasks. We show that all of the methods are actually sparse fine-tuned models and conduct a novel theoretical analysis of them. Despite the effectiveness of sparsity grounded by our theory, it still remains an open problem of how to choose the tunable parameters.
  arXiv  Detail & Related papers  (2022-11-28T17:41:48Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Optimization of Annealed Importance Sampling Hyperparameters [77.34726150561087]
  Annealed Importance Sampling (AIS) is a popular algorithm used to estimates the intractable marginal likelihood of deep generative models. We present a parameteric AIS process with flexible intermediary distributions and optimize the bridging distributions to use fewer number of steps for sampling. We assess the performance of our optimized AIS for marginal likelihood estimation of deep generative models and compare it to other estimators.
  arXiv  Detail & Related papers  (2022-09-27T07:58:25Z)
• 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)
• 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)
• Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control [23.180330602334223]
  Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. We investigate fine-tuning MPC methods in the context of MPC, which presents extra challenges due to the randomness of the controller's actions.
  arXiv  Detail & Related papers  (2020-10-01T05:31:41Z)
• Accurate Parameter Estimation for Risk-aware Autonomous Systems [0.0]
  This paper addresses the use of a spectral lines-based approach for estimating parameters of the dynamic model of an autonomous system. Existing literature has treated all unmodeled components of the dynamic system as sub-Gaussian noise. We show that the proposed approach can ensure a $tildeO(sqrtT)$ regret, matching existing literature.
  arXiv  Detail & Related papers  (2020-06-23T01:20:44Z)
• Online Parameter Estimation for Safety-Critical Systems with Gaussian Processes [6.122161391301866]
  We present a Bayesian optimization framework based on Gaussian processes (GPs) for online parameter estimation. It uses an efficient search strategy over a response surface in the parameter space for finding the global optima with minimal function evaluations. We demonstrate our technique on an actuated planar pendulum and safety-critical quadrotor in simulation with changing parameters.
  arXiv  Detail & Related papers  (2020-02-18T20:38:00Z)

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.