Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction

• URL: http://arxiv.org/abs/2209.03698v1
• Date: Thu, 8 Sep 2022 10:33:30 GMT
• Title: Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction
• Authors: Namhoon Cho, Hyo-Sang Shin, Antonios Tsourdos, Davide Amato
• Abstract summary: The proposed approach is to linearise the dynamics around the baseline values of its arguments. The online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control.
• Score: 6.729108277517129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study presents incremental correction methods for refining neural network parameters or control functions entering into a continuous-time dynamic system to achieve improved solution accuracy in satisfying the interim point constraints placed on the performance output variables. The proposed approach is to linearise the dynamics around the baseline values of its arguments, and then to solve for the corrective input required to transfer the perturbed trajectory to precisely known or desired values at specific time points, i.e., the interim points. Depending on the type of decision variables to adjust, parameter correction and control function correction methods are developed. These incremental correction methods can be utilised as a means to compensate for the prediction errors of pre-trained neural networks in real-time applications where high accuracy of the prediction of dynamical systems at prescribed time points is imperative. In this regard, the online update approach can be useful for enhancing overall targeting accuracy of finite-horizon control subject to point constraints using a neural policy. Numerical example demonstrates the effectiveness of the proposed approach in an application to a powered descent problem at Mars.

Related papers

• Continual Learning via Sequential Function-Space Variational Inference [65.96686740015902]
  We propose an objective derived by formulating continual learning as sequential function-space variational inference. Compared to objectives that directly regularize neural network predictions, the proposed objective allows for more flexible variational distributions. We demonstrate that, across a range of task sequences, neural networks trained via sequential function-space variational inference achieve better predictive accuracy than networks trained with related methods.
  arXiv  Detail & Related papers  (2023-12-28T18:44:32Z)
• Achieving Constraints in Neural Networks: A Stochastic Augmented Lagrangian Approach [49.1574468325115]
  Regularizing Deep Neural Networks (DNNs) is essential for improving generalizability and preventing overfitting. We propose a novel approach to DNN regularization by framing the training process as a constrained optimization problem. We employ the Augmented Lagrangian (SAL) method to achieve a more flexible and efficient regularization mechanism.
  arXiv  Detail & Related papers  (2023-10-25T13:55:35Z)
• Neural ODEs as Feedback Policies for Nonlinear Optimal Control [1.8514606155611764]
  We use Neural ordinary differential equations (Neural ODEs) to model continuous time dynamics as differential equations parametrized with neural networks. We propose the use of a neural control policy posed as a Neural ODE to solve general nonlinear optimal control problems.
  arXiv  Detail & Related papers  (2022-10-20T13:19:26Z)
• Adaptive Self-supervision Algorithms for Physics-informed Neural Networks [59.822151945132525]
  Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function. We study the impact of the location of the collocation points on the trainability of these models. We propose a novel adaptive collocation scheme which progressively allocates more collocation points to areas where the model is making higher errors.
  arXiv  Detail & Related papers  (2022-07-08T18:17:06Z)
• Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals [23.31865419578237]
  We extend the idea of automated debiased machine learning to the dynamic treatment regime and more generally to nested functionals. We show that the multiply robust formula for the dynamic treatment regime with discrete treatments can be re-stated in terms of a Riesz representer characterization of nested mean regressions.
  arXiv  Detail & Related papers  (2022-03-25T19:54:17Z)
• Neural Solvers for Fast and Accurate Numerical Optimal Control [12.80824586913772]
  This paper provides techniques to improve the quality of optimized control policies given a fixed computational budget. We achieve the above via a hypersolvers approach, which hybridizes a differential equation solver and a neural network.
  arXiv  Detail & Related papers  (2022-03-13T10:46:50Z)
• On the adaptation of recurrent neural networks for system identification [2.5234156040689237]
  This paper presents a transfer learning approach which enables fast and efficient adaptation of Recurrent Neural Network (RNN) models of dynamical systems. The system dynamics are then assumed to change, leading to an unacceptable degradation of the nominal model performance on the perturbed system. To cope with the mismatch, the model is augmented with an additive correction term trained on fresh data from the new dynamic regime.
  arXiv  Detail & Related papers  (2022-01-21T12:04:17Z)
• Automating Control of Overestimation Bias for Continuous Reinforcement Learning [65.63607016094305]
  We present a data-driven approach for guiding bias correction. We demonstrate its effectiveness on the Truncated Quantile Critics -- a state-of-the-art continuous control algorithm.
  arXiv  Detail & Related papers  (2021-10-26T09:27:12Z)
• Better Training using Weight-Constrained Stochastic Dynamics [0.0]
  We employ constraints to control the parameter space of deep neural networks throughout training. The use of customized, appropriately designed constraints can reduce the vanishing/exploding problem. We provide a general approach to efficiently incorporate constraints into a gradient Langevin framework.
  arXiv  Detail & Related papers  (2021-06-20T14:41:06Z)
• Adaptive Gradient Method with Resilience and Momentum [120.83046824742455]
  We propose an Adaptive Gradient Method with Resilience and Momentum (AdaRem) AdaRem adjusts the parameter-wise learning rate according to whether the direction of one parameter changes in the past is aligned with the direction of the current gradient. Our method outperforms previous adaptive learning rate-based algorithms in terms of the training speed and the test error.
  arXiv  Detail & Related papers  (2020-10-21T14:49:00Z)
• Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems [91.43582419264763]
  We study the problem of system identification and adaptive control in partially observable linear dynamical systems. We present the first model estimation method with finite-time guarantees in both open and closed-loop system identification. We show that AdaptOn is the first algorithm that achieves $textpolylogleft(Tright)$ regret in adaptive control of unknown partially observable linear dynamical systems.
  arXiv  Detail & Related papers  (2020-03-25T06:00:33Z)

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.