Deep Operator Neural Network Model Predictive Control

• URL: http://arxiv.org/abs/2505.18008v1
• Date: Fri, 23 May 2025 15:13:19 GMT
• Title: Deep Operator Neural Network Model Predictive Control
• Authors: Thomas Oliver de Jong, Khemraj Shukla, Mircea Lazar,
• Abstract summary: We consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets)<n>We introduce a multi step DeepONet architecture that computes in one shot multi step predictions of system outputs from multi step input sequences.<n>We prove that the MS DeepONet is a universal approximator in terms of multi step sequence prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets). These neural networks are capable of accurately approximating real and complex valued solutions of continuous time nonlinear systems without relying on recurrent architectures. The DeepONet architecture is made up of two feedforward neural networks: the branch network, which encodes the input function space, and the trunk network, which represents dependencies on temporal variables or initial conditions. Utilizing the original DeepONet architecture as a predictor within MPC for Multi Input Multi Output (MIMO) systems requires multiple branch networks, to generate multi output predictions, one for each input. Moreover, to predict multiple time steps into the future, the network has to be evaluated multiple times. Motivated by this, we introduce a multi step DeepONet (MS-DeepONet) architecture that computes in one shot multi step predictions of system outputs from multi step input sequences, which is better suited for MPC. We prove that the MS DeepONet is a universal approximator in terms of multi step sequence prediction. Additionally, we develop automated hyper parameter selection strategies and implement MPC frameworks using both the standard DeepONet and the proposed MS DeepONet architectures in PyTorch. The implementation is publicly available on GitHub. Simulation results demonstrate that MS-DeepONet consistently outperforms the standard DeepONet in learning and predictive control tasks across several nonlinear benchmark systems: the van der Pol oscillator, the quadruple tank process, and a cart pendulum unstable system, where it successfully learns and executes multiple swing up and stabilization policies.

Related papers

• A Wireless Foundation Model for Multi-Task Prediction [50.21098141769079]
  We propose a unified foundation model for multi-task prediction in wireless networks that supports diverse prediction intervals.<n>After trained on large-scale datasets, the proposed foundation model demonstrates strong generalization to unseen scenarios and zero-shot performance on new tasks.
  arXiv  Detail & Related papers  (2025-07-08T12:37:55Z)
• DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift [56.04579258267126]
  This paper investigates maximizing the throughput of millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems with obstructed direct communication paths.<n>A reconfigurable intelligent surface (RIS) is employed to enhance transmissions, considering mmWave characteristics related to line-of-sight (LoS) and multipath effects.<n>Deep neural network (DNN) is developed to facilitate faster codeword selection.
  arXiv  Detail & Related papers  (2025-07-03T17:35:06Z)
• Multi-QuAD: Multi-Level Quality-Adaptive Dynamic Network for Reliable Multimodal Classification [57.08108545219043]
  Existing reliable multimodal classification methods fail to provide robust estimation of data quality.<n>New framework for reliable classification termed textitMulti-level Quality-Adaptive Dynamic multimodal network (Multi-QuAD) is proposed.<n>Experiments conducted on four datasets demonstrate that Multi-QuAD significantly outperforms state-of-the-art methods in classification performance and reliability.
  arXiv  Detail & Related papers  (2024-12-19T03:26:51Z)
• Deep Recurrent Stochastic Configuration Networks for Modelling Nonlinear Dynamic Systems [3.8719670789415925]
  This paper proposes a novel deep reservoir computing framework, termed deep recurrent configuration network (DeepRSCN) DeepRSCNs are incrementally constructed, with all reservoir nodes directly linked to the final output. Given a set of training samples, DeepRSCNs can quickly generate learning representations, which consist of random basis functions with cascaded input readout weights.
  arXiv  Detail & Related papers  (2024-10-28T10:33:15Z)
• SimQ-NAS: Simultaneous Quantization Policy and Neural Architecture Search [6.121126813817338]
  Recent one-shot Neural Architecture Search algorithms rely on training a hardware-agnostic super-network tailored to a specific task and then extracting efficient sub-networks for different hardware platforms. We show that by using multi-objective search algorithms paired with lightly trained predictors, we can efficiently search for both the sub-network architecture and the corresponding quantization policy.
  arXiv  Detail & Related papers  (2023-12-19T22:08:49Z)
• Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications [8.434224141580758]
  Split-Et-Impera determines the set of the best-split points of a neural network based on deep network interpretability principles. It performs a communication-aware simulation for the rapid evaluation of different neural network rearrangements. It suggests the best match between the quality of service requirements of the application and the performance in terms of accuracy and latency time.
  arXiv  Detail & Related papers  (2023-03-22T13:00:00Z)
• Learning to Estimate RIS-Aided mmWave Channels [50.15279409856091]
  We focus on uplink cascaded channel estimation, where known and fixed base station combining and RIS phase control matrices are considered for collecting observations. To boost the estimation performance and reduce the training overhead, the inherent channel sparsity of mmWave channels is leveraged in the deep unfolding method. It is verified that the proposed deep unfolding network architecture can outperform the least squares (LS) method with a relatively smaller training overhead and online computational complexity.
  arXiv  Detail & Related papers  (2021-07-27T06:57:56Z)
• Joint Deep Reinforcement Learning and Unfolding: Beam Selection and Precoding for mmWave Multiuser MIMO with Lens Arrays [54.43962058166702]
  millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) systems with discrete lens arrays have received great attention. In this work, we investigate the joint design of a beam precoding matrix for mmWave MU-MIMO systems with DLA.
  arXiv  Detail & Related papers  (2021-01-05T03:55:04Z)
• Hybrid Backpropagation Parallel Reservoir Networks [8.944918753413827]
  We propose a novel hybrid network, which combines the effectiveness of learning random temporal features of reservoirs with the readout power of a deep neural network with batch normalization. We demonstrate that our new network outperforms LSTMs and GRUs, including multi-layer "deep" versions of these networks. We show also that the inclusion of a novel meta-ring structure, which we call HBP-ESN M-Ring, achieves similar performance to one large reservoir while decreasing the memory required by an order of magnitude.
  arXiv  Detail & Related papers  (2020-10-27T21:03:35Z)
• Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis [12.386788662621338]
  Multimodal sentiment analysis utilizes multiple heterogeneous modalities for sentiment classification. Recent multimodal fusion schemes customize LSTMs to discover intra-modal dynamics. We propose a common network to discover both intra-modal and inter-modal dynamics.
  arXiv  Detail & Related papers  (2020-10-16T08:02:11Z)
• Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems [59.804810122136345]
  We propose a framework for deep-unfolding, where a general form of iterative algorithm induced deep-unfolding neural network (IAIDNN) is developed. An efficient IAIDNN based on the structure of the classic weighted minimum mean-square error (WMMSE) iterative algorithm is developed. We show that the proposed IAIDNN efficiently achieves the performance of the iterative WMMSE algorithm with reduced computational complexity.
  arXiv  Detail & Related papers  (2020-06-15T02:57:57Z)
• Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks [50.42141893913188]
  We study a distributed variable for large-scale AUC for a neural network as with a deep neural network. Our model requires a much less number of communication rounds and still a number of communication rounds in theory. Our experiments on several datasets show the effectiveness of our theory and also confirm our theory.
  arXiv  Detail & Related papers  (2020-05-05T18:08:23Z)

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.