... a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles. Fig. 1 Constrained sliding block exemplar with robot Constrained sliding block exemplar with robot Fig. 2 Diagram of constrained sliding block exemplar Diagram...

... to tackle this challenge. By leveraging artificial neural networks, an LPV state-space representation of the system dynamics is first learned. The mismatch between this model and real plant is then estimated using Bayesian neural networks, enabling scenario generation for ScMPC design. Soft constraints...

...Yejiang Yang; Zihao Mo; Weiming Xiang This article proposes a neural network hybrid modeling framework for dynamics learning to promote an interpretable, computationally efficient method of dynamics learning and system identification. First, a low-level model is trained to learn the system dynamics...

... training datasets that may not always be accessible in practical applications. To address this issue, this work proposes a hybrid model consisting of a transformer neural network and a single particle model with electrolyte dynamics (SPMe) for SOC estimation in limited data scenarios. The transformer can...

... methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online implementation challenging. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network. The neural network learns a full-route...

... layer consists of 50 neurons with ReLU activation functions. All layers are fully connected. Fig. 2 Neural network architectures: ( a ) actor network architecture and ( b ) critic network architecture Neural network architectures: (a) actor network architecture and (b) critic network...

...@villanova.edu Email: garrett.clayton@villanova.edu Email: c.nataraj@villanova.edu 18 04 2021 01 02 2022 13 02 2022 22 03 2022 machine learning neural networks This article presents a classification tool to assist an explosive ordnance disposal (EOD) technician...

... estimation machine learning neural networks pattern recognition and classification uncertain systems Deep neural networks are gaining popularity due to their ability to learn representations of data with multiple levels of abstraction [ 1 ]. In Ref. [ 2 ], researchers used deep learning to detect...

...Hailin Ren; Jingyuan Qi; Pinhas Ben-Tzvi This paper presents a method to imitate flatness-based controllers for mobile robots using neural networks. Sample case studies for a unicycle mobile robot and an unmanned aerial vehicle (UAV) quadcopter are presented. The goals of this paper are to (1...