Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …

Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …

We present differentiable predictive control (DPC) as a deep learning-based alternative to
the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC …

A new Wiener-type dynamic neural network (DNN) approach for nonlinear device modeling
is proposed in this paper. The proposed analytical formulation of Wiener-type DNN structure …

Neural network modules conditioned by known priors can be effectively trained and
combined to represent systems with nonlinear dynamics. This work explores a novel …

One of the difficulties in practical implementations of the classic Real-Time Optimization
(RTO) strategy is the integration between optimization and control layers, mainly due to the …

In real industrial processes, new process “excitation” patterns that largely deviate from
previously collected training data will appear due to disturbances caused by process inputs …

The control reliability of model predictive control is largely determined by the accuracy of the
process model. The Hammerstein–Wiener (HW) model is an important nonlinear process …

We present an alternative to model predictive control (MPC) for unknown nonlinear systems
in low-resource embedded device settings. The structure of the presented datadriven control …

This paper presents a head motion detection and recognition study using a smart helmet for
motorcycle rider which can potential be used for the analysis of behavior of motorcycle …