Uncertainty propagation in nonlinear dynamical systems is a field of study that explores how measurement inaccuracies, initial condition errors, and model approximations evolve within inherently ...

We often encounter nonlinear dynamical systems that behave unpredictably, such as the earth's climate and the stock market. To analyze them, measurements taken over time are used to reconstruct the ...

Engineers have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on sensor time series. The proposed technique involves feeding ...

This study presents a newly engineered nonlinear stiffness-softening mechanism that enables micro-electro-mechanical systems (MEMS) accelerometers to operate with dramatically reduced bias force and ...

As a follow-on course to "Linear Kalman Filter Deep Dive", this course derives the steps of the extended Kalman filter and the sigma-point Kalman filter for estimating the state of nonlinear dynamic ...

Example-oriented survey of nonlinear dynamical systems, including chaos. Combines numerical exploration of differential equations describing physical problems with analytic methods and geometric ...

We have considerable expertise in MPC as a powerful tool for providing optimal control in dynamic environments, ensuring real-time performance and adaptability. Our work includes developing predictive ...