This work thoroughly compares the efficiency of Long Short-Term Memory Networks
(LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical …

Model Predictive Control (MPC) algorithms typically use the classical L 2 cost function,
which minimises squared differences of predicted control errors. Such an approach has …

In this paper, a pole-independent, single-input, multi-output explicit linear MPC controller is
proposed to stabilize the fourth-order cart–inverted-pendulum system around the desired …

This paper presents the process of designing, fabricating, assembling, programming and
optimizing a prototype nonlinear mechatronic Ball-Plate System (BPS) as a laboratory …

In the teaching process, it is important that students do not carry out exercises only by
computer simulations, but also that they carry out research in real time. In times of distance …

This work aims to develop a novel system, including software and hardware, to perform
independent control tasks in a genuine parallel manner. Currently, to control processes with …

Multi-drone cooperative transport (CT) problem has been widely studied in the literature.
However, limited work exists on control of such systems in the presence of time-varying …

We present a nonlinear non-convex model predictive control approach to solving a real-
world labyrinth game. We introduce adaptive nonlinear constraints, representing the non …

This work addresses the problem of controlling the ball-and-plate system using the model
reference adaptive control technique. This approach uses state feedback with adaptive …

Major achievements in control engineering, like the least squares estimation, minimum
variance, LQG (Linear Quadratic Gaussian), MPC (Model Predictive Control) or adaptive …