Recently, model predictive control (MPC) is increasingly applied to path tracking of mobile
devices, such as mobile robots. The characteristics of these MPC-based controllers are not …

The control problem was formulated in the previous chapters considering all signals to
possess an unlimited range. This is not very realistic because in practice all processes are …

An evolutionary algorithm based framework, a combination of modified breeder genetic
algorithms incorporating characteristics of classic genetic algorithms, is utilized to design an …

In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …

A neural network controller is applied to the optimal model predictive control of constrained
nonlinear systems. The control law is represented by a neural network function …

Model Predictive Control (MPC) is an optimal control algorithm with strong stability and
robustness guarantees. Despite its popularity in robotics and industrial applications, the …

Physics-informed neural networks (PINNs) incorporate established physical principles into
the training of deep neural networks, ensuring that they adhere to the underlying physics of …

This paper presents a simple and effective solution for the path tracking problem of a mobile
robot using a PID controller. The proposed method uses a simple linearized model of the …

This work focus on the application of model-based predictive control (MPC) to the trajectory
tracking problem of nonholonomic wheeled mobile robots (WMR). The main motivation of …

This study deals with the dynamic interactions between seaports and decision-making
strategy for seaport operations by utilizing four-dimensional fractional Lotka-Volterra …