Dual-arm robots show better task adaptability but face more constraints than single-arm
robots. Inspired by human cooperation options, a nonlinear model predictive cooperative …

This paper describes the development of a method to optimally tune constrained MPC
algorithms with model uncertainty. The proposed method is formulated by using the worst …

There is growing interest in the use of control theory for interdisciplinary applications, where
data may be sparse or missing, be non-uniformly sampled, have greater uncertainty, and …

In this paper, an improved approach of extended non-minimal state space (ENMSS)
fractional order model predictive control (FMPC) is presented and tested on the temperature …

The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge
technologies to meet the growing demand for life‐saving treatments. In this context, Model …

Two multi-objective optimization based tuning methods for model predictive control are
proposed. Both methods consider the minimization of the error between the closed-loop …

An approach to minimize tuning effort of nominal Model Predictive Control algorithms is
proposed. The algorithm dynamically calculates output set points to accommodate user …

This work is concerned with the tuning of the parameters of Model Predictive Control (MPC)
algorithms when used for industrial tasks, ie, compensation of disturbances that affect the …

A tuning procedure for a model predictive controller (MPC) is presented for multi-input multi-
output systems. The approach consists of two steps based on a hybrid method: the goal …

Model predictive control (MPC) algorithms brought increase of the control system
performance in many applications thanks to relatively easily solving issues that are hard to …