CONTROLLER design is more troublesome in aerospace systems due to, inter alia, diversity
of mission platforms, convoluted nonlinear dynamics, predominantly strict mission and …

For at least fifty years, the inverted pendulum has been the most popular benchmark, among
others, in nonlinear control theory. The fundamental focus of this work is to enhance the …

This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in
dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic …

Abstract Industry 4.0 is taking human-robot collaboration at the center of the production
environment. Collaborative robots enhance productivity and flexibility while reducing …

This paper provides a review of the available tuning guidelines for model predictive control,
from theoretical and practical perspectives. It covers both popular dynamic matrix control …

This paper describes nonlinear methods in model building, dynamic data reconciliation, and
dynamic optimization that are inspired by researchers and motivated by industrial …

A moving horizon estimation (MHE) approach to simultaneously estimate states and
parameters is revisited. Two different noise models are considered, one with measurement …

A non-linear model predictive controller (NMPC) was investigated as a route to delivering
improved product quality, batch to batch reproducibility and significant cost reductions by …

In this paper, we discuss the model predictive control algorithms that are tailored for
uncertain systems. Robustness notions with respect to both deterministic (or set based) and …

For the last 30 years the theory and technology of model predictive control (MPC) have been
developed rapidly. However, facing the increasing requirements on the constrained …