Agriculture plays a decisive role in the survival of humankind. The efficient and precise
regulation of agriculture will ensure the welfare of people throughout the world. However …

Constrained optimization problems arise in numerous scientific and engineering
applications, and many papers on the online solution of constrained optimization problems …

In this paper, we present a neurodynamic approach to model predictive control (MPC) of
unknown nonlinear dynamical systems based on two recurrent neural networks (RNNs). The …

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 …

This paper presents new results on a neural network approach to nonlinear model predictive
control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of …

This paper presents a recurrent neural network (RNN) approach to nonlinear model
predictive control (MPC). By using decomposition, the original optimization associated with …

Model predictive control (MPC), also known as receding horizon control (RHC), is an
advanced control strategy for optimizing the performance of control systems. For nonlinear …

A robust model predictive control (MPC) method is proposed for nonlinear affine systems
with bounded disturbances. The robust MPC technique requires on-line solution of a …

This paper presents a neurodynamic approach to bicriteria model predictive control (MPC)
of nonlinear affine systems based on a goal programming formulation. Bicriteria MPC refers …

Model predictive control algorithm with constraints research has important significance in
industrial applications. In this paper, thanks to model predictive control problem with input …