In this work, a novel model predictive control (MPC) scheme is introduced, by integrating
direct and indirect neural control methodologies. The proposed approach makes use of a …

This paper presents a new approach for identifying interval fuzzy models, which enables
estimating fuzzy model structures, parameters, and upper and lower bounds simultaneously …

Fractional calculus plays a fundamental role in understanding the physics of nonlinear
systems due to its heritage of uncertainty, nonlocality and complexity. In this study, novel …

Volterra series is a promising technique with great potential for nonlinear system
identification. The conventional Volterra series model computes the output responses by …

Optimal scheduling in an anti‐lock brake system of ground vehicles is performed through
approximate dynamic programming for reducing the stopping distance in severe braking …

We propose a nonparametric approach for the identification of Wiener systems. We model
the impulse response of the linear block and the static nonlinearity using Gaussian …

This paper concerns the nonparametric identification problem for a class of nonlinear
discrete-time dynamical systems that is characterized by its cascade structure. This is a …

The article addresses the problem of nonlinear system identification with particular focusing
on Wiener models. The proposed input injection methodology allows for identification of a …

This paper develops a nonlinear model predictive control (MPC) algorithm for dynamic
systems represented by piecewise linear (PWL) Hammerstein models. At each sampling …

In this paper, the problem of optimal system identification in nonlinear model predictive
control (NMPC) for highly nonlinear dynamic processes is presented. Due to the short term …