The cement kiln is one of the major energy-intense processes that need efficient controllers
to minimise fuel consumption, enhance clinker production, and improve cement quality. A …

This paper investigates the parameter estimation of multivariable Wiener nonlinear systems.
To solve the inconsistency problem of the parameter vector and the parameter matrix, the …

To cope with the computational complexity of the traditional model predictive control, and to
reduce the error of the linearization and prediction processes, this paper presents an …

In order to solve the problem of the traditional sequential quadratic programming thrust
allocation method, such as limited feasible region and low propulsion efficiency, a thrust …

Shallow models have limited ability to express high‐dimensional nonlinear complex
functions. Based on deep learning, a Gaussian radial basis function neural network …

For many practical industrial objects with time-varying operating points, strong nonlinearity,
and difficulty in obtaining analytical models, the data-driven identification method is usually …

Bilinear models are a special class of nonlinear models significant for nonlinear systems'
parameter estimation and control design. This study proposes a novel application of partial …

In this paper, a Nonlinear Model Predictive (NMPC) strategy is proposed. It is based on a
reduced structure Radial Basis Function (RBF) neural network trained by a recent algorithm …

The resonant frequency region characteristics of non-minimum phase (NMP) DC-DC
converter systems in the presence of input voltage and load current disturbances are crucial …

Aiming at the problem of performance degradation of flue gas acid‐making control system
caused by system uncertainty and external interference, this paper presents a model …