This paper concentrates on the problem of control of a hybrid energy storage system (HESS)
for an improved and optimized operation of load-frequency control applications. The HESS …

The presented input convex neural multi-modelling approach to Model Predictive Control
(MPC) has two essential advantages. Firstly, the MPC algorithm solves only convex …

In this work, a novel approach combining first principle equation of Darcy's law on cake
filtration and artificial neural network (ANN) predictive models were utilized to represent the …

Oxygen content in flue gas is a key variable in the operation of the municipal solid waste
incineration (MSWI) process. However, the control performance of oxygen content could not …

In this article, a novel Hammerstein structure is proposed for nonlinear networked systems
based on an interval type-2 Takagi–Sugeno–Kang (IT2TSK) fuzzy system. The proposed …

In the last three decades, artificial intelligence (AI) has been increasingly and vigorously
utilized for process control in chemical, biochemical, and biomedical engineering. These …

Model Predictive Control (MPC) algorithms typically use the classical L 2 cost function,
which minimises squared differences of predicted control errors. Such an approach has …

This study proposes a novel fuzzy Wiener structure for identifying engineering systems. The
proposed model has a cascade structure; a nonlinear static part preceded by a linear …

This paper presents a model-based approach for on-line monitoring of difficult-to-measure
quality variables with the description of their dynamic behavior. The strategy comprises the …

Wiener model, which is one of the structures used in the modeling of nonlinear systems,
consists of the cascade connection as that a dynamic linear system is followed in series by a …