Fuzzy control has long been applied to industry with several important theoretical results
and successful results. Originally introduced as model-free control design approach, model …

With the increasing amount of available data, computing power and network speed for a
decreasing cost, the manufacturing industry is facing an unprecedented amount of data to …

This article presents a hybrid metaheuristic optimization algorithm that combines particle
filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm …

This paper presents a new Reinforcement Learning (RL)-based control approach that uses
the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the …

This paper introduces a novel reference tracking control approach implemented using a
combination of the Actor-Critic Reinforcement Learning (RL) framework and the Grey Wolf …

This paper proposes as an element of novelty the Unified Form (UF) clustering algorithm,
which treats Fuzzy C-Means (FCM) and K-Means (KM) algorithms as a single configurable …

This paper presents a novel application of the metaheuristic Slime Mould Algorithm (SMA) to
the optimal tuning of interval type-2 fuzzy controllers. Inserting the information feedback …

This paper suggests five new contributions with respect to the state-of-the-art. First, the
optimal tuning of cost-effective fuzzy controllers represented by Takagi–Sugeno–Kang …

Smart manufacturing arises the growing demand for predictive analytics to forecast the
deterioration and reliability of equipment. Many machine learning algorithms, especially …

This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a
reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs …