In metal additive manufacturing (AM), the material microstructure and part geometry are
formed incrementally. Consequently, the resulting part could be defect-and anomaly-free if …

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 the Virtual Reference Feedback Tuning (VRFT) of a combination of two
control algorithms, Active Disturbance Rejection Control (ADRC) as a representative data …

In the eyes of many control scientists, the theory of the scenario approach is a tool for
determining the sample size in certain randomized control-design methods, where an …

This book categorizes the wide area of data-driven model-free controllers, reveals the exact
benefits of such controllers, gives the in-depth theory and mathematical proofs behind them …

This paper proposes validates an Iterative Feedback Tuning (IFT) algorithm, which is a
classical and also popular data-driven algorithm, on tower crane systems. The IFT algorithm …

A novel mix of two data-driven algorithms is proposed in this paper. The mix of the
algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback …

Data-driven predictive control (DDPC) has been recently proposed as an effective
alternative to traditional model-predictive control (MPC) for its unique features of being time …

We introduce a novel data-driven model-reference control design approach for unknown
linear systems with fully measurable state. The proposed control action is composed by a …

This paper proposes a combination of a data-driven algorithm represented by continuous-
time Active Disturbance Rejection Control (ADRC) and a Sliding Mode Control (SMC) …