An $${\mathscr {H}}_{\infty }$$ Approach to Data-Driven Offset-Free Tracking

Abstract

Data-driven controllers also called model-free controllers were invented in order to omit plant modeling step of model-based controllers. Design procedure of these controllers is directly based on experimental I/O data collected from real plant. It can ensure their reliability in real world applications, where the exact model is not available in most cases. In this paper, we consider the problem of accurate tracking performance in presence of external disturbances using data-driven methodologies combined with H ∞ approach. Defining the improved subspace-based predictor, as the base step of the proposed controller's design procedure, an integrator is applied to the control loop, which increases the accuracy of controller's reference tracking performance. Moreover, a weighting function is considered for disturbance attenuation. Simulation results evidently illustrate efficiency and satisfactory performance of the proposed controller.

An H∞ approach to data‐driven fault estimation, and isolation for Hammerstein‐Wiener systems

“…This subspace predictor can be computed without the realization of the actual state‐space models, which significantly reduces computational requirements. Applications of subspace methods in the fields of control engineering such as predictive control, 33 offset‐free tracking control, 34 fault estimation, 35 and simultaneous fault detection and control (SFDC) 36 can be mentioned. Data‐driven fault identification methods that use subspace identification to detect fault have been reviewed in Reference 37.…”

Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

“…However, these control methods are commonly designed based on a priori knowledge about the robots’ implicit/explicit models which are often unavailable, or it is laborious to achieve them. In the literature, there exist several data-based techniques, such as virtual-reference feedback tuning (Radac and Precup, 2018), subspace predictors (Salim and Esmaeili, 2020; Salim and Khosrowjerdi, 2017), and dynamic linearization (Hou and Xiong, 2019; Yu et al, 2020b), which only rely on the plants’ measurement data.…”