Automatica (Journal of IFAC)
Adaptive-predictive control of a class of SISO nonlinear systems
Dynamics and Control
Robust Control: Systems with Uncertain Physical Parameters
Robust Control: Systems with Uncertain Physical Parameters
Brief paper: Stabilization of linear systems over networks with bounded packet loss
Automatica (Journal of IFAC)
Direct data-driven recursive controller unfalsification with analytic update
Automatica (Journal of IFAC)
Compensation for control packet dropout in networked control systems
Information Sciences: an International Journal
Model-free adaptive control design using evolutionary-neural compensator
Expert Systems with Applications: An International Journal
Iterative Learning Control: Brief Survey and Categorization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Journal of Control Science and Engineering
Local Models for data-driven learning of control policies for complex systems
Expert Systems with Applications: An International Journal
From model-based control to data-driven control: Survey, classification and perspective
Information Sciences: an International Journal
| Hi-index | 12.05 |
It is well-known that robustness refers to the ability dealing with unknown uncertainties or unmodeled dynamics of the model-based control theory. However, this kind of robustness no longer has any significance for model free or data-driven control theory because the controller is designed only using I/O data of the controlled plant. In this paper, a novel robustness of the model free control or data-driven control is presented when the system is controlled by the model free adaptive control (MFAC) scheme. It is assumed that an MFAC scheme is implemented via a network control system and that data dropout occurs due to a failing sensor, actuator or network failure, resulting in what it is called intermittent MFAC. The stability of such a MFAC scheme is analyzed by the statistical approach. It is shown that the MFAC system is still stable when data dropout occurs, and the data dropout impacts the convergence speed.