The stability margin is an important attribute for the robustness analysis of the closed-loop in control system design, which indicates the tolerant-ability of the closed-loop stability to system uncertainty (or fault). Seeking to develop an advanced data-driven monitoring and management framework for control performance (especially for robust stability) of the closed-loop system, this paper presents a study on the data-driven realization of the closed-loop stability margin, and its application to control-performance-oriented process monitoring. Specifically, without identifying the system parameters, a data-driven realization of the stability margin is first determined based on the identified multiplication operator of the closed-loop transfer function matrices using time domain closed-loop measurements. Secondly, a control-performance-oriented process monitoring approach is proposed based on the determined data-driven realization of the stability margin. The contributions of this paper will bridge the gap between the model-based robustness analysis/design and the data-driven techniques for the future research. The main results of this paper are verified and demonstrated through the case studies on a DC motor benchmark system.

This paper deals with data-driven fault diagnosis of three phase induction motor. The tools from subspace identification based data driven scheme are utilized to design a framework of fault detection and isolation. The fault detection scheme is so designed that it caters for even small magnitude faults. In order to discriminate different faults, a fault isolation algorithm is proposed. Isolation thresholds are designed. The results show the effectiveness of the proposed schemes

Time-delays, model uncertainties, faults, and disturbances are frequently the main causes of performance degradation and instability in industrial process control. This article presents a fault-tolerant robust model predictive control design for processes that involve the above effects and process constraints. The uncertainties are modeled into a polytopic affine form based on variations in the system's parameters. A new model based on augmentation of the state variables and tracking error is used that provides increased degrees of freedom for the control design and guarantees tracking performance. A parameter-dependent Lyapunov Krasovskii functional is used to design a state-feedback control that reduces the conservatism of the conventional approach and ensures robust stability and tracking performance of the closed-loop system. At each sampling instant, a control action is computed by solving an online constrained optimization problem that minimizes the upper bound of the "worst-case" performance index. Finally, the proposed framework is employed for the fault-tolerant control of a continuous stirred tank reactor to demonstrate its effectiveness in mitigating actuator faults and tracking the desired set-point.

Integrated design of observer based Fault Detection (FD) for a class of uncertain nonlinear systems with Lipschitz nonlinearities is studied. In the context of norm based residual evaluation, the residual generator and evaluator are designed together in an integrated form, and, based on it, a trade-off FD system is finally achieved in the sense that, for a given Fault Detection Rate (FDR), the False Alarm Rate (FAR) is minimized. A numerical example is given to illustrate the effectiveness of the proposed design method.