Fault Detection and Control of Process Systems

Abstract

This paper develops a stochastic hybrid model-based control system that can determine online the optimal control actions, detect faults quickly in the control process, and reconfigure the controller accordingly using interacting multiple-model (IMM) estimator and generalized predictive control (GPC) algorithm. A fault detection and control system consists of two main parts: the first is the fault detector and the second is the controller reconfiguration. This work deals with three main challenging issues: design of fault model set, estimation of stochastic hybrid multiple models, and stochastic model predictive control of hybrid multiple models. For the first issue, we propose a simple scheme for designing faults for discrete and continuous random variables. For the second issue, we consider and select a fast and reliable fault detection system applied to the stochastic hybrid system. Finally, we develop a stochastic GPC algorithm for hybrid multiple-models controller reconfiguration with soft switching signals based on weighted probabilities. Simulations for the proposed system are illustrated and analyzed.

Robust Model Predictive Control Schemes for Tracking Setpoints

“…A similar result in [19] can be applied for the above tracking setpoint RMPC scheme with a common Lyapunov matrix to find a robust stabilizing state feedback for the uncertain systems. However we still have other methods to ensure the stability within a finite prediction horizon.…”

Quantifying Electrode Reliability During Brain–Computer Interface Operation

“…Detection of anomalous data patterns has been largely addressed in fields like control systems [15] and remote sensing [16]. However, this issue has been largely neglected in the design of BCI applications.…”

Time forward observer based adaptive controller for a teleoperation system

“…The environment modeling and verification are referred to in Minh et al in [10]. It is assumed that the environmental uncertainties can be represented by mathematical models.…”

“…The teleoperation transparency in time-delays and the effect of local force feedback was also investigated in Hashtrudi-Zaad and Salcudean [9] where the stability robustness of the system was analyzed and the transparency conditions for a general teleoperation system are presented. The environment uncertainty modeling and verification are referred to in Minh et al in [10]. The stability of uncertain cellular neural networks and the stability criterion for bidirectional associate memory neural networks with interval time-varying delays are also referred to in Park and Kwon in [11] and in Chen et al in [12].…”