Abstract
We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.
Highlights
Surveillance of processes is generally defined as monitoring of activities and behavior to collect information to influence, manage, or direct
The surveillance is done via industrial drones that have applications in defense, newsgathering, telecommunication, humanitarian needs, public and private safety, traffic monitoring, security, and process industry [1]. ese drones are equipped with a gimbal camera sensor that collects information and sends it to a remote control room through a communication network
A smart surveillance system makes use of a remote control mechanism that can handle the surveillance operation. e remote control of geographically distributed networked systems is widely used [2,3,4] for cost reduction, simple installation, and maintenance and for increasing efficiency and reliability in the recent industrial revolution
Summary
Surveillance of processes is generally defined as monitoring of activities and behavior to collect information to influence, manage, or direct. Most of the researchers addressed either the actuator faults or sensor faults with network-induced effects especially with time delays [14]. E authors of [18] presented a new fault model for actuators and tolerated its effects using a state feedback controller. To address the aforementioned issues, we have designed a remote fault-tolerant control for an industrial smart surveillance system with the following contributions. (2) e mode-dependent output feedback-based remote controller is designed such that the controller does not directly depend on the sensor’s output Such dynamic remote fault-tolerant controllers are significant for industrial processes where the system’s states are unknown.
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