Model-based fault-tolerant control for distributed systems

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A Closer Look at Fault-Tolerant Control



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Faults are inevitable and even incipient faults that progress very slowly can downgrade the performance of the system. In cases where a fault is not critical, the system performance can be kept at an acceptable level by mitigating the effect of fault. In this chapter, model-based fault-tolerant control and fault accommodation algorithm are presented for two challenging classes of distributed systems; first a spatially distributed system that can be decomposed into interconnected subsystems, and second a distributed parameter system where the system state is distributed over a continuous range of space. The design of a decentralized fault tolerant controller (DFTC) is presented for interconnected nonlinear continuous-time systems by using local subsystem state vector alone in contrast with traditional distributed fault tolerant controllers or fault accommodation schemes where the measured or estimated state vector of the overall system is needed. The decentralized controller uses local state and input vectors in each subsystem and minimizes the fault effects on the entire system. The DFTC in each subsystem includes a traditional controller and a neural network based online approximator which is used to deal with the unknown parts of the system dynamics, such as fault and interconnection terms. The stability of the overall system with DFTC is investigated by using Lyapunov approach and the boundedness of all signals is guaranteed in the presence of a fault. Therefore, the proposed controller enables the system to continue its normal operation after the occurrence of a fault, as long as it does not cause failure or break- down of a component. Next, a model-based fault accommodation scheme is introduced for a class of linear distributed parameter systems (DPS) represented by partial differential equations (PDEs) in the presence of both actuator and sensor faults. A filter-based observer on the basis of the linear PDE model of the DPS is designed with output measurements. The estimated output from the observer and the measured outputs are utilized to generate a residual for fault detection. Upon detection, the fault function is estimated by using an unknown parameter vector and a known basis function. Update laws are introduced to estimate the unknown fault parameter vector for actuator and sensor faults. These estimates will then be used to modify the nominal controller in order to accommodate the actuator and sensor faults.

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Aadaptive estimation, Distributed systems, Fault accommodation, Fault detection, Fault-tolerant control


Department of Electrical Engineering

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