13,001 research outputs found
Model Prediction-Based Approach to Fault Tolerant Control with Applications
Abstract— Fault-tolerant control (FTC) is an integral component in industrial processes as it enables the system to continue robust operation under some conditions. In this paper, an FTC scheme is proposed for interconnected systems within an integrated design framework to yield a timely monitoring and detection of fault and reconfiguring the controller according to those faults. The unscented Kalman filter (UKF)-based fault detection and diagnosis system is initially run on the main plant and parameter estimation is being done for the local faults. This critical information\ud
is shared through information fusion to the main system where the whole system is being decentralized using the overlapping decomposition technique. Using this parameter estimates of decentralized subsystems, a model predictive control (MPC) adjusts its parameters according to the\ud
fault scenarios thereby striving to maintain the stability of the system. Experimental results on interconnected continuous time stirred tank reactors (CSTR) with recycle and quadruple tank system indicate that the proposed method is capable to correctly identify various faults, and then controlling the system under some conditions
Optimal mapping of joint faults into healthy joint velocity space for fault-tolerant redundant manipulators
redundant manipulator
Evaluating the Impact of SDC on the GMRES Iterative Solver
Increasing parallelism and transistor density, along with increasingly
tighter energy and peak power constraints, may force exposure of occasionally
incorrect computation or storage to application codes. Silent data corruption
(SDC) will likely be infrequent, yet one SDC suffices to make numerical
algorithms like iterative linear solvers cease progress towards the correct
answer. Thus, we focus on resilience of the iterative linear solver GMRES to a
single transient SDC. We derive inexpensive checks to detect the effects of an
SDC in GMRES that work for a more general SDC model than presuming a bit flip.
Our experiments show that when GMRES is used as the inner solver of an
inner-outer iteration, it can "run through" SDC of almost any magnitude in the
computationally intensive orthogonalization phase. That is, it gets the right
answer using faulty data without any required roll back. Those SDCs which it
cannot run through, get caught by our detection scheme
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