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The problem of model-based fault detection is studied with application of the Kalman filter for residual generation. The filter has two important incoming parameters, the state noise and the output noise covariance matrices, which tuning is analyzed in order to optimize the fault detection performance. The problem is formulated through an appropriate optimization criteria and applied to the oscillatory...
Based on the developed nonlinear dynamic equations of a quadrotor (named as Qball-X4) UAV (Unmanned Aerial Vehicle), attitude and trajectory tracking control designs based on an inner/outer loop control structure has been proposed in this paper. Feedback linearization is designed to control the attitude stability in inner loop, traditional PID is designed to follow trajectory in accordance with pre-planned...
This work compares the use of direct torque and flux control (DTFC) and model predictive control (MPC) for induction motor (IM) control. These two strategies are fundamentally different in operation since (i) DTFC decides the current control action based on a switching table constructed using a simplified model of the IM, whereas (ii) MPC decides the current control action by on-line minimization...
Predictive Control formulations can be designed with nominal asymptotic stability guarantees, provided that the associated optimization problem is feasible at each sampling time. However, model-plant mismatches, external perturbations or faults may cause the optimization to become infeasible. Such a problem motivates the development of techniques aimed at recovering feasibility without violating hard...
In this paper, a new approach for state filtering of dynamic stochastic discrete-time systems affected by unknown inputs is presented. The proposed state filtering scheme includes a restricted diagonal detection filter generating a set of minimum variance white detection signals, each of them sensitive to a particular component of the unknown input vector. After having tested the statistical effect...
A new methodology is presented in this paper which incorporates the marginalized likelihood ratio (MLR) test for online fault detection and isolation. The proposed methodology reduces the number of optimization problems required for isolating the fault by means of a simple integration scheme. Moreover, the dependency on the accuracy of the statistical fault detection and confirmation tests is relaxed...
This paper examines the fault tolerant control problem for a generic class of incipient failure modes that grow in severity as a function of component loading. Assuming that a prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component loads to minimize risk of...
This paper deals with a nonlinear model predictive control designed for a boiler unit. The predictive controller is realized by means of a recurrent neural network which acts as an one-step ahead predictor. Then, based on the neural predictor, the control law is derived solving an optimization problem. Fault tolerant properties of the proposed control system are investigated. A set of 12 faulty scenarios...
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