Particle filtering for sensor fault diagnosis and identification in nonlinear plants

Design of state space linear quadratic tracking control using GA optimization for batch processes with partial actuator failure

“…[8][9][10][11][12][13]. It is seen that responses of IMC-2 are not as good as those of IMC-1 and the proposed LQ.…”

“…action of actuator in response to the controller output, which may cause serious deterioration of performance [10]. To cope with these issues, various results focusing on fault diagnosis and control are conducted [11][12][13][14][15]. Among these results, iterative learning may be a very attractive one.…”

Robust Fault Detection for a Class of Uncertain Nonlinear Systems Based on Multiobjective Optimization

“…Recently in [9], the authors introduced a novel fault detection and diagnosis for nonlinear non-Gaussian dynamic processes using kernel dynamic independent component analysis method. Sensor fault diagnosis and identification in nonlinear plants were discussed in [10]. The faults under consideration in [10] were assumed to be abruptly occurring calibration errors.…”

“…Sensor fault diagnosis and identification in nonlinear plants were discussed in [10]. The faults under consideration in [10] were assumed to be abruptly occurring calibration errors. Thus, an adaptive particle filter was developed to diagnose sensor faults and compensate for their effects.…”

2D non-linear state optimization using evolutionary techniques

“…The algorithm of Particle filter [1] is fit for the state estimation of nonlinear, non-Gaussian noise system, hence is an active topic of research and has found an extensive amount of applications in the fields of object tracking [2], diagnosis of faults [3], economic forecasting [4] and diverse other areas on account of its stability, fast and robust nature. Sequential Monte Carlo methods used the sampling approach to estimate the state distributions with the set of particles to represent the posterior density.…”