1,721,069 research outputs found
Validation of the Fuzzy Kalman Filter for ship tracking applications
No full textTracking of ships’ motion and monitoring of maritime traffic can be performed with the use of distributed Kalman
Filtering. However, some of the local Kalman Filters which constitute distributed estimation schemes may be based on
inaccurate models of the vessel’s dynamics or kinematics and in such a case the aggregate state estimate provided by the
distributed filter is unreliable. To treat this problem the paper proposes a statistical method of optimized performance for the
validation of Fuzzy Kalman Filters used in ship tracking. This statistical validation test is capable of detecting the faulty local
filter within the distributed estimation method, even in the case of small errors in the local model’s parameters which do not
exceed 1% of the associated nominal values
Power corporations' default probability forecasting using the Derivative-free nonlinear Kalman Filter
No full textFlatness-based adaptive fuzzy control of brushless doubly-fed reluctance machines
No full textThe article proposes an adaptive control approach that is capable of compensating for model uncertainty and parametric changes of the doubly-fed reluctance machines (DFRMs), as well as for the lack of measurements about the DFRM's state vector elements. First it is proven that the DFRM's model is a differentially flat one. By exploiting differential flatness properties it is shown that the DFRM model can be transformed into the linear canonical form. For the latter description, the new control inputs comprise unknown nonlinear functions which can be identified with the use of neurofuzzy approximators. The estimated dynamics of the generator is used by a feedback controller thus establishing an indirect adaptive control scheme. Moreover, to robustify the control loop, a supplementary control term is computed using H-infinity control theory. Another problem that has to be dealt with comes from partial measurements of the state vector of the generator. Thus, a state observer is implemented in the control loop. The stability of the considered observer-based adaptive control approach is proven using Lyapunov analysis. Moreover, the performance of the control scheme is evaluated through simulation experiments
A nonlinear optimal control method for autonomous submarines' diving
No full textA nonlinear H-infinity (optimal) control method is developed for the problem of simultaneous control of the depth and heading angle of an autonomous submarine. This is a multi-variable nonlinear control problem and its solution allows for precise underwater navigation of the submarine. The submarine's dynamic model undergoes approximate linearization around a temporary equilibrium that is recmputed at each iteration of the control algorithm. The linearization procedure is based on Taylor series expansion and on the computation of the submarine's model Jacobian matrices. For the approximately linearized model, the optimal control problem is solved through the design of an H-infinity feedback controller. The computation of the controller's gain requires the solution of an algebraic Riccati equstion, which is repetitively performed at each step of the control method. The stability of the control scheme is proven through Lyapunov analysis. First, it is demonstrated that for the submarine's control loop, the H-infinity tracking performance criterion holds. Moroever, under moderate conditions it is shown that that the control scheme is globally asymptotically stable
Kalman filtering and statistical decision making for detection of attacks against power grid sensors
No full textKalman Filtering and statistical decision making criteria are used to develop a systematic method for the detection of attacks against sensors of the power grid. To emulate the functioning of the grid's sensors in the fault-free mode, the Kalman Filter is used as a virtual sensor. By comparing the output of the Kalman Filter against the output of the real sensors, the resulting differences generate the residuals' sequence. By weighting the square of the residuals' vector with the inverse of the associated covariance matrix a random variable is defined which is shown to follow the Ï2distribution. This variable provides a statistical test about the deviation of the sensors functioning from the normal mode. Moreover, by exploiting the properties of the Ï2distribution and by using the confidence intervals approach, one can define thresholds against which the value of the statistical test is compared. In case that these thresholds are exceeded by the value of the statistical test then it can be inferred that the sensors' functioning is abnormal. Additionally, sections of the power grid which have been exposed to the attack can be identified by applying the statistical test on clusters of sensors. Actually, by applying the statistical test at each individual sensor one can isolate the compromised sensors. Finally, one can estimate the additive disturbance inputs that affect the sensors by redesigning the Kalman Filter as a disturbance observer. This may provide an indication on whether the deviation of the sensors functioning from normal has been the result of an attack to the grid by intruders
Nonlinear H-infinity control for the rotary pendulum
No full textThe rotary (Furuta's) pendulum is used to analyzed the performance of a new nonlinear optimal (H-infinity) control for underactuated robotic systems. After applying partial feedback linearization, the pendulum's dynamic model is first transformed to an equivalent form. The later description of the pendulum's dynamics undergoes approximate linearization which takes place round a temporary operating point (equilibrium) recomputed at each iteration of the control algorithm. The linearization makes use of Taylor series expansion of the state-space model of the system and of computation of the associated Jacobian matrices. For the approximately linearized model of the pendulum an H-infinity feedback controller is developed. Through the repetitive solution of an algebraic Riccati equation which is also performed at each step of the control method, the controller's gain is computed. The stability features of the control loop are proven with Lyapunov analysis. First it is shown that the control loop satisfies the H-infinity tracking performance condition. Next, under moderate conditions it is also shown that the global asymptotic stability of the control loop can be assured
Distributed filtering and local statistical approach to fault diagnosis for securing the power grid
No full textDistributed Kalman filtering and the local statistical approach to fault diagnosis are used to develop a systematic method for the detection of attacks against sensors of the power grid. To treat the case of grid's frequency may deviation from its nominal value as well as the case of imprecise grid frequency measurement (i) the sensors dynamics is described using multiple local Kalman filters associated with different measurements of the grid's frequency, and (ii) fusion of the local filters' estimates is performed, in the sense of distributed Kalman filtering, so as to obtain a reliable aggregate estimate of the sensors' state. Next, to emulate the functioning of the grid's sensors in the fault-free mode, the distributed Kalman filter is used as a virtual sensor. By comparing the output of the distributed Kalman filter against the output of the real sensors, the resulting differences generate the residuals' vector. The residuals sequence undergoes statistical signal processing, so as to determine if specific sensors have undergone an intruder;s attack. The generalized likelihood ratio of the residuals sequence is used, as a statistical change detection criterion. This provides a statistical test that is based on the Ï distribution, and allows to detect deviation of the sensors' functioning from normal mode. Using the properties of the Ï distribution, an optimal threshold is defined for deciding on whether a sensor has been providing distorted measurements or not. Besides, with the application of this statistical criterion to clusters of sensors within the entire sensors' set it is possible to isolate those particular sensors which have been exposed to failure or an intruder's attack. The method achieves detection of sensors' malfunctioning that differs less than 1% from the nominal sensor's output. The application of the proposed method contributes to the enforcement of the security levels of the electric power grid
Nonlinear optimal control for the VSC-HVDC transmission system
No full textA nonlinear H-infinity (optimal) control method is proposed for the problem of control of the VSC-HVDC transmission system (Voltage Source Converter - High Voltage DC transmission system). Approximate linearization, round a local operating point, is performed for the dynamic model of the VSC-HVDC transmission system. This local equilibrium consists of the present value of the state vector of the VSC-HVDC model and of the last value of the control input that was exerted on it, and is re-calculated at each time instant. To accomplish this linearization, Taylor series expansion and the computation of the associated Jacobian matrices are performed. The robustness of the control scheme allows to compensate for the modelling error which is due to truncation of higher order terms from the Taylor expansion. Next, an H-infinity feedback controller is designed. After solving an algebraic Riccati equation at each iteration of the control algorithm, the feedback gain is computed. Lyapunov stability analysis is used to prove that the control loop satisfies an H-infinity tracking performance criterion. This also indicates elevated robustness to model uncertainty and external perturbations. Moreover, under moderate conditions it is proven that the control loop is globally asymptotically stable
- …
