Search results for "state estimation"
showing 10 items of 13 documents
An Improved Load Flow Method for MV Networks Based on LV Load Measurements and Estimations
2019
A novel measurement approach for power-flow analysis in medium-voltage (MV) networks, based on load power measurements at low-voltage level in each secondary substation (SS) and only one voltage measurement at the MV level at primary substation busbars, was proposed by the authors in previous works. In this paper, the method is improved to cover the case of temporary unavailability of load power measurements in some SSs. In particular, a new load power estimation method based on artificial neural networks (ANNs) is proposed. The method uses historical data to train the ANNs and the real-time available measurements to obtain the load estimations. The load-flow algorithm is applied with the e…
Racecar Longitudinal Control in Unknown and Highly-Varying Driving Conditions
2020
This paper focuses on racecar longitudinal control with highly-varying driving conditions. The main factors affecting the dynamic behavior of a vehicle, including aerodynamic forces, wheel rolling resistance, traction force resulting from changing tire-road interaction as well as the occurrence of sudden wind gusts or the presence of persistent winds, are considered and assumed to have unknown models. By exploiting the theory on delayed input-state observers and using measurement data about the vehicle and wheel speeds, a dynamic filter that allows the online reconstruction of the above-mentioned unknown time-varying quantities is derived. Moreover, by exploiting the notion of effective tir…
New Measurement Procedure for Load Flow Evaluation in Medium Voltage Smart Grids
2013
The aim of this paper is to present a new approach for the medium voltage (MV) distribution network load flow analysis, mainly based on power measurement at the low voltage (LV) level of MV/LV distribution substations. This allows to use measurement instruments, usually already installed in the secondary substations, thus achieving an equally reliable measurement system with a lower cost compared to measurements at the MV side. The new approach can be applied using a proper communication system to collect the nodal measurements and an iterative algorithm based on ladder iterative technique (LIT) to compute the load flow. The validity of this method is presented and discussed on the basis of…
Robust control of stochastic systems against bounded disturbances with application to flight control
2014
This paper investigates the problems of state observer design and observer-based integral sliding-mode control (SMC) for a class of Itô stochastic systems subject to simultaneous input and output disturbances. A new type of sliding-mode-based descriptor observer method is developed to approximate the system state and disturbance vectors. An integral-type SMC scheme is proposed based on the state estimation to stabilize the overall system. The main contributions of this approach are as follows: 1) The desired estimations of state and disturbance vectors can be obtained simultaneously, and 2) in the designed sliding-mode observer, the integral term of the Itô stochastic noise is eliminated …
Descriptor-type Robust Kalman Filter and Neural Adaptive Speed Estimation Scheme for Sensorless Control of Induction Motor Drive Systems
2012
Abstract This paper deals with robust estimation of speed and rotor flux for sensorless control of motion control systems which use induction motors as actuators. Due to the observability lack of five and six order Extended Kalman Filters, speed is here estimated by means of a Total Least Square algorithm with Neural Adaptive mechanism. This allows the use of a fourth-order Kalman Filter for estimating rotor flux and to filter stator currents. To cope with motor-load parameter variations, a descriptor-type robust Kalman Filter is designed taking explicitly into account these variations. The descriptor-type structure allows direct translation of parameter variations into variations of the co…
Predictive Intelligent Fuzzy Control for Cooperative Motion of Two Nonholonomic Wheeled Cars
2007
In this paper a problem of intelligent cooperative motion control of two wheeled nonholonomic cars (target and follower) is considered. Once a target car converges to a fixed state (position and orientation), a follower car coming from different position and orientation, converges to the state above, without excessive delay between the known arrival time of the target car and the arrival time of the follower. In this sense we present a new predictive fuzzy control system. A Kalman's filter and an odometric model are used to predict the future position and orientation of the target car. The prediction above is employed to plane a circular nonholonomic reference motion for the follower car. A…
Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
2020
Winemaking is concerned about sustainable energy availability that implies new methods for process monitoring and control. The aim of this paper is to realize a comparative analysis of the possibilities offered using estimation techniques, balances, and filtering techniques such as the Kalman filter (KF) and the extended Kalman filter (EKF), to obtain indirect information about the alcoholic fermentation process during winemaking. Thus, an estimation solution of the process variables in the exponential growing phase is proposed, using an extended observer. In addition, two estimation solutions of this process with the EKF and an estimation of the decay phase of the fermentation process are …
Exploring Training Options for RF Sensing Using CSI
2018
This work analyzes human behavior recognition approaches using WiFi channel state information from the perhaps less usual point of view of training and calibration needs. With the help of selected literature examples, as well as with more detailed experimental insights on our own Doppler spectrum-based approach for physical motion/presence/cardinality detection, we first classify the diverse forms of training so far employed into three main categories (trained, trained-once, and training-free). We further discuss under which conditions it is possible to move toward lighter forms of calibration or even succeed in devising fully untrained model-based solutions. Our take home messages are main…
Robust Discrete-Time Lateral Control of Racecars by Unknown Input Observers
2023
This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and as…
State Estimation of a Nonlinear Unmanned Aerial Vehicle Model using an Extended Kalman Filter
2008
An Extended Kalman Filter is designed in order to estimate both state variables and wind velocity vector at the same time for a non conventional unmanned aircraft. The proposed observer uses few measurements, obtained by means of either conventional simple air data sensors or a low cost GPS. To cope with the low rate of the GPS with respect to the other sensors, the EKF algorithm has been modified to allow for a dual rate measurement model. State propagation is obtained by means of an accurate six degrees of freedom nonlinear model of the aircraft dynamics. To obtain joint estimation of state and disturbance, wind velocity components are included in the set of the state variables. Both stoc…