A5068059152- Adaptive Control of Nonlinear Systems
- Advanced Control Systems Optimization
- Fault Detection and Control Systems
- Control and Dynamics of Mobile Robots
- Microgrid Control and Optimization
- Neural Networks and Applications
- Dynamics and Control of Mechanical Systems
- Target Tracking and Data Fusion in Sensor Networks
- Robotic Path Planning Algorithms
- Distributed Control Multi-Agent Systems
- Fuzzy Logic and Control Systems
- Sensorless Control of Electric Motors
- Frequency Control in Power Systems
- Electric Motor Design and Analysis
- Advanced DC-DC Converters
- Stability and Controllability of Differential Equations
- Machine Fault Diagnosis Techniques
- Aerospace Engineering and Control Systems
- Hydraulic and Pneumatic Systems
- Stability and Control of Uncertain Systems
- Economic theories and models
- Stochastic processes and financial applications
- Complex Systems and Time Series Analysis
- Magnetic Bearings and Levitation Dynamics
- Control Systems in Engineering
Industrial Systems Institute
2016-2025
Institute of Automation
2024
Institute of Electrical and Electronics Engineers
2024
University of Salerno
2015-2020
North China Electric Power University
2020
Indira Gandhi Institute of Development Research
2017-2020
University of Missouri
2020
GE Global Research (United States)
2020
Centre National de la Recherche Scientifique
2020
General Electric (United States)
2020
Extended Kalman Filtering (EKF) is proposed for: (i) the extraction of a fuzzy model from numerical data; and (ii) localization an autonomous vehicle. In first case, EKF algorithm compared to Gauss–Newton nonlinear least-squares method shown be faster. An analysis convergence given. second estimates state vector vehicle by fusing data coming odometric sensors sonars. Simulation tests show that accuracy EKF-based satisfactory.
For nonlinear systems, subject to Gaussian noise, the extended Kalman filter (EKF) is frequently applied for estimating system's state vector from output measurements. The EFK based on linearization of systems' dynamics using a first-order Taylor expansion. Although EKF efficient in several problems, it characterized by cumulative errors due gradient-based performs, and this may affect accuracy estimation or even risk stability estimation-based control loop. To overcome flaws EKF, has been...
A control method for distributed interconnected power generation units is developed. The system comprises permanent-magnet synchronous generators (PMSGs), which are connected to each other through transformers and tie-lines. derivative-free nonlinear Kalman filtering approach introduced aiming at implementing sensorless of the generators. In proposed method, generator's model first subjected a linearization transformation that based on differential flatness theory next state estimation...
State estimation is a major problem in industrial systems, particularly robotics. To this end, Gaussian and nonparametric filters have been developed. In paper, the extended Kalman filter, which assumes measurement noise, compared with particle does not make any assumption on noise distribution. As case study, of state vector an robot used when measurements are available from accelerometer that was mounted end effector robotic manipulator encoders joints' motors. It shown that, kind sensor...