Fuzzy Logic and Kalman Filters Applied in Robotics and Process Control
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (10 December 2021) | Viewed by 449
Special Issue Editors
Interests: fuzzy modeling; fuzzy control; sensor fusion; robot navigation; interactive robots
Interests: control education; machine learning; autonomous systems; fuzzy control; social robots; human–robot interaction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The extended Kalman filter has been widely used for more than five decades using probabilistic techniques. This algorithm provides an accurate state estimation by fusing incoming information from internal and external sensors and adjusting appropriately the uncertainty matrices of the initial state estimation and of the process and observation models. However, the conventional Kalman filter has problems when dealing with asymmetric process and observation noises; it generates accumulative errors when probability distributions are propagated through non-linear equations and does not work properly in the presence of inaccurate models as those obtained from human experience.
In such a way, fuzzy logic shows a complementary way to cope with uncertainty, so the combination of fuzzy logic and the Kalman filter has been widely used for the last few years in several ways:
- Kalman filters where fuzzy rules are used to adapt process and observation noises.
- Kalman filters where fuzzy logic is used for sensor fusion outside the state estimation process.
- State estimation using a set of linear Kalman filters, which are averaged by a fuzzy supervisor.
- Kalman filters to design an estimator for the Takagi–Sugeno fuzzy model.
- Kalman filters in which the parameters of the models are fuzzy numbers.
- Kalman filters in which the parameters are intervals (extending the idea of interval Kalman filters).
- Kalman filters in which process and observation models are represented by fuzzy relations.
- Kalman filters in which probability distributions are completely replaced by possibility distributions.
This Special Issue addresses innovative solutions in the field of state estimation and sensor fusion that combine fuzzy logic with Kalman filters, including applications from robotics to process control.
Prof. Fernando Matia
Dr. Daniel Galan
Guest Editors
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Keywords
- Kalman filter
- sensor fusion
- state estimation
- fuzzy logic
- robotics
- process control
- probability versus possibility
