Fuzzy supervised neurocontrol of electrohydraulic servos
نویسندگان
چکیده
منابع مشابه
Development of a Model - Based Impedance Controller for Electrohydraulic Servos
In this paper, a model-based impedance controller for electrohydraulic servosystems is developed. Rigid body and electrohydraulic models, including servovalve models are employed and described by a set of integrated system equations. Friction and leakage of hydraulic elements are also included. The control law consists of two signals, a feedback and a feedforward signal. An impedance filter mod...
متن کاملNeurocontrol and fuzzy logic: Connections and designs
Artificial neural networks (ANNs) and f u z z y logic are complementary technologies. A N N s extract information f rom systems to be learned or controlled, while fuzzy techniques most often use verbal information f rom experts. Ideally, the two sources o f information should be combined. For example, one can learn rules in a hybrid fashion and then calibrate them f o r better whole-system perf...
متن کاملTakagi - Sugeno fuzzy control scheme for electrohydraulic active suspensions
Abstract: The paper presents a new control strategy for active vehicle suspensions using electrohydraulic actuators based on Takagi-Sugeno (T-S) fuzzy modelling technique. As the electrohydraulic actuator dynamics is highly nonlinear, the T-S fuzzy modelling technique using the idea of “sector nonlinearity” is applied to exactly represent the nonlinear dynamics of electrohydraulic actuator in a...
متن کاملActive semi-supervised fuzzy clustering
Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user’s expectations. To make such a semi-supervised categorization approach acceptable for the user, thi...
متن کاملExperiments with Supervised Fuzzy LVQ
Prototype based classifiers so far can only work with hard labels on the training data. In order to allow for soft labels as input label and answer, we enhanced the original LVQ algorithm. The key idea is adapting the prototypes depending on the similarity of their fuzzy labels to the ones of training samples. In experiments, the performance of the fuzzy LVQ was compared against the original ap...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: PAMM
سال: 2006
ISSN: 1617-7061,1617-7061
DOI: 10.1002/pamm.200610405