215 research outputs found
Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
In support of reasoning with sparse rule bases, fuzzy rule interpolation (FRI) offers a helpful inference mechanism for deriving an approximate conclusion when a given observation has no overlap with any rule in the existing rule base. One of the recent and popular FRI approaches is the scale and move transformation-based rule interpolation, known as T-FRI in the literature. It supports both interpolation and extrapolation with multiple multi-antecedent rules. However, the difficult problem of defining the precise-valued membership functions required in the representation of fuzzy rules, or of the observations, restricts its applications. Fortunately, this problem can be alleviated through the use of type-2 fuzzy sets, owing to the fact that the membership functions of such fuzzy sets are themselves fuzzy, providing a more flexible means of modelling. This paper therefore, extends the existing T-FRI approach using interval type-2 fuzzy sets, which covers the original T-FRI as its specific instance. The effectiveness of this extension is demonstrated by experimental investigations and, also, by a practical application in comparison to the state-of-the-art alternative approach developed using rough-fuzzy setspublishersversionPeer reviewe
Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments
This paper investigates the path tracking control
problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached
by employing a 2-degree of freedom vehicle model, which is
reformulated into a newly defined parametric form with the
system uncertainties being lumped into an unknown parametric
vector. On top of the parametric system representation, a novel
robust adaptive learning control (RALC) approach is then
developed, which estimates the system uncertainties through
iterative learning while treating the external disturbances by
adopting a robust term. It is shown that the proposed approach
is able to improve the lateral tracking performance gradually
through learning from previous control experiences, despite only
partial knowledge of the vehicle dynamics being available. It is
noteworthy that a novel technique targeting at the non-square
input distribution matrix is employed so as to deal with the
under-actuation property of the vehicle dynamics, which extends
the adaptive learning control theory from square systems to
non-square systems. Moreover, the convergence properties of
the RALC algorithm are analysed under the framework of
Lyapunov-like theory by virtue of the composite energy function
and the λ-norm. The effectiveness of the proposed control
scheme is verified by representative simulation examples and
comparisons with existing methods
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