E-mail: maurizio.melluso@unipa.it .
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page:
www.unipa.it/fmraimondi/melluso/melluso.htm
Tutor:
Prof. Francesco Maria Raimondi, Dipartimento di
Ingegneria dell’Automazione e dei Sistemi, Università di Palermo
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Fuzzy Lyapunov/EKF
intelligent Control for Automatic Motion of Ground Vehicles with Uncertainties
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Advisor: Prof. Francesco
Maria Raimondi, Dipartimento di Ingegneria dell’Automazione
e dei Sistemi, Università di Palermo
Summary of the
thesis
Fuzzy adaptive motion control system
including on-line Extended Kalman’s filter (EKF) for
wheeled underactuated electrical vehicles with nonholonomic constraints on the motion has been developed.
The presence of parametric uncertainties both in the kinematics and in the
dynamics of the vehicle is treated using suitably differential adaptation laws.
We merge adaptive control with fuzzy inference system. By using fuzzy system,
the parameters of the kinematical controller are functions of the lateral,
longitudinal and orientation errors of the motion. In this way we have a robust
control system where the dynamics of the motion errors is with lower time response than the adaptive control without fuzzy. Also Lyapunov’s
stability of the motion errors has been proved based on the properties of the
Fuzzy maps. If data from incremental encoders are employed
for the feedback directly, sensors noises can
damage the performance of the motion control in terms of the motion
errors and of the parametric adaptation. The noises above are aleatory and denote kind of nonparametric uncertainties
which perturb the nominal model of the car. Therefore an EKF has been
inserted in the adaptive control system
to compensate the non parametric uncertainties above. The control algorithm efficiency has been confirmed
through simulation experiments in Matlab environment.
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