Abstract: In this paper, we introduce a robust state feedback controller design using Linear Matrix Inequalities (LMIs) and guaranteed cost approach for Takagi-Sugeno fuzzy systems. The purpose on this work is to establish a systematic method to design controllers for a class of uncertain linear and non linear systems. Our approach utilizes a certain type of fuzzy systems that are based on Takagi-Sugeno (T-S) fuzzy models to approximate nonlinear systems. We use a robust control methodology to design controllers. This method not only guarantees stability, but also minimizes an upper bound on a linear quadratic performance measure. A simulation example is presented to show the effectiveness of this method.
Abstract: This paper proposes a methodology for analysis of
the dynamic behavior of a robotic manipulator in continuous
time. Initially this system (nonlinear system) will be decomposed
into linear submodels and analyzed in the context of the Linear
and Parameter Varying (LPV) Systems. The obtained linear
submodels, which represent the local dynamic behavior of the
robotic manipulator in some operating points were grouped in
a Takagi-Sugeno fuzzy structure. The obtained fuzzy model was
analyzed and validated through analog simulation, as universal
approximator of the robotic manipulator.
Abstract: This paper deals with the synthesis of fuzzy controller
applied to a permanent magnet synchronous machine (PMSM) with a
guaranteed H∞ performance. To design this fuzzy controller,
nonlinear model of the PMSM is approximated by Takagi-Sugeno
fuzzy model (T-S fuzzy model), then the so-called parallel
distributed compensation (PDC) is employed. Next, we derive the
property of the H∞ norm. The latter is cast in terms of linear matrix
inequalities (LMI-s) while minimizing the H∞ norm of the transfer
function between the disturbance and the error ( ) ev T . The
experimental and simulations results were conducted on a permanent
magnet synchronous machine to illustrate the effects of the fuzzy
modelling and the controller design via the PDC.
Abstract: This paper investigates the problem of designing a robust state-feedback controller for a class of uncertain Markovian jump nonlinear systems that guarantees the L2-gain from an exogenous input to a regulated output is less than or equal to a prescribed value. First, we approximate this class of uncertain Markovian jump nonlinear systems by a class of uncertain Takagi-Sugeno fuzzy models with Markovian jumps. Then, based on an LMI approach, LMI-based sufficient conditions for the uncertain Markovian jump nonlinear systems to have an H performance are derived. An illustrative example is used to illustrate the effectiveness of the proposed design techniques.