This paper addresses the problem of stabilizing a class of nonlinear systems subject to Markovian jump parameters using a robust stochastic fuzzy controller with H_infin performance. The class of jump nonlinear systems considered is described by a fuzzy model composed of two levels: A crisp level which represents the jumps and a fuzzy level which represents the system nonlinearities. Considering the approximation error between the fuzzy model and the jump nonlinear system as norm-bounded uncertainties, we develop a systematic technique based on a coupled Lyapunov function to obtain a robust stochastic fuzzy controller which guarantees the L₂ gain of the closed-loop system in respect to external inputs to be equal to or less than a prescribed value. A simulation example on an industrial power plant operating in a cogeneration scheme is presented to illustrate the effectiveness of the proposed stabilizing controller in reducing oscillations as well as maintaining a desired operation condition in the presence of fluctuations in the local load

In this article, the fuzzy Lyapunov function approach is considered for stabilising continuous-time Takagi-Sugeno fuzzy systems. Previous linear matrix inequality (LMI) stability conditions are relaxed by exploring further the properties of the time derivatives of premise membership functions and by introducing slack LMI variables into the problem formulation. The relaxation conditions given can also be used with a class of fuzzy Lyapunov functions which also depends on the membership function first-order time-derivative. The stability results are thus extended to systems with large number of rules under membership function order relations and used to design parallel-distributed compensation (PDC) fuzzy controllers which are also solved in terms of LMIs. Numerical examples illustrate the efficiency of the new stabilising conditions presented.

In this work, a robust static output feedback (SOF) design for continuous-time nonlinear systems under norm bounded uncertainties, described as a Takagi–Sugeno (TS) system is proposed. The key features are that a switching control law is used to activate the SOF gains to stabilize the uncertain TS fuzzy system and the SOF design does not require the measurements of all premise variables in real time as in the standard fuzzy parallel distributed compensation design. The main result is given in terms of linear matrix inequalities, which guarantees a ${\mathcal {H}}_{\infty }$ attenuation level of $\gamma$ for the SOF control system. The efficiency of the main results is illustrated with examples, including a nonlinear active suspension system under uncertain driver masses, and compared with other published results from the literature.