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In this paper, the controllability property for a class of Takagi-Sugeno (T-S) fuzzy models is analyzed, while a fully nonlinear stabilizer is designed in a practical way. It is shown that global fuzzy stabilizers can be constructed in a nonconservative way by means of a relatively simple approach. The existence of such controllers depends on the fuzzy controllability conditions, which are derived in a straightforward way. The main advantage of the proposed approach is that the convergence of the closedloop system can be imposed arbitrarily. Some examples are given in order to illustrate the validity of the method. Finally, the proposed controller is applied on an underactuated system known as "pendubot" and the results are compared with an stabilizer designed on the basis of LMIs. Index Terms-FuzzyAckermann's formula, fuzzy controllability, fuzzy stability, Takagi-Sugeno (T-S) fuzzy models.

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In this work, the design, manufacturing, instrumentation, and application of a two-finger exoskeleton with force feedback are presented. The exoskeleton is based on remote center of motion mechanisms in order to avoid mechanical interference with the user’s fingers and is manufactured by three-dimensional printing. The developed exoskeleton is applied in a mobile robot teleoperation by mapping the finger movements in forward and turning commands for the robot. The presence of obstacles detected by the robot is sensed by the user by means of a feedback force. The problem of simultaneously communicating a data acquisition card and the robot hardware by MATLAB ® Simulink® was solved by using an external Wi-Fi module. The result is a lightweight exoskeleton which is able to communicate bidirectionally with a mobile robot by a personal computer for teleoperation tasks. The success of the system implementation is proven by a set of experiments presented in the final part of the article.

In this note, the problem of tracking random references and rejecting random perturbations in a quadrotor, both generated by an auxiliary system named exosystem, is solved by extending the deterministic tracking problem to the area of stochastic processes. Besides, it is considered that only a part of the state vector of the quadrotor is available through measurements. As a consequence, the state vector of the plant must be estimated in order to close the control loop. On this basis, a controller to track random references and to reject random perturbations is developed by combining a Kalman filter to estimate the references and perturbations of an exosystem and an observer to estimate the states of a quadrotor. Besides, to obtain a more practical controller, the analysis is carried out in discrete time. Numerical simulations are used in a quadrotor to confirm the validity and effectiveness of the proposed control.

The exact output regulation problem for Takagi-Sugeno (TS) fuzzy models, designed from linear local subsystems, may have a solution if input matrices are the same for every local linear subsystem. Unfortunately, such a condition is difficult to accomplish in general. Therefore, in this work, an adaptive network-based fuzzy inference system (ANFIS) is integrated into the fuzzy controller in order to obtain the optimal fuzzy membership functions yielding adequate combination of the local regulators such that the output regulation error in steady-state is reduced, avoiding in this way the aforementioned condition. In comparison with the steepest descent method employed for tuning fuzzy controllers, ANFIS approximates the mappings between local regulators with membership functions which are not necessary known functions as Gaussian bell (gbell), sigmoidal, and triangular membership functions. Due to the structure of the fuzzy controller, Levenberg-Marquardt method is employed during the training of ANFIS.