An approach to design stabilizers and observers for a class of Multiple-Input Multiple-Output (MIMO) Takagi-Sugeno (TS) fuzzy models is developed on the basis of local gains and the searching for a set of interpolation functions capable of properly combining the aforementioned local gains. As expected, the existence of such interpolation functions depends on the controllability and observability properties of the overall multivariable TS fuzzy model. For that reason, practical controllability and observability tests are also proposed for MIMO TS fuzzy systems. Some numerical simulations are used in order to validate the efficacy of the method. Besides, the results are compared with an approach based on Linear matrix Inequalities (LMIs), namely Parallel Distributed Compensation (PDC).

This paper presents a biomimetic prototype of a mobile robot that can be used to inspect the subdrainage conditions of pipelines located along different highways in Mexico. Computer-aided design tools have been used to size each of the prototype components as inspired by anatomical spider structure. Springs are integrated to generate proper contact pressure against the pipe walls. The robot locomotion system is implemented with adaptable behaviour for the irregularities of pipelines along its journey. The robot prototype is manufactured in 3D printing with the advantage of having its spare parts easily replaceable. Reported results show internal pipe status through a mini video camera on the top of the robot.

Based on the general structure of mechanical systems described by their state-space representation, the Takagi-Sugeno fuzzy modelling, and the controllability property of fuzzy systems, an algebraic and practical approach to compute the fuzzy gain capable of ensuring the stability property of the Takagi-Sugeno fuzzy model is proposed. The main idea consists of finding a continuous fuzzy gain such that, any linear behavior, defined by the adequate selection of eigenvalues, is induced in the closed-loop fuzzy system. Therefore, by continuity, if the fuzzy model is an approximation sufficiently close to the mechanical system, then such a nonlinear system is also stabilized by the fuzzy controller. A notable advantage of the proposed method, when compared with similar approaches, is the simplicity of the resulting gain. The validity of the approach is illustrated through the numerical simulation of a sufficiently complex nonlinear system.

This research introduces a physics-based identification technique utilizing genetic algorithms. The primary objective is to derive a parametric matrix, denoted as A, describing the time-invariant linear model governing the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests that the genetic algorithm provided with the adequate fitness function could be an appealing alternative for aircraft model identification, even when limited data are available. Furthermore, in some cases, linearization using a genetic algorithm can be more efficient than classical methods.

In this work, a novel methodology for the identification of stiffness and damping rotordynamic coefficients in a rotor-bearing system is proposed. The mathematical model for the identification process is based on the algebraic identification technique applied to a finite element (FE) model of a rotor-bearing system with multiple degree-of-freedom (DOF). This model considers the effects of rotational inertia, gyroscopic moments, shear deformations, external damping and linear forces attributable to stiffness and damping parameters of the supports. The proposed identifier only requires the system’s vibration response as input data. The performance of the proposed identifier is evaluated and analyzed for both schemes, constant and variable rotational speed of the rotor-bearing system, and numerical results are obtained. In the presented results, it can be observed that the proposed identifier accurately determines the stiffness and damping parameters of the bearings in less than 0.06 s. Moreover, the identification procedure rapidly converges to the estimated values in both tested conditions, constant and variable rotational speed.

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.