Controle por modos deslizantes com compensação difusa aplicado a sistemas com descontinuidade

The development of non-linear controllers gained space in the theoretical ambit and of practical applications on the moment that the arising of digital computers enabled the implementation of these methodologies. In comparison with the linear controllers more utilized, the non -linear controllers present the advantage of not requiring the linearity of the system to determine the parameters of control, which permits a more efficient control especially when the system presents a high level of non-linearity. Another additional advantage is the reduction of costs, since to obtain the efficient control through linear controllers it is necessary the utilization of sensors and more refined actuators than when it is utilized a non-linear controller. Among the non-linear theories of control, the method of control by gliding ways is detached for being a method that presents more robustness, before uncertainties. It is already confirmed that the adoption of compensation on the region of residual error permits to improve better the performance of these controllers. So, in this work it is described the development of a non-linear controller that looks for an association of strategy of control by gliding ways, with the fuzzy compensation technique. Through the implementation of some strategies of fuzzy compensation, it was searched the one which provided the biggest efficiency before a system with high level of nonlinearities and uncertainties. The electrohydraulic actuator was utilized as an example of research, and the results appoint to two configurations of compensation that permit a bigger reduction of the residual error

Stabilizing controller design for uncertain nonlinear systems using fuzzy models

A Lyapunov-based stabilizing control design method for uncertain nonlinear dynamical systems using fuzzy models is proposed. The controller is constructed using a design model of the dynamical process to be controlled. The design model is obtained from the truth model using a fuzzy modeling approach. The truth model represents a detailed description of the process dynamics. The truth model is used in a simulation experiment to evaluate the performance of the controller design. A method for generating local models that constitute the design model is proposed. Sufficient conditions for stability and stabilizability of fuzzy models using fuzzy state-feedback controllers are given. The results obtained are illustrated with a numerical example involving a four-dimensional nonlinear model of a stick balancer.

On relaxed LMI-based design for fuzzy controllers

Relaxed conditions for stability of nonlinear continuous-time systems given by fuzzy models axe presented. A theoretical analysis shows that the proposed method provides better or at least the same results of the methods presented in the literature. Digital simulations exemplify this fact. This result is also used for fuzzy regulators design. The nonlinear systems are represented by fuzzy models proposed by Takagi and Sugeno. The stability analysis and the design of controllers axe described by LMIs (Linear Matrix Inequalities), that can be solved efficiently using convex programming techniques.

On relaxed LMI-based designs for fuzzy regulators and fuzzy observers

Relaxed conditions for stability of nonlinear, continuous and discrete-time systems given by fuzzy models are presented. A theoretical analysis shows that the proposed methods provide better or at least the same results of the methods presented in the literature. Numerical results exemplify this fact. These results are also used for fuzzy regulators and observers designs. The nonlinear systems are represented by fuzzy models proposed by Takagi and Sugeno. The stability analysis and the design of controllers are described by linear matrix inequalities, that can be solved efficiently using convex programming techniques. The specification of the decay rate, constrains on control input and output are also discussed.

Design of fuzzy regulators with optimal initial conditions compensation

In almost all cases, the goal of the design of automatic control systems is to obtain the parameters of the controllers, which are described by differential equations. In general, the controller is artificially built and it is possible to update its initial conditions. In the design of optimal quadratic regulators, the initial conditions of the controller can be changed in an optimal way and they can improve the performance of the controlled system. Following this idea, a LNU-based design procedure to update the initial conditions of PI controllers, considering the nonlinear plant described by Takagi-Sugeno fuzzy models, is presented. The importance of the proposed method is that it also allows other specifications, such as, the decay rate and constraints on control input and output. The application in the control of an inverted pendulum illustrates the effectively of proposed method.

Controle ótimo H∞ de sistemas não-lineares com modelos fuzzy takagi-sugeno

Neste trabalho é proposta uma metodologia de rastreamento de sinais e rejeição de distúrbios aplicada a sistemas não-lineares. Para o projeto do sistema de rastreamento, projeta-se os controladores fuzzy M(a) e N(a) que minimizam o limitante superior da norma H∞ entre o sinal de referência r(t) e o sinal de erro de rastreamento e(t), sendo e(t) a diferença entre a entrada de referência e a saída do sistema z(t). No método de rejeição de distúrbio utiliza-se a realimentação dinâmica da saída através de um controlador fuzzy Kc(a) que minimiza o limitante superior da norma H∞ entre o sinal de entrada exógena w(t) e o sinal de saída z(t). O procedimento de projeto proposto considera as não-linearidades da planta através dos modelos fuzzy Takagi-Sugeno. Os métodos são equacionados utilizando-se inequações matriciais lineares (LMIs), que quando factíveis, podem ser facilmente solucionados por algoritmos de convergência polinomial. Por fim, um exemplo ilustra a viabilidade da metodologia proposta.

Design of a Takagi-Sugeno Fuzzy Regulator for a Set of Operation Points

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

Statements on chaos control designs, including a fractional order dynamical system, applied to a MEMS comb-drive actuator

In this work, we deal with a micro electromechanical system (MEMS), represented by a micro-accelerometer. Through numerical simulations, it was found that for certain parameters, the system has a chaotic behavior. The chaotic behaviors in a fractional order are also studied numerically, by historical time and phase portraits, and the results are validated by the existence of positive maximal Lyapunov exponent. Three control strategies are used for controlling the trajectory of the system: State Dependent Riccati Equation (SDRE) Control, Optimal Linear Feedback Control, and Fuzzy Sliding Mode Control. The controls proved effective in controlling the trajectory of the system studied and robust in the presence of parametric errors.

Multivalued adaptive neuro-fuzzy controller for robot vehicle - art. no. 1406

Robotic vehicle navigation in unstructured and uncertain environments is still a challenge. This paper presents the implementation of a multivalued neurofuzzy controller for autonomous ground vehicle (AGVs) in indoor environments. The control system consists of a hierarchy of mobile robot using multivalued adaptive neuro-fuzzy inference system behaviors.

Design of a fuzzy Takagi-Sugeno controller to vary the joint knee angle of paraplegic patients

The papers shows, through theoretical studies and simulations, that using the description of the plant by Takagi-Sugeno (T-S), it is possible to design a nonlinear controller to control the position of the leg of a paraplegic patient. The control system was designed to change the angle of the joint knee of 60 degrees. This is the first study that describes the application of Takagi-Sugeno (T-S) models in this kind of problem.

Design of a fuzzy controller with simplified architecture

This work presents the design of a fuzzy controller with simplified architecture. This architecture tries to minimize the time processing used in? the several stages of hazy modeling of systems and processes. The basic procedures of fuzzification and defuzzification are simplified to the maximum while the inference procedures are computed in private way. Therefore, the simplified architecture allows a fast and easy configuration of the fuzzy controller.All rules that define the control actions are determined by inference procedures and the defuzzification is made automatically using a simplified algorithm. The fuzzy controller operation is standardized and the control actions are previously calculated For general-purpose application? ann results, the industrial systems of fluid pow cona ol will be considered.

Urban Traffic Control and Monitoring - An Approach for the Brazilian Intelligent Cities Project

This paper describes an urban traffic control system which aims at contributing to a more efficient traffic management system in the cities of Brazil. It uses fuzzy sets, case-based reasoning, and genetic algorithms to handle dynamic and unpredictable traffic scenarios, as well as uncertain, incomplete, and inconsistent information. The system is composed by one supervisor and several controller agents, which cooperate with each other to improve the system's results through Artificial Intelligence Techniques.

Novos resultados sobre a estabilidade e controle de sistemas nao-lineares utilizando modelos fuzzy e LMI

Relaxed conditions for the stability study of nonlinear, continuous and discrete-time systems given by fuzzy models are presented. A theoretical analysis shows that the proposed method provides better or at least the same results of the methods presented in the literature. Digital simulations exemplify this fact. These results are also used for the fuzzy regulators design. The nonlinear systems are represented by the fuzzy models proposed by Takagi and Sugeno. The stability analysis and the design of controllers are described by LMIs (Linear Matrix Inequalities), that can be solved efficiently by convex programming techniques. The specification of the decay rate, constraints on control input and output are also described by LMIs. Finally, the proposed design method is applied in the control of an inverted pendulum.