Adaptación paramétrica de un sistema borroso mediante el filtro de Kalman extendido

Cuando se pretende analizar o controlar un sistema cuyo modelo se ha obtenido ´únicamente en base a datos de entrada/salida, es fundamental la precisión en el modelo. Por otro lado, para que el procedimiento sea practico, la etapa de modelado ha de ser eficiente computacionalmente hablando. En este sentido, se presenta en este trabajo la aplicación del filtro de Kalman extendido para la adaptación paramétrica de un modelo borros

Design of Explicit Fuzzy Prediction Controller for Constrained Nonlinear Systems

This paper presents an explicit fuzzy predictive control method for a class of nonlinear systems with constrained inputs. The main idea is to construct a terminal invariant set and explicit predictive controller with affine input on the basis of T-S fuzzy model. This method need not compute the complex nonconvex nonlinear programming problem of earlier nonlinear predictive control methods and decreases the number of optimization variables and guarantees stability of the closed-loop system. The simulation results on a numerical example show the validity of the method presented in this paper

Global Feed-Forward Adaptive Fuzzy Control of Uncertain MIMO Nonlinear Systems

This study proposes a novel adaptive control approach using a feedforward Takagi-Sugeno (TS) fuzzy approximator for a class of highly unknown multi-input multi-output (MIMO) nonlinear plants. First of all, the design concept, namely, feedforward fuzzy approximator (FFA) based control, is introduced to compensate the unknown feedforward terms required during steady state via a forward TS fuzzy system which takes the desired commands as the input variables. Different from the traditional fuzzy approximation approaches, this scheme allows easier implementation and drops the boundedness assumption on fuzzy universal approximation errors. Furthermore, the controller is synthesized to assure either the disturbance attenuation or the attenuation of both disturbances and estimated fuzzy parameter errors or globally asymptotic stable tracking. In addition, all the stability is guaranteed from a feasible gain solution of the derived linear matrix inequality (LMI). Meanwhile, the highly uncertain holonomic constrained systems are taken as applications with either guaranteed robust tracking performances or asymptotic stability in a global sense. It is demonstrated that the proposed adaptive control is easily and straightforwardly extended to the robust TS FFA-based motion/force tracking controller. Finally, two planar robots transporting a common object is taken as an application example to show the expected performance. The comparison between the proposed and traditional adaptive fuzzy control schemes is also performed in numerical simulations. Keywords: Adaptive control; Takagi-Sugeno (TS) fuzzy system; holonomic systems; motion/force control

Fuzzy Prediction for Traffic Flow Based on Delta Test

This paper presents a novel approach to one-step-forward prediction of traffic flow based on fuzzy reasoning. The successful construction of a competent fuzzy inference system of Sugeno type largely relies on proper choice of input dimension and accurate estimation of structure parameters and rules. The first issue is addressed with a proposed method, based on δ-test, which can simultaneously determine input dimension and reduce noise level. In response to the second issue, two clustering techniques, based on nearest-neighbor clustering and Gaussian mixture models, are successively employed to determine the antecedent parameters and rules, and the estimation for the consequent parameters is achieved by the least square estimation technique. A number of experiments have been performed on the one-week data of traffic flow to evaluate the proposed approach in terms of denosing, prediction performances, overfitting, and so forth. The experimental results have demonstrated that the proposed prediction approach is effective in removing noise and constructing a competent and compact fuzzy inference system without significant overfitting

Time-Delay Systems

Time delay is very often encountered in various technical systems, such as electric, pneumatic and hydraulic networks, chemical processes, long transmission lines, robotics, etc. The existence of pure time lag, regardless if it is present in the control or/and the state, may cause undesirable system transient response, or even instability. Consequently, the problem of controllability, observability, robustness, optimization, adaptive control, pole placement and particularly stability and robustness stabilization for this class of systems, has been one of the main interests for many scientists and researchers during the last five decades