Disturbance rejection based tracking control design for fuzzy switched systems with time‐varying delays and disturbances

Abstract

The disturbance rejection and tracking problem of T‐S fuzzy switched systems with uncertainties, input time‐varying delays and disturbances is addressed in this article. For that cause, a modified repetitive control protocol based on the improved‐equivalent‐input‐disturbance (IEID) estimator and extended Smith predictor approach has been proposed, which guarantees the perfect disturbance estimation and tracking performances with high precision. Specifically, by incorporating the transfer function of main feedback path in conventional Smith predictor block, the input time‐varying delays are effectively compensated. In the direction of estimating the disturbances, an active disturbance rejection technique called IEID estimator approach is precisely considered. By exploiting the output of the IEID estimator and parallel distributed compensation strategy, the fuzzy rule based modified repetitive control system is formulated. Further, by making use of Lyapunov method together with average dwell‐time approach, a set of sufficient conditions in the form of matrix inequalities are established. More respectively, by solving the developed matrix inequalities, the controller and observer gain matrices are determined. At last, the method proposed in this work is validated in terms of presenting the simulation results of two numerical examples, including boiler‐turbine system.

Design of delay compensation system for visual tracking control of industrial robots

“…In the design process, a large number of positive and negative samples are used for training, the mapping of the Gaussian kernel function space is completed and then computed, the sample loop matrix is constructed, the computation is simplified by Fourier transform, and the algorithm is embedded in the system to improve the real-time tracking and deskew correction in the welding process. In the literature [2] , the deep convolutional neural network is trained first, and then go to calculate each transmission image in the video image to get the image depth information as well as the related estimation, to complete the identification of the target area of the robot in the working area, and also to be able to complete the marking of the tracking direction of the target's movement. Literature [3] in the use of color features to determine the circular point of light, in the use of binocular vision to obtain the target point feature image, and substituting into the OpenCV technology function library, the optical flow method for function matching after the binarization threshold processing and segmentation of the background and the target, to achieve the visual tracking and localization of the target.…”

A new switching control for T‐S fuzzy systems with mixed time delays

“…Therefore, there are many research achievements for T-S fuzzy systems with time delay. [18][19][20][21][22] In Reference 18, Lyapunov functional and Leibniz-Newton formula are introduced to obtain the stability conditions of the system when mixed time-varying delay and interval distributed time-varying delay are considered at the same time. In Reference 20, an improved repetition control protocol based on an improved equivalent input disturbance estimator and an extended Smith predictor is proposed to solve the problem of time-varying input delay and disturbance.…”

“…In Reference 18, Lyapunov functional and Leibniz‐Newton formula are introduced to obtain the stability conditions of the system when mixed time‐varying delay and interval distributed time‐varying delay are considered at the same time. In Reference 20, an improved repetition control protocol based on an improved equivalent input disturbance estimator and an extended Smith predictor is proposed to solve the problem of time‐varying input delay and disturbance. For T‐S systems with distributed time delay, a new Lyapunov‐Krasovskii functional is constructed in Reference 21, and a state feedback control based on fuzzy memory is designed to obtain the conditions of asymptotic stability of the system.…”

“…In practical systems, various time delays are unavoidable. Therefore, there are many research achievements for T‐S fuzzy systems with time delay 18‐22 . In Reference 18, Lyapunov functional and Leibniz‐Newton formula are introduced to obtain the stability conditions of the system when mixed time‐varying delay and interval distributed time‐varying delay are considered at the same time.…”