This paper investigates the problem of adaptive integral sliding mode control for general Takagi–Sugeno fuzzy systems with matched uncertainties and its applications. Different control input matrices are allowed in fuzzy systems. The matched uncertainty is modeled in a unified form, which can be handled by the adaptive methodology. A fuzzy integral-type sliding surface is utilized and the parameter matrices can be determined according to user's requirement. Based on the designed sliding surface, a new sliding mode controller is proposed, and the structure of the controller depends on the difference between the disturbance input matrices and the control input matrices. It is shown that under the proposed sliding mode controller, the resulting closed-loop system can achieve the uniformly ultimate boundedness. Furthermore, simulation examples are presented to show the merit and applicability of the proposed fuzzy sliding mode control method.

Adaptive Sliding Mode Control for Takagi–Sugeno Fuzzy Systems and Its Applications

This paper is concerned with the problem of nonfragile H∞ filtering for continuous-time Takagi-Sugeno (T-S) fuzzy systems. The filter to be designed is assumed to have two types of multiplicative gain variations. First, two relaxed H∞ filtering analysis conditions are proposed based on useful linear matrix inequality preliminaries. Whereafter, the results are exploited to derive sufficient conditions for designing a nonfragile H∞ filter, which guarantees a prescribed H∞ performance of the filtering error system. Compared with the existing results, the proposed design methods not only suit for a standard form of the fuzzy filter but also give more relaxed design conditions. Finally, simulation examples will be given to show the efficiency of the proposed design methods.

Nonfragile $H_{\infty}$ Filter Design for T–S Fuzzy Systems in Standard Form

This paper addresses the problems of observer-based fault reconstruction and fault-tolerant control for Takagi–Sugeno fuzzy descriptor systems subject to time delays and external disturbances. A novel fuzzy descriptor learning observer is constructed to achieve simultaneous reconstruction of system states and actuator faults. Sufficient conditions for the existence of the proposed observer are explicitly provided. Utilizing the reconstructed fault information, a reconfigurable fuzzy fault-tolerant controller based on the separation property is designed to compensate for the impact of actuator faults on system performance by stabilizing the closed-loop system. In addition, the design of the fault reconstruction observer and the fault-tolerant controller is formulated in terms of linear matrix inequalities that can be conveniently solved using convex optimization techniques. Finally, simulation results on a truck–trailer system are presented to verify the effectiveness of the proposed approaches.

Fault Reconstruction and Fault-Tolerant Control via Learning Observers in Takagi–Sugeno Fuzzy Descriptor Systems With Time Delays

This paper is concerned with the problems of state and output feedback control for interval type-2 (IT2) fuzzy systems with mismatched membership functions. The IT2 fuzzy model and the IT2 state and output feedback controllers do not share the same membership functions. A novel performance index, which is expressed as an extended dissipativity performance, is introduced to be a generalization of $H_{\infty }$ , $L_{2}$ – $L_{\infty }$ , passive, and dissipativity performances indexes. First, the IT2 Takagi–Sugeno fuzzy model and the controllers are constructed by considering the mismatched membership functions. Second, on the basis of Lyapunov stability theory, the IT2 fuzzy state and output feedback controllers are designed, respectively, to guarantee that the closed-loop system is asymptotically stable with extended dissipativity performance. The existence conditions of the two kinds of controllers are obtained in terms of convex optimization problems, which can be solved by standard software. Finally, simulation results are provided to illustrate the effectiveness of the proposed methods.

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State and Output Feedback Control of Interval Type-2 Fuzzy Systems With Mismatched Membership Functions

Underactuated robotics is an emerging research direction in the field of robotics. The control input of the underactuated robot is less than the degree of freedom of the system. It has the advantag...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419862164

Underactuated robotics: A review:

This paper aims to investigate the formation control of multi-robot systems, where the first-order kinematic model of a differential wheeled robot is considered. Based on the graph theory and consensus algorithm, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. Simulations are adopted to verify the feasibility of proposed techniques. From simulation results, the proposed adaptive neural fuzzy controller can provide better formation responses compared to conventional consensus algorithm.

Design of adaptive neural fuzzy formation controller for multi-robot systems

There are a large number of power distribution transformer stations due to it is far away from city, cable-line, wireless network, GPRS, and 3.5G single transmission device provides a good communication solution to supervise power transformer stations. This paper develops a novel device used to monitor on-line signal for power transformers. The measured-data is captured from multi-sensors and stored in the server equipment database. Besides, these data is used for the prediction of the power transformer life consumption. The transformer life consumption is satisfied with IEEE Standard. Various faults could occur in a transformer such as overload current, overheating, partial discharge and arcing, which can diagnose various fault-related conditions. According to the conventional way of the dissolved gas analysis, the fault is probably determined. As researcher knows, the IEC codes cannot determine the fault in many cases. Therefore, this paper presents a fuzzy logic tool that can be used to diagnosis multiple faults in a transformer and monitor the trend. It has been proved to be a very useful tool for transformer diagnosis and customer servicing.

http://www.wseas.us/e-library/conferences/2011/Cambridge/NEHIPISIC/NEHIPISIC-43.pdf

Smart three-phase power transformer utilizing fuzzy logic approach

This paper presents a distributed adaptive formation control method for a class of uncertain multiple Euler-Lagrange systems. The proposed approach is based on the graph theory and an adaptive dynamic surface control, where the system uncertainties are approximately modelled by interval type-2 neuro-fuzzy networks. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all agents reach a desired formation following a designated trajectory. In addition to simulation example, the proposed method is applied to each agents of Euler-Lagrange dynamics for performance evaluations. Simulation results indicate that the proposed control scheme has superior responses compared to distributed dynamic surface formation control.

Wei-Shou Chan

Papers

Design of adaptive neural fuzzy formation controller for multi-robot systems

Smart three-phase power transformer utilizing fuzzy logic approach

Formation control for uncertain multiple Euler-Lagrange systems with dynamic surface control and interval type-2 neuro-fuzzy networks