Synthesis of a relay regulator with fuzzy switching times on the basis of a macro-variable

A method for the identification of the dynamic regimes of a controlled object on the basis of an analysis of a macrovariable of the form, square of the norm of the variable states, and its time derivatives is proposed. Fuzzy if-then rules for reversal of a relay control are constructed on this basis. Based on the total number of positions of the relay, all possible regimes are divided into “correct” and “incorrect.” The control strategy consists in control reversal upon the appearance of an “incorrect” motion regime.     

A new approach to stabilization of high‐order nonlinear systems with an asymmetric output constraint

This paper is concerned with the problem of state feedback stabilization for a class of high‐order nonlinear systems with an asymmetric output constraint. A novel asymmetric barrier Lyapunov function (BLF) is first proposed by deliberating the characteristics of the system nonlinearities. Then, the presented BLF, together with a skillful manipulation of sign functions, is utilized to delicately revamp the technique of adding a power integrator, thereby developing a systematic approach that guides us in constructing a continuous state feedback stabilizer and preventing the violation of a pre‐specified asymmetric output constraint during operation. The novelty of this paper is attributed to the development of a unified method that is able to simultaneously tackle the problem of stabilization for high‐order nonlinear systems with and without output constraints in a constructive fashion, without changing the controller structure. An illustrative example is presented to demonstrate the superiority of the proposed approach.     

多粒度犹豫模糊语言环境下未知权重的多属性群推荐方法

针对群推荐中存在的多粒度、犹豫性、模糊性语言信息问题, 提出多粒度犹豫模糊语言环境下未知权重的多属性群推荐方法. 首先, 提出多粒度犹豫模糊语言术语集的概念, 定义其距离公式; 然后, 在多粒度犹豫模糊语言环境下, 针对属性权重完全未知的情况, 建立目标规划模型, 利用拉格朗日方程求解, 针对属性权重不完全未知的情况, 建立线性规划模型求解; 最后, 通过算例计算和分析表明了上述模型求解权重问题是有效的.

     

Adaptive fuzzy dynamic surface control for a class of perturbed nonlinear time-varying delay systems with unknown dead-zone

In this paper,adaptive dynamic surface control(DSC) is developed for a class of nonlinear systems with unknown discrete and distributed time-varying delays and unknown dead-zone.Fuzzy logic systems are used to approximate the unknown nonlinear functions.Then,by combining the backstepping technique and the appropriate Lyapunov-Krasovskii functionals with the dynamic surface control approach,the adaptive fuzzy tracking controller is designed.Our development is able to eliminate the problem of "explosion of complexity" inherent in the existing backstepping-based methods.The main advantages of our approach include:1) for the n-th-order nonlinear systems,only one parameter needs to be adjusted online in the controller design procedure,which reduces the computation burden greatly.Moreover,the input of the dead-zone with only one adjusted parameter is much simpler than the ones in the existing results;2) the proposed control scheme does not need to know the time delays and their upper bounds.It is proven that the proposed design method is able to guarantee that all the signals in the closed-loop system are bounded and the tracking error is smaller than a prescribed error bound,Finally,simulation results demonstrate the effectiveness of the proposed approach.     

A new approach to stabilisation of a class of nonlinear systems with an output constraint

ABSTRACT

This paper investigates the problem of state-feedback stabilisation for a class of high-order nonlinear systems with an output constraint. A novel tangent-type barrier Lyapunov function is first developed to cope with the output constraint. Then, by introducing the sign function and incorporating the developed tangent-type barrier Lyapunov function, the celebrated adding a power integrator technique is revamped to systematically design a continuous state feedback stabilising controller that prevents violation of the output constraint during operation. The novelty of this paper is the development of an unified design procedure, which can tackle the stabilisation task of the systems with/without the output constraint simultaneously.     

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection (CMSS) approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.