In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

This paper is devoted to event-triggered stabilization for a class of interval type-2 (IT2) fuzzy systems with aperiodic sampling. Firstly, the IT2 Takagi-Sugeno fuzzy model and sampled-data controllers are established subject to mismatched membership functions. Secondly, considering non-uniform sampling case, a refined adaptive event-triggered communication scheme (RAETCS) is proposed in a hierarchy form to dynamically adjust the direction and rate of event-triggered threshold parameter by state changing trend and relative state error, respectively. Thus, a complete dual-directional regulating mechanism with sensitivity to state variation is reasonably created to give extra flexibility, which is beneficial for preferable tradeoff between control performance and network resource. Thirdly, taking the practical behaviors on the sampling interval $[i_c, i_{c+1}]$ into account, a novel integral type of time-dependent Lyapunov function is constructed, in which not only the information from $x(i_c)$ to $x(t)$ , but also that from $x(t)$ to $x(i_{c+1})$ are fully exploited. Moreover, by virtue of characteristics for looped function, the positive requirement for individual Lyapunov matrix is further relaxed to facilitate more feasible space. Then, the stability criterion and controller design approach are derived based on the Wirtinger-based integral inequality with slack variables. Finally, the numerical examples are provided to demonstrate the effectiveness and advantages of the proposed methods.

In this paper, we will investigate the problem of finite frequency memory fixed-order output feedback controller design for Takagi-Sugeno (T-S) fuzzy affine systems. It is assumed that the disturbances reside in a finite frequency range, i.e., the low, middle, or high frequency range. The objective is to design a memory piecewise affine controller by using past output measurements to guarantee the asymptotic stability of the resulting closed-loop system with prescribed finite frequency H-infinity performance. Via the system state-input augmentation, a novel descriptor system approach is proposed to facilitate the controller design. All the design conditions are formulated in the form of linear matrix inequalities (LMIs). It is also proven that the H1 performance can be improved with the memory control strategy. Finally, simulation studies are presented to show the effectiveness of the proposed design method.

This paper considers the problem of asynchronous fault detection filter (FDF) design for T-S fuzzy singular systems via dynamic event-triggered scheme. A mode-dependent dynamic event-triggered scheme is adopted to alleviate the communication load. Besides, a hidden Markov model is introduced to describe the asynchronous phenomenon between the system and the FDF. Firstly, some sufficient criteria are established to ensure that the residual system is stochastically admissible (SA) with a certain $H_\infty$ performance. Secondly, solvability criteria are presented to codesign the desired FDF gains and the event-triggered matrices. Finally, the correctness of the proposed method is shown by two examples.

This paper tackles the problem of piecewise affine memory filtering design for the discrete-time norm-bounded uncertain Takagi-Sugeno fuzzy affine systems. The objective is to design an admissible filter using past output measurements of the system, guaranteeing the asymptotic stability of the filtering error system with a given [Formula: see text] performance index. Based on the piecewise fuzzy Lyapunov functions and the projection lemma, a new sufficient condition for [Formula: see text] filtering performance analysis is first derived, and then the filter synthesis is carried out. It is shown that the filter gains can be obtained by solving a set of linear matrix inequalities. In addition, it is also shown that the filtering performance can be improved with the increasing number of past output measurements used in the filtering design. Finally, two examples are presented to show the advantages and effectiveness of the proposed approach.

This paper focuses on the problem of fault detection filtering design for discrete-time interval type-2 Takagi-Sugeno (T-S) fuzzy systems in finite frequency domain. Considering the fact that external disturbances and faults are usually reside in finite frequency ranges, the finite frequency H and H- performances are introduced to reflect the disturbance robustness and fault sensitiveness in finite frequency domain, respectively. Based on discrete-time Fourier transform and its properties, finite frequency performance analysis results are firstly obtained. Then, by exploiting the information on upper and lower membership functions, the membership-function-dependent filtering design conditions in the form of linear matrix inequalities (LMIs) are established for discrete-time interval type-2 T-S fuzzy systems in finite frequency domain. With the obtained filter, a fault detection scheme is then proposed and it is shown that the resulting fault detection system is asymptotically stable with prescribed finite frequency H and H- performances. Finally, the effectiveness of the proposed method is validated by simulation studies.