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This paper aims to address the robust controller design problem for a class of fuzzy C-means clustering algorithm that is robust against both the plant parameter perturbations and controller gain variations. Based on Takagi–Sugeno (T-S) fuzzy model description, the stability and control problems of nonlinear systems are studied.

A recently proposed integral inequality is selected based on the free-weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs).

Under the premise that the controller and the system share the same, the method does not require the number of membership functions and rules.

Furthermore, the modified controller in a large-scale nonlinear system is utilized as a stability criterion for a closed-loop T-S fuzzy system obtained by LMI, and is rearranged by a machine learning membership function.

The closed-loop controller criterion is derived by energy functions to guarantee the stability of systems. Finally, an example is given to demonstrate the results.

Based on laser-range-finder (LRF) sensing, the control design of location and orientation stabilization for the mobile robot is investigated. However, the practical limitation of the LRF sensing is usually ignored in the control design, which leads to incorrect localization and unexpected control results. The purpose of this study is to design the fuzzy controller subject to the practical limitation on the LRF-based localization for a differentially driven wheeled mobile robot.

First, the Takagi–Sugeno (T-S) fuzzy model is derived from the polar kinematic model of a differentially driven mobile robot. Then, the fuzzy controller is designed to the derived T-S fuzzy kinematic model in accordance with the Lyapunov stabilization theorem. The derived Lyapunov stabilization conditions for the fuzzy control design are expressed as the linear matrix inequality (LMI) form and effectively solved by LMI tools. The practical limitation on the LRF-based localization is also expressed as the LMI form and simultaneously solved with the control design.

The location and posture stabilization experiments are carried out on a mobile robot with LRF-based localization to prove the effectiveness of the proposed T-S fuzzy model-based control design. Furthermore, the ground truth experiment evaluates the accuracy of LRF-based localization.

The contribution of this study is to develop the fuzzy control law for a differentially driven wheeled mobile robot under the practical limitation on LRF-based localization. The proposed control design can be applied to other robots with practical limitations on the sensors.

The purpose of this paper is to deal with the stabilization of the continuous Takagi Sugeno (TS) fuzzy models using their discretized forms based on the decay rate performance approach.

This approach is structured as follows: first, a discrete model is obtained from the discretization of the continuous TS fuzzy model. The discretized model is obtained from the Euler approximation method which is used for several orders. Second, based on the decay rate stabilization conditions, the gains of a non-PDC control law ensuring the stabilization of the discrete model are determined. Third by keeping the values of the gains, the authors determine the values of the performance criterion and the authors check by simulation the stability of the continuous TS fuzzy models through the zero order hold.

The proposed idea lead to compare the performance continuous stability results with the literature. The comparison is, also, taken between the quadratic and non-quadratic cases.

Therefore, the originality of this paper consists in the improvement of the continuous fuzzy models by using their discretized models. In this case, the effect of the discretization step on the performances of the continuous TS fuzzy models is studied. The usefulness of this approach is shown through two examples.

The purpose of this paper is to stabilize the type-2 Takagi–Sugeno (T–S) fuzzy systems with the sufficient and guaranteed stability conditions. The given conditions efficaciously handle parameter uncertainties by the upper and lower membership functions of the type-2 fuzzy sets (FSs).

This paper reports on a relevant study of stable fuzzy controllers and type-2 T–S fuzzy systems and reported that the synthesis of controller for nonlinear systems described by the type-2 T–S fuzzy model is a key problem and it can be resolve to convex problems via linear matrix inequalities (LMIs).

The multigain fuzzy controllers are established to improve the solvability of the stability conditions, and the authors design multigain fuzzy controllers which have extensive information of upper and lower membership grades. Consequently, the authors derive the traditional stability condition in terms of LMIs. One simulation examples illustrate the effectiveness and robustness of the derived stabilization conditions.

The uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions. The proposed control approach will give superior fault-tolerant control permanence under the actuator fault [partial loss of effectiveness (LOE)]. Also the controller robust against the unmeasurable process disturbances. Additionally, the statistical z-test are carried out to validate the proposed control approach against the control approach proposed by Himanshukumar and Vipul (2019a).

The purpose of this paper is to propose a stability analysis and control synthesis for achieving passivity properties of a class of continuous‐time nonlinear systems. These nonlinear systems are represented via continuous affine Takagi‐Sugeno (T‐S) fuzzy models, which played an important role in nonlinear control systems. The affine T‐S fuzzy models are more approximate than homogeneous T‐S fuzzy models for modeling nonlinear systems. Using the energy concept of passivity theory with Lyapunov function, the conditions are derived to ensure the passivity and stability of nonlinear systems. Based on the parallel distribution compensation (PDC) technique, this paper proposes a fuzzy controller design approach to achieve the passivity and stability for the continuous affine T‐S fuzzy systems.

For solving stability and stabilization problems of affine T‐S fuzzy models, the conversion techniques and passive theory are employed to derive the stability conditions. By applying the linear matrix inequality technique, a modified iterative linear matrix inequality algorithm is proposed to determine and update the auxiliary variables for finding feasible solutions of these stability conditions.

By studying the numerical example, the proposed design technique of this paper is an effectiveness and useful approach to design the PDC‐based fuzzy controller. From the simulation results, the considered nonlinear system with external disturbances driven by proposed design fuzzy controller is stable and strictly input passive.

This paper is interesting for designing fuzzy controller to guarantee the stability and strict input passivity of affine T‐S fuzzy models.

The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

The authors perform Briat Lemma, multiple integral approach and linear convex combination technique to investigate a class of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay. New sufficient criterion is established by linear matrix inequalities conditions.

It turns out that the obtained methods are easy to be verified and result in less conservative conditions than the existing literature. Two examples show the effectiveness of the proposed results.

The novelty of the proposed approach lies in establishing a new Wirtinger-based integral inequality and the use of the Lyapunov functional method, Briat Lemma, multiple integral approach and linear convex combination technique for stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

To prove the effectiveness of the proposed design method, this study aims to propose the Fisher equation and temperature cooling fins that control high-speed aerospace vehicles.

A new approach whereby the control of aerospace vehicles can be achieved by fuzzy controller and appropriate Navier–Stokes equations in this article. The design of the controller based on models of Navier–Stokes equations simplified complex mathematical simulations and approximations.

If the fuzzy controller cannot stabilize the system, the Navier–Stokes fuzzy function is injected into the system as a controller tool, and the system is asymptotically stabilized by adjusting the fuzzy parameters.

The simulation results show that if the tuning frequency is high enough, the fuzzy controller and fuzzy observer can create chaotic movements by adjusting the dither amplitude appropriately. The demonstration of the Fisher equation and the temperature-cooled fin control problem for high-speed aerospace vehicles has displayed the benefits of combining fuzzy control with the Navier–Stokes equation.

To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper aims to propose an evolved bat algorithm fuzzy neural network (NN) controller algorithm.

In evolved fuzzy NN modeling, the NN model and linear differential inclusion representation are established for the arbitrary nonlinear dynamics. The control problems of the Fisher equation and a temperature cooling fin for high-speed aerospace vehicles will be described and demonstrated. The signal auxiliary controlled system is represented for the nonlinear parabolic partial differential equation (PDE) systems and the criterion of stability is derived via the Lyapunov function in terms of linear matrix inequalities.

This representation is constructed by sector nonlinearity, which converts the nonlinear model to a multiple rule base for the linear model and a new sufficient condition to guarantee the asymptotic stability.

This study also injects high frequency as an auxiliary and the control performance to stabilize the nonlinear high-speed aerospace vehicle system.

This paper aims to deal with the problem of the global stabilization for a class of tension leg platform (TLP) nonlinear control systems.

It is well-known that, in general, the global asymptotic stability of the TLP subsystems does not imply the global asymptotic stability of the composite closed-loop system.

An effective approach is proposed to control chaos via the combination of fuzzy controllers, fuzzy observers and dithers.

If a fuzzy controller and a fuzzy observer cannot stabilize the chaotic system, a dither, as an auxiliary of the controller and the observer, is simultaneously introduced to asymptotically stabilize the chaotic system.

Thus, the behavior of the closed-loop dithered chaotic system can be rigorously predicted by establishing that of the closed-loop fuzzy relaxed system.

Maintenance organizations continue to be under pressure to systematically eliminate maintenance wastes and deliver services that their customers value. To this end, maintenance managers are implementing lean maintenance practices. But how does one consistently estimate the leanness of these practices in their organization? The purpose of this paper is to develop a framework for a metrics – referred to as the lean maintenance index (LMI) that can help managers estimate the leanness of maintenance practices.

Based on a comprehensive review of literature in the domain, this study identifies four factors and nineteen subfactors that are essential for the success of a lean maintenance program. A fuzzy-set-theory-based assessment framework is developed that can be used by an in-house team to measure the degree of implementation of lean maintenance practices in their organization. The authors applied the framework to a maintenance workshop that services diesel engines and other prime movers.

The framework provides maintenance managers valuable insights to help identify the strengths and weaknesses of their organization vis-à-vis their maintenance practices, thus enabling them come up with a firm action plan for future process improvements.

This paper adapts the concept of agility and readiness to maintenance work. A key contribution of this study is the identification of factors and subfactors that forms the basis to estimate the leanness of maintenance practices in an organization. Another contribution is its application to a large maintenance workshop that demonstrates the ease of its implementation. Future research in this area can help identify more factors and subfactors and thus improve the estimation of leanness.