Uncertain Fuzzy Systems Research Articles

Guaranteed Set-Membership Estimation for Local Nonlinear Uncertain Fuzzy Systems Subject to Partially Decouplable Unknown Inputs

Local nonlinear fuzzy systems are useful for control solutions due to their ability to handle unmeasurable/inexact premise variables and reduce computational complexity. However, their estimation problems still require further development. This paper addresses this issue by investigating set-membership estimation for a class of discrete-time local nonlinear uncertain Takagi–Sugeno fuzzy systems with guaranteed performance. We propose a new observer architecture that solves partially decouplable unknown inputs and converts nonlinear error dynamics into a linear parameter-varying type. Using the systematic <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _\infty$</tex-math></inline-formula> -technique, the design conditions in the form of linear matrix inequalities ensure stability and output performance of the state estimation. We also derive a straightforward and effective zonotopic analysis method, considering the fuzzy and local nonlinear context, for less conservative results without using any specific interval set computation. Furthermore, a fast fault detection logic is proposed as an application of the set-membership estimation. Finally, we demonstrate the feasibility and advantages of our approach through three compelling examples, showcasing its efficacy in different scenarios.

Necessary and sufficient conditions for crisp state-feedback design for fuzzy linear systems

In this paper, we examine the uncertain fuzzy continuous time linear systems. There is fuzzy uncertainty in the dynamics of the investigated system in the form of uncertain parameters represented by fuzzy granular numbers. A state feedback is designed to guarantee the stability of the closed-loop system with crisp gains and a necessary and sufficient condition is proposed to guarantee the stability. Furthermore, by using the proposed control input, the fuzzy eigenvalues of the closed-loop system are placed in a way that they have the greatest possibility of occurring in the desired poles. The state feedback gain which is presented in this paper is a crisp number and can be applied to real systems in practical applications. The proposed method ensures closed-loop stability whereas previous methods have presented fuzzy gains which are defuzzified to be applied to a real system without guaranteeing the stability. At the end, two practical simulation examples are given to verify the validity of the theoretical results.

Robust tracking control for uncertain fuzzy systems with time delay and external disturbances

AbstractIn this article, robust tracking and disturbance rejection problem for Takagi–Sugeno fuzzy systems with time delay and external disturbances are discussed. Specifically, proportional‐integral controller with an enhanced equivalent‐input‐disturbance technique is constructed to force the output trajectories to track the bounded reference input signal even in the presence of external disturbances. Further, the resulting fuzzy control system can be represented as an augmented system with state delay and uncertainties and the output tracking problem is transformed into stabilization problem of the augmented system. In the stabilization analysis, the considered fuzzy system does not share the same membership functions with the proposed control. Moreover, the delay‐dependent stabilization criteria for the addressed fuzzy system are presented in the form of linear matrix inequalities by the virtue of augmented Lyapunov–Krasovskii functional. To be specific, the symmetric matrices involved in the Lyapunov–Krasovskii functional need not be positive definite. Ultimately, three numerical examples are offered to support the validity of the established theoretical results.

Practical stability of fractional-order nonlinear fuzzy systems

In this paper, we present a practical Mittag–Leffler stability for a new class of fractional-order nonlinear Takagi–Sugeno fuzzy uncertain systems. Based on fractional-order Lyapunov stability theory and the parallel distributed compensation (PDC) controller techniques, we show the convergence of the solutions of the closed-loop considered system toward a neighborhood of the origin. Furthermore, a numerical example is given to show the applicability of the main result.

Formula omitted] Sampled-Data Control for Uncertain Fuzzy Systems under Markovian Jump and FBm

This study concerns the sampled-data H∞ control problems of Markovian jump uncertain fuzzy systems constrained by the fractional Brownian motion (fBm). By constructing a piecewise Lyapunov-Krasovskii functional (LKF) and derivating it, sufficient conditions are gained for the stability of the Takagi-Sugeno (T-S) fuzzy systems. Especially, the free-weighting matrix and Newton-Leibniz formula are exploited to relax the conservatism. At last, state curves of three classic practical examples show the rationality of our design.

Regularization scheme for uncertain fuzzy differential equations: Analysis of solutions

&lt;abstract&gt;&lt;p&gt;In this paper a regularization scheme for a family of uncertain fuzzy systems of differential equations with respect to the uncertain parameters is introduced. Important fundamental properties of the solutions are discussed on the basis of the established technique and new results are proposed. More precisely, existence and uniqueness criteria of solutions of the regularized equations are established. In addition, an estimation is proposed for the distance between two solutions. Our results contribute to the progress in the area of the theory of fuzzy systems of differential equations and extend the existing results to the uncertain case. The proposed results also open the horizon for generalizations including equations with delays and some modifications of the Lyapunov theory. In addition, the opportunities for applications of such results to the design of efficient fuzzy controllers are numerous.&lt;/p&gt;&lt;/abstract&gt;

Observer-Based Fault Diagnosis and Fault-Tolerant Tracking Control for T-S Fuzzy Uncertain System Affected by Simultaneous Sensor and Actuator Faults

Observer-Based Fault Diagnosis and Fault-Tolerant Tracking Control for T-S Fuzzy Uncertain System Affected by Simultaneous Sensor and Actuator Faults

Optimal design of robust control based on compound form: steady-state performance and control cost

In this paper, we propose an optimal robust control for dealing with the uncertain fuzzy dynamical systems. First, a dynamical system is established with uncertainty. The uncertainty is nonlinear, time varying, and is regarded as bounded. The bound exists within a specified fuzzy set. Then, a robust control is proposed to ensure the system performance. The proposed control is deterministic, which is not on the basis of the IF-THEN rule. Besides, to achieve better performance, a performance index, including both the steady-state performance measurement and the control cost measurement of the system, is proposed. Furthermore, a fuzzy dynamical model of the robot joint module system is established with parameters uncertainty and external disturbance. By using the proposed optimal robust control, the performance of the dynamical system is not only definitely ensured but also optimized.

Nonstationary Filtering for Fuzzy Markov Switching Affine Systems With Quantization Effects and Deception Attacks

This article focuses on the issue of nonstationary filtering for uncertain fuzzy Markov switching affine systems (FMSASs) with quantization effects and deception attacks (DAs). The resulting FMSASs are comprised of Markov switching piecewise-affine systems over a set of operating regions. To characterize the multinetwork-induced constraints, the measurement output is quantized before being transmitted, and a compensation scheme is applied to tackle the quantized measurement output loss intermittently. Meanwhile, the randomly occurring DAs are involved, in which the attack behaviors are identified by the bounded stochastic signals. Differently, to deal with the multinetwork-induced constraints, a novel nonstationary region-dependent affine filter strategy is developed. By resorting to a mode-dependent and region-dependent Lyapunov functional and S-procedure theory, sufficient conditions are elicited such that the filtering error system is mean-square exponentially stable. Finally, the practicability of the derived results is verified by a practical tunnel diode circuit model.

High-order robust control and Stackelberg game-based optimization for uncertain fuzzy PMSM system with inequality constraints

There exist the uncertainties and the inequality constraints in permanent magnet synchronous motor (PMSM) system. In order to meet the safety control requirements in industrial applications, the state transformation is used to meet the inequality constraints for limiting the outputs within desired bounds. Then, fuzzy set theory, which is different from fuzzy logic, is used to describe uncertainty, and the fuzzy PMSM dynamical model is established. Based on that, a robust control with high-order term is proposed to compensate for the time-varying uncertainty. Furthermore, for improving the system performance and decreasing the control cost, the Stackelberg game is introduced into the optimization scheme design, in which the leader plays a more important role than follower. These characteristic corresponds to the influence of the two tunable control parameters on the system. Thus, the optimal parameters are obtained by the rules of Stackelberg game. Finally, experimental results show the effectiveness of the above theories.

Design of robust control for uncertain fuzzy quadruple-tank systems with time-varying delays

The robust H_infty observer-based control design is addressed here for non-linear Takagi-Sugeno (T-S) fuzzy systems with time-varying delays, subject to uncertainties and external disturbances. This is motivated by the quadruple-tank with time delay control problem. The observer design methodology is based on constructing an appropriate Lyapunov–Krasovskii functional (LKF) for an augmented system formed from the original and the delayed states. The bilinear terms are transferred to the linear matrix inequalities, thanks to a change of variables which can be solved in one step. Furthermore, by employing the mathcal {L}_2 performance index, the adverse effects of persistent bounded disturbances is largely avoided. The proposed method has the advantage of relating the controller and Lyapunov function to both the original and delayed states. Then, the controller and observer gains are obtained simultaneously by solving these inequalities with off-the-shelf software (Yalmip/MATLAB toolbox). Finally, an application to a simulated quadruple-tank system with time delay is carried out to demonstrate the benefits of the proposed technique, showing a compromise between controller simplicity and robustness that outperforms previous approaches.

Stabilization of Fuzzy Hydraulic Turbine Governing System With Parametric Uncertainty and Membership Function Dependent H∞ Performance

This paper examines the stabilization problem and membership function dependent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> performance analysis for uncertain hydraulic turbine governing systems with stochastic actuator faults and time-varying delays via sampled-data control. At first, the nonlinear hydraulic turbine systems are modeled as Takagi-Sugeno (T-S) fuzzy systems with time-varying delay and bounded external disturbance through membership functions. Then, a novel delay-dependent looped Lyapunov-Krasovskii functional (LKF) is formulated with complete information throughout the sampling interval. In the meantime, a membership function dependent <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> performance index is suggested to diminish the impact of disturbances on the uncertain fuzzy system. Based on the robust control and novel LKF, new delay-dependent stability conditions for the closed-loop system are attained in the framework of linear matrix inequalities (LMIs). At last, the numerical example validates the proposed theoretical contributions in terms of achieving robust stability and minimizing disturbance attenuation levels.

Stability Analysis and Robust Stabilization of Uncertain Fuzzy Time-Delay Systems

New sufficient conditions for delay-independent and delay-dependent robust stability of uncertain fuzzy time-delay systems based on uncertain fuzzy Takagi-Sugeno (T-S) models are presented by using the properties of matrix and norm measurements. Further sufficient conditions are formulated, in terms of the linear matrix inequalities (LMIs) of robust stabilization, and are developed via the technique of parallel distributed compensation (PDC), and then the simplification of the conditions for the controller design of uncertain fuzzy time-delay systems. The proposed methods are simple and effective. Some examples below are presented to illustrate our results.

Robust finite frequency static output feedback control for uncertain fuzzy systems via a sum‐of‐squares approach

AbstractThis article deals with the problem of robust static output feedback control in finite frequency (FF) domain for polynomial Takagi–Sugeno fuzzy systems. By using generalized Kalman–Yakubovich–Popov lemma and polynomial Lyapunov functions, sufficient conditions are proposed for controllers design in terms of sum of squares. These conditions include neither transformation matrices nor equality constraints, which simplifies the numerical solution. The proposed method is designed in the FF domain to reduce the conservativeness generated by those designed in the full frequency domain. Some numerical examples are provided to demonstrate the applicability of the proposed approach.

Nonfragile Sampled-Data Filtering of Uncertain Fuzzy Systems With Time-Varying Delays

This article studies the nonfragile sampled-data filtering problems for Takagi-Sugeno fuzzy systems with uncertainties and delays. By introducing two delay-product-type terms and other augmented terms, a novel Lyapunov-Krasovskii functional containing more detailed information of time delays is constructed. With the help of some new bounding inequalities, improved stability results for the addressed systems are established. Besides, a resilient sampled-data controller is devised with less cost. Furthermore, the efficiency of the obtained criteria is illustrated by a numerical example.

Robust Constrained Model Predictive Control for T-S Fuzzy Uncertain System with Data Loss and Data Quantization

This paper addresses the robust constrained model predictive control (MPC) for Takagi-Sugeno (T-S) fuzzy uncertain quantized system with random data loss. To deal with the quantization error and the data loss over the networks, the sector bound approach and the Bernoulli process are introduced, respectively. The fuzzy controller and new conditions for stability, which are written as the form of linear matrix inequality (LMI), are presented based on nonparallel distributed compensation (non-PDC) control law and an extended nonquadratic Lyapunov function, respectively. In addition, slack and collection matrices are provided for reducing the conservativeness. Based on the obtained stability results, a model predictive controller which explicitly considers the input and state constraints is synthesized by minimizing an upper bound of the worst-case infinite horizon quadratic cost function. The developed MPC algorithm can guarantee the recursive feasibility of the optimization problem and the stability of closed-loop system simultaneously. Finally, the simulation example is given to illustrate the effectiveness of the proposed technique.

Stability and stabilization for uncertain fuzzy system with sampled-data control and state quantization

This paper studies the stability and stabilization problems of the T-S fuzzy systems with uncertainty and state quantization. Considering that fuzzy membership functions(FMFs) are the main characteristic of T-S fuzzy model, if the information about the membership function is not added, it will be conservative. So, a novel Lyapunov-Krasovskii functional (LKF) which contains not only integral variables but also FMFs is constructed. To include more information about the sampling pattern, the states on both sides of the sampling interval are incorporated into the LKF. When taking the derivative of the LKF, the product terms which consist of derivative of FMFs and LKF coefficient are involved. Then, the product terms are discussed to ensure their negative definition. By further derivation, enough stability conditions are expressed in the form of linear matrix inequalities (LMIs). The sampling intervals and controller parameters for the T-S fuzzy system can be solved by MATLAB toolbox with the optimal parameters. Finally, two numerical examples are simulated to illustrate the effectiveness of the proposed method.

Reliable intermittent extended dissipative control for uncertain fuzzy flexible spacecraft systems with Bernoulli stochastic distribution

This paper addresses the problem of uncertain fuzzy intermittent extended dissipative control to stabilize the flexible spacecraft (FS). However, to the systems with actuator random failures, input saturation and Bernoulli stochastic distribution by utilizing a novel switching Lyapunov function method. Compared with the existing common Lyapuonv function, the proposed approach is convenient for us to exploit more information of switching interval. Especially, the condition that control length and control period must be a certain proportion in the previous works of intermittent control strategy is removed in this work, which promotes the obtained criterion to be less conservative. Numerical simulations are finally given to demonstrate the validity and superiority of the theoretical results.

Stochastic Switched Sampled-Data Control for Uncertain Fuzzy Systems with Packet Dropout

This paper addresses the stability and stabilization problems of uncertain Takagi–Sugeno (T-S) fuzzy system with control packet dropout. The process of packet loss is proposed, which is modeled according to some white noise sequences of Bernoulli distribution. Then, under the zero-input strategy, a newly stochastic switched sampled-data controller is introduced. Based on the Lyapunov function method, a novel Lyapunov–Krasovskii (LKF) function is constructed via introducing a fuzzy membership functions (FMFs), which can use the information about the actual sampling pattern. Each term of the LKF need not be positive, but it needs to be positive at sampling instants. Using the reciprocally convex method and relaxed free-matrix-based (FMB) integral inequality, novel stabilization criteria are established to guarantee that the T-S fuzzy system is stochastic stable when the control packet dropout occurs in a random way. Based on the linear matrix inequalities (LMIs), a fuzzy controller design algorithm for sampled data is presented to obtain a larger sampling interval. Finally, two numerical examples are used to verify the effectiveness and advantages of the proposed method.

H∞ Tracking Control of Uncertain Markovian Hybrid Switching Systems: A Fuzzy Switching Dynamic Adaptive Control Approach.

This article investigates the H∞ stochastic tracking control problem for uncertain fuzzy Markovian hybrid switching systems by using a fuzzy switching dynamic adaptive control approach. The long and the short is to construct multiple piecewise stochastic Lyapunov functions which provide an effective tool for designing hybrid switching law and fuzzy switching dynamic adaptive law. A hybrid switching law, including both stochastic switching and deterministic switching, is designed to represent more general switching scenarios, which can improve the H∞ adaptive tracking performance through offering a running time before stochastic switching for the adaptive control strategy to work well. A fuzzy switching dynamic adaptive control technique is developed such that all signals of the tracking error equation are bounded, and the system state trajectory tracks the reference model state trajectory under a disturbance attenuation level as closely as possible. Finally, an application study verifies the effectiveness of the acquired methods.