Quantized tracking control for Interval Type‐2 fuzzy systems under privacy protection and hybrid attacks

This paper introduces the analytical structure of a class of interval type-2 (IT2) fuzzy PD and PI controllers that have symmetrical rule base. Two assumptions are made: (1) Zadeh AND operator is employed as the t-norm operator; (2) the centroid type-reduction method is used. The main contribution of this paper is the analytical relationship between the inputs and output of this IT2 fuzzy controller. Comparing with its T1 counterpart, the addition of two FOU parameters generated 31 extra local regions that each provides a unique relationship between the inputs and output signals. The relatively large number of local regions that results, at the cost of two extra design parameters, means that an IT2 fuzzy controller has the potential to provide better performance. Another outcome is the analysis of the centroid type-reduction algorithm yielded closed form expressions for each partition. This result may provide a platform for further theoretical study.

Tracking control design of interval type-2 polynomial-fuzzy-model-based systems with time-varying delay

This paper presents a practical implementation of an interval type-2 fuzzy logic controller for two stages photovoltaic system consisting of DC- DC boost converter and three-phase inverter. The power converter circuits are nonlinear and have uncertainties. These uncertainties cause noise and delay in the response of power systems. A photovoltaic system has also uncertainties caused by itself. The change in temperature and irradiation cause the change in parameters of photovoltaic systems. Conventional controllers and type-1 fuzzy controllers can’t overcome uncertainties. The main objective of this study is to show the superiority of interval type-2 fuzzy logic controller to overcome the uncertainties. Interval type-2 fuzzy logic controller algorithm was developed for DC- DC boost converter and three-phase space vector modulated inverter. To make comparison, the system was controlled by a type-1 and an interval type-2 fuzzy controller respectively. Simulation studies and real time application were performed. Settling time, overshoot and ripple voltage were investigated for the boost converter. Current and voltage harmonic analysis, frequency and phase angle data were analyzed for three-phase inverter. According to the simulation results and the practical results, interval type-2 fuzzy logic controller offered better solution for the whole system.

This article provides a novel framework for the concurrent development of asynchronous observers and controllers for discrete-time nonlinear semi-Markov jump singularly perturbed systems subjected to mismatched modes, states, and premise variables between controlled systems and observer-based controllers. Aiming at characterizing nonlinearity with parameter uncertainty, the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy technique is implemented in system modeling. Meanwhile, it is supposed that both observer and controller modes can just be acquired via a hidden Markov mode detector in the first attempt. Then, following the concept of non-parallel distribution compensation (non-PDC), the observer-based IT2 fuzzy asynchronous controllers are constructed with observers and controllers sharing the same fuzzy membership function but different from that in systems, which improves the designed flexibility. In accordance with semi-Markov kernel approach and the Lyapunov function contingent upon both system modes and sojourn times, sufficient criteria are established for the functioning of expected mode-dependent IT2 fuzzy observers and controllers such that the σ-mean-square stability for the resulting nonlinear augmented semi-Markov jump singularly perturbed systems comprised of the controlled systems and observation error systems is guaranteed. Furthermore, from the perspective of fuzzy processing, parameters and relaxation matrices that comply with fuzzy rules are added to ensure system stability while further reducing the conservatism of conditions. Ultimately, a circuit model and comparison examples are shown to substantiate the necessity and superiority of the suggested technique.

A smart DPLL for robust carrier tracking systems using uncertain rule-based IT2 fuzzy controllers

This paper presents the analytical structure of a class of interval type-2 (IT2) fuzzy proportional derivative (PD) and proportional integral (PI) controllers that have symmetrical rule base and symmetrical consequent sets. Two assumptions are made: 1) The Zadeh AND operator is employed as the t-norm operator; 2) type-reduction is performed by the Karnik-Mendel (KM) type-reduction method. The main contributions are the methodology that identifies the input conditions, where the KM algorithm uses a new switch point to compute the bounds of the type-reduced set, the closed-form expressions that relate the inputs and output of an IT2 fuzzy controller, and insights into the potential performance improvement because of the inclusion of the footprint of uncertainty (FOU). Compared with its T1 counterpart, two additional FOU parameters generate 31 extra local regions, each providing a unique relationship between the inputs and output signals. The generation of a relatively large number of local regions at the cost of two extra design parameters indicates that an IT2 fuzzy controller may be able to provide better performance. Furthermore, by comparing the analytical structure with the corresponding T1 counterpart, the potential advantages to use the IT2 over the T1 fuzzy controller are studied. Four interesting characteristics are identified, and they provide insights into why the IT2 fuzzy controller may better balance the conflicting aims of fast rise time and small overshoot.

This article is concerned with the problem of the Hankel-norm model reduction for stochastic discrete-time nonlinear systems in interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy framework. The IT2 T-S fuzzy model is an efficient model for describing uncertain nonlinear systems, and the model reduction is to simplify the high-order complex systems by reducing the order of the original system. The aim of this article is to reduce the order of the original stochastic discrete-time IT2 fuzzy system into lower order system without ignoring the influence of IT2 membership functions. First, the Hankel-norm performance of the stochastic discrete-time IT2 fuzzy model is analyzed. Then, based on the projection theorem and cone complementary linearization approach, a convex Hankel-norm-based model reduction approach subject to conditions in the form of linear matrix inequalities (LMIs) is obtained. A membership-functions-dependent (MFD) technique is applied to capture the information of IT2 membership functions and further reduce the conservativeness. A numerical example is presented to illustrate the effectiveness of the proposed results.

Adaptive fuzzy tracking control for a class of uncertain nonaffine nonlinear systems with dead-zone inputs

This paper investigates the dissipative interval type-2 (IT2) fuzzy control issue for a group of uncertain nonlinear systems subject to randomly occurring sensor saturation and quantization. By utilizing the both lower and upper membership functions with relevant tradeoff moduli, uncertainties, which exist in the considered systems, can be captured effectively. During the design of the IT2 fuzzy controller, we take the actuator failure into consideration. Considering the randomicities for the occurrences of sensor saturation and quantization, two separate Bernoulli stochastic distributed courses are utilized to construct the corresponding complicated transmission route. In term of Lyapunov stability theory, sufficient criteria to design dissipative IT2 fuzzy controller are presented to assure the stochastic stability with strict dissipativity of resulting closed-loop systems. Finally, illustrative example verifies the effectiveness and merits of the control strategy proposed in this paper.

In the domain of mobile robots (MRs) utilizing multi-wheel directional control, position synchronization in steering wheels remains a challenging problem, particularly when implemented in permanent magnet steering wheels for vertical tank robots. Addressing this critical problem, this paper presents a position synchronization control strategy that integrates a mean deviation coupling strategy (MDCS) with state feedback tracking control (SFTC) enhanced by integral action. In essence, MDCS minimizes position synchronization errors, especially during deviations or fluctuations, while SFTC enables fast responses to state changes and improves adaptability in uneven terrain. Combined with integral action, SFTC compensates for static errors, enhancing long-term stability. Furthermore, to establish robust evidence for the proposed strategy, this paper validates the results through both simulation and experimental analysis. Initially, simulation results show that integrating the MDCS with SFTC improves settling time, reducing it from 0.6 to 0.4 s. Additionally, experimental tests demonstrated that the improvement in settling time closely matched the simulation results during the reference angle change phase, excluding the initial startup phase. Without the synchronization strategy, the settling time was 0.5 s, whereas applying the control strategy increased it to 0.7 s. This discrepancy arises from the selected MDCS control coefficients, which were not well-suited to the actual system, leading to ineffective compensation signal intervention. As a result, the system dynamics were altered, and the stability of the poles was no longer ensured compared to the original selection. It can be concluded that the integration of MDCS with SFTC holds strong potential for practical application in steering wheels, improving synchronization while reducing the computational burden on hardware.

In this paper, a reference model tracking control for a nonlinear system expressed as a Takagi-Sugeno (T-S) fuzzy model is set through a fuzzy integral sliding mode controller. The stabilization conditions for the sliding motion are derived in terms of a linear matrix inequality, and the fuzzy integral sliding mode controller is designed. Moreover, it is verified through the Lyapunov stability condition that the designed controller allows the error dynamics model to reach the sliding surface. Finally, in the simulation example, it is shown that the proposed method successfully achieves the fuzzy tracking control. In addition, a performance comparison between the proposed method and the existing state feedback control technique is provided.

Compliance monitoring for quality of web service (QoWS) has accuracy issues due to uncertain network behaviors. Existing models use precise computation-based methods for defining and monitoring QoWS requirements, but these methods have limited ability to handle uncertainties. Consequently, the accuracy of the monitoring results is degraded. Defining expected QoWS using exact values is unrealistic, as generally not all service requestors know what values should be specified in the contract. Therefore, this paper proposes an interval type-2 (IT2) fuzzy model for QoWS compliance monitoring because it has greater capability than precise computation methods to reduce the effects of uncertainties. IT2 also has greater capability than the traditional fuzzy sets to manage uncertainty problem due to its non-crisp membership degrees assigned to the input. The model is able to perform compliance monitoring on linguistically defined QoWS. The model is developed based on fuzzy C-means algorithm, and the number of clusters is optimized using a clustering validity index. The model is constructed based on a Mamdani fuzzy inference system. The results show that the IT2 model outperforms type-1 fuzzy and precise computation-based models in terms of the accuracy of monitoring results. This research results in more accurate and precise QoWS compliance monitoring. It also provides user-centric QoWS specifications because requestors can define their requirements using linguistic values.

This paper is concerned with interval type-2 (IT2) fuzzy control design for a class of nonlinear space teleoperation systems with external disturbances and time-varying delays. IT2 fuzzy model based (FMB) control design with exponential-type Barrier Lyapunov function (EBLF) is presented to address state constraints, communication burden from ground stations to satellites (space-robot), and uncertain human/environment interaction parameters in a unified event-triggered control structure. We show that, with the proposed adaptive event-triggered control scheme, the exponential convergence performance of the synchronization tracking errors is guaranteed, while the prescribed constraint requirement is satisfied. Simulation results are provided to validate the effectiveness of the proposed controller.

SummaryIn this article, the H∞ tracking control problem is first considered for two‐dimensional fuzzy networked control systems. The system is described by fuzzy blending of the so‐called second Fornasini‐Marchesini local linear state‐space model. Based on the fuzzy model, an observer‐based state feedback controller is employed to guarantee the H∞ tracking performance. The separation property is established in the case of observer‐based H∞ tracking control framework, that is, the controller gains and observer gains can be simultaneously obtained by solving two sets of linear matrix inequalities. A simulation example is given to illustrate the effectiveness of the proposed method.

<abstract> <p>The asymptotic behavior of solutions of fuzzy control systems is a component of the study of fuzzy control theory. The study of stability for T-S (Takagi-Sugeno) fuzzy systems, which process qualitative data through linguistic expressions, is the subject of this paper. Asymptotic stability is conservative in many real-world applications due to measurement noise and other disruptions. The ultimate limit, which indicates that the mistakes stay in a specific area close to the origin after a long enough amount of time, is a crucial characteristic that is frequently defined for such systems. We are interested with the problem of the state feedback controller for T-S fuzzy models with uncertainties where the global exponential ultimate boundedness of solutions is studied for certain fuzzy control systems. We use common quadratic Lyapunov function and parallel distributed compensation controller techniques to study the asymptotic behavior of the solutions of fuzzy control system in presence of perturbations. An example demonstrating the validity of the main result is discussed.</p> </abstract>