Interval Type-2 (IT2) Fuzzy Logic Controllers (FLCs) have emerged in the past several years due to their ability to handle high levels of uncertainties and disturbances in real-world applications. They are most widely used due to their superior control performance compared to their Type-1 (T1) counterparts. This paper presents nine different mathematical models of the simplest IT2 Fuzzy PID (FPID) controllers of Mamdani-type by considering IT2 Fuzzy Sets (FSs) with triangular Membership Functions (MFs) on both input and output linguistic variables, Larsen Product (LP) inference, and three different defuzzification methods, namely Centroid defuzzification, Average defuzzification, and Nie-Tan (NT) defuzzification. One-Dimensional (1D) input space is employed to reduce the computational complexity of the models. The properties of the models are thoroughly examined. Guidelines for the synthesis of controllers are presented. Simulation and real-time studies are conducted to justify the validity and usefulness of the proposed controller models. The robustness property of the proposed controllers is also investigated via simulation studies and real-time experiments by considering input and output disturbances and plant parameter variations.
Deep domain adaptation methods have gained significant attention in few-shot intelligent industrial fault diagnosis. However, most existing approaches often struggle when confronted with unknown faults in target domains, posing operational risks. To tackle this problem, an open-set domain adaptation method is presented to improve fault diagnosis accuracy and robustness. The proposed Dual-Adaptive Unknown Faults Separation Network (DAUFSN) approach integrates a shared feature extractor with two specialized networks: a closed-set adversarial unknown separation network and an open-set adversarial domain adaptation network. The closed-set network generates similarity scores to separate known and unknown faults, while the open-set network introduces an extra class, especially for unknown faults, leveraging the similarity scores in the loss functions to separate the unknown faults. This allows the system to both accurately classify known faults and effectively identify unknown ones, addressing the critical challenge of unknown fault identification. Experimental validation using datasets from rolling bearing and multiphase flow processes demonstrates the efficacy and superiority of the DAUFSN method.
Friction-hydraulic braking system employs friction to dissipate kinetic energy, and motor-electric braking system utilizes an electric motor to convert kinetic energy into electric energy. To achieve rapid stopping and recover some energy, battery electric vehicles require adopting the motor to realize the anti-lock braking function, primarily by reducing friction-hydraulic operation. This is especially challenging for the motor-electric anti-lock braking system, for which almost all characteristics of the motor-electric differ from the friction-hydraulic. Many studies on anti-lock braking systems mainly focus on wheel slip ratio control, and some other research is related to wheel deceleration control. In this article, a parameter-tuned sliding mode control approach is designed to realize the optimal wheel deceleration by the motor-electric torque control, and an ideal wheel deceleration curve is developed as the optimal. A proportional-integral observer is used to compute the road adhesion coefficient. Real vehicle experiments are performed using a battery electric vehicle to illustrate the effectiveness of the designed approach. According to the obtained results and statistical analysis, the designed motor-electric anti-lock braking control approach based on wheel deceleration improves the braking performance of the vehicle.
The present work proposes an integral sliding mode multiobserver (ISMMO) for nonlinear systems in the presence of sensor faults. By augmenting the sliding manifold with an integral term, the sliding mode multiobserver exhibits low-pass filtering dynamics that attenuate high-frequency chattering while preserving the stability of the estimation error dynamics. For the simultaneous estimation of the states and the sensor faults, these faults are considered as auxiliary state variables, thus an augmented multiobserver structure based on a discrete-time decoupled state multimodel is constructed. The stability conditions are presented in terms of Linear Matrix Inequalities (LMIs) using a common Lyapunov function. The superiority and effectiveness of the proposed ISMMO are illustrated through an experimental validation on a chemical reactor and a comparative study.
In this paper, a new discrete-time event-triggered mechanism (DTETM) is proposed, which is used in the framework of designing a discrete-time event-triggered observer (DTETO) for Takagi-Sugeno (T-S) fuzzy models with external disturbances. Existence conditions for the proposed DTETO are derived and formulated in terms of a convex optimization problem, which gives unknown observer matrices and minimized attenuation levels. Finally, an effective algorithm is obtained to estimate the state vector of the uncertain wind turbine system with a permanent magnet synchronous generator, the Chua's circuits, and the Rossler chaotic system. Compared with the existing T-S fuzzy observers operated based on time-triggered mechanisms or periodic memory sampled-data mechanisms, which may lead to wasteful communication resources, the proposed approach in this paper uses a novel Zeno-free DTETM, which effectively reduces communication and estimation resource usage while ensuring the desired estimation performance. Moreover, unlike the existing discrete-time event-triggered T-S fuzzy observers and fuzzy filters, which are effective in estimating the state vectors of discrete-time fuzzy dynamic models, the one in this paper may be useful in estimating the state vectors of continuous-time fuzzy dynamic models.
