Stability Analysis and Stabilization of T-S Fuzzy Delta Operator Systems with Time-Varying Delay via an Input-Output Approach

The stability analysis and stabilization of Takagi-Sugeno (T-S) fuzzy delta operator systems with time-varying delay are investigated via an input-output approach. A model transformation method is employed to approximate the time-varying delay. The original system is transformed into a feedback interconnection form which has a forward subsystem with constant delays and a feedback one with uncertainties. By applying the scaled small gain (SSG) theorem to deal with this new system, and based on a Lyapunov Krasovskii functional (LKF) in delta operator domain, less conservative stability analysis and stabilization conditions are obtained. Numerical examples are provided to illustrate the advantages of the proposed method

Dissipativity-Based Filtering of Time-Varying Delay Interval Type-2 Polynomial Fuzzy Systems under Imperfect Premise Matching

This paper investigates the dissipativity-based filtering problem for the nonlinear systems subject to both uncertainties and time-varying delay in the time-delay interval type-2 (IT2) polynomial fuzzy framework. Filter design is a challenging issue for complex nonlinear systems especially when uncertainties and time delay exist. IT2 polynomial fuzzy model is an effective and powerful approach to analyze and synthesize uncertain nonlinear systems. This is the first attempt to design both the full-order and reduced-order IT2 polynomial fuzzy filter to ensure that the filtering error system is asymptotically stable under the dissipativity constraint. The design of filtering is based on the imperfect premise matching scheme where the number of fuzzy rules and shapes of membership functions of the designed fuzzy filter can differ from those of IT2 polynomial fuzzy model, to provide greater design flexibility and lower implementation burden. By utilizing the Lyapunov-Krasovskii functional based approach, the information of membership functions, time delay and system states is taken into account in the design process to develop the relaxed membership-function-dependent (MFD) and delay-dependent filtering existence criteria. Finally, simulation results are presented to illustrate the effectiveness of the filtering algorithm reported in this paper.</p

Membership-Function-Dependent Stabilization of Event-Triggered Interval Type-2 Polynomial Fuzzy-Model-Based Networked Control Systems

In this article, the stability analysis and control synthesis of interval type-2 (IT2) polynomial-fuzzy-model-based networked control systems are investigated under the event-triggered control framework. The nonlinear dynamics in the plant is efficiently represented by an IT2 polynomial fuzzy model that the IT2 membership functions are utilized to capture the uncertainties in the plant. An event-triggered IT2 polynomial fuzzy controller is then designed to stabilize the nonlinear model subject to uncertainties. The stability conditions of the closed-loop control system are summarized in the form of sum-of-squares. Under the imperfectly premise matching (IPM) concept, the membership-function-dependent (MFD) approach is applied to endow the polynomial fuzzy controllers with more flexibility in terms of number of rules and premise membership functions. In the MFD approach under the IPM concept, both the number of rules and the shape of membership functions in the fuzzy models and controllers can be different. Also, the information of IT2 membership functions of the polynomial fuzzy model and controller is considered and adopted to further relax the stability conditions. Furthermore, the intrinsic mismatched issue of the premise variables of the fuzzy model and controllers due to the event-triggering mechanism is handled by the MFD approach. A detailed simulation example is provided to verify the effectiveness of the proposed event-based control strategy.</p

H_∞ tracking control for nonlinear multivariable systems using wavelet-type TSK fuzzy brain emotional learning with particle swarm optimization

This paper studies the H∞ tracking control for uncertain nonlinear multivariable systems. We propose a control strategy, which combines the adaptive wavelet-type Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning controller (WTFBELC) and the H∞ robust tracking compensator. As for the adaptive WTFBELC, it is a main controller designed to mimic the ideal controller. The proposed WTFBELC is to obtain much better ability of handling nonlinearities and uncertainties, but the proposed H∞ robust tracking compensator is to compensate the residual error between the adaptive WTFBELC and the ideal controller. Furthermore, the optimal learning rates of the adaptive WTFBELC are searched quickly by using the particle swarm optimization (PSO) algorithm, and the parameter updated laws are derived based on the steepest descent gradient method. The robust tracking performance of this novel control scheme is guaranteed based on Lyapunov stability theory. The mass-spring-damper mechanical system and the three-link robot manipulator, are used to verify the effectiveness of the proposed adaptive PSO-WTFBELC H∞ control scheme.</p

Modeling, optimization, and control for complex networked systems

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Circulating MicroRNA Expression Profiles in Patients with Stable and Unstable Angina

OBJECTIVES: High incidence and case fatality of unstable angina (UA) is, to a large extent, a consequence of the lack of highly sensitive and specific non-invasive markers. Circulating microRNAs (miRNAs) have been widely recommended as potential biomarkers for numerous diseases. In the present study, we characterized distinctive miRNA expression profiles in patients with stable angina (SA), UA, and normal coronary arteries (NCA), and identified promising candidates for UA diagnosis. METHODS: Serum was collected from patients with SA, UA, and NCA who visited the Department of Cardiovascular Diseases of the Meizhou People’s Hospital. Small RNA sequencing was carried out on an Illumina HiSeq 2500 platform. miRNA expression in different groups of patients was profiled and then confirmed based on that in an independent set of patients. Functions of differentially expressed miRNAs were predicted using gene ontology classification and Kyoto Encyclopedia of Genes and Genomes pathway analysis. RESULTS: Our results indicated that circulating miRNA expression profiles differed between SA, UA, and NCA patients. A total of 36 and 161 miRNAs were dysregulated in SA and UA patients, respectively. miRNA expression was validated by reverse transcription quantitative polymerase chain reaction. CONCLUSION: The results suggest that circulating miRNAs are potential biomarkers of UA

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

This letter presents a complete framework Meeting-Merging-Mission for multi-robot exploration under communication restriction. Considering communication is limited in both bandwidth and range in the real world, we propose a lightweight environment presentation method and an efficient cooperative exploration strategy. For lower bandwidth, each robot utilizes specific polytopes to maintains free space and super frontier information (SFI) as the source for exploration decision-making. To reduce repeated exploration, we develop a mission-based protocol that drives robots to share collected information in stable rendezvous. We also design a complete path planning scheme for both centralized and decentralized cases. To validate that our framework is practical and generic, we present an extensive benchmark and deploy our system into multi-UGV and multi-UAV platforms

Fuzzy Neural Network-Based Adaptive Sliding-Mode Descriptor Observer

This study examines the state estimation problem for uncertain descriptor systems subject to unknown dynamics. An integration of interval type-2 fuzzy set (IT2-FS) and cerebellar model articulation controller (CMAC) neural network, called the IT2-FCMAC approximator, is introduced to approximate the unknowndynamics and is incorporated into a sliding-mode descriptor observer. Then, its learning problem is cast into a robust control framework subject to discrete-Time nonlinear systems, and a robust H∞ control-based learning algorithm is proposed. Besides, an adaptive compensator is introduced to mitigate the impact of approximation error. An IT2-FCMAC-based adaptive sliding-mode observer is developed and the calculation of observer gain and learning parameters is solved by several linear matrix inequalities. The proposed scheme is applied in estimating the state of charge of lithium-ion batteries, showcasing its exceptional performance.</p