Composite fault reconstruction and fault-tolerant control design for cyber-physical systems: An interval type-2 fuzzy approach.

This article scrutinizes the stabilization and fault reconstruction issues for interval type-2 fuzzy-based cyber-physical systems with actuator faults, deception attacks and external disturbances. The primary objective of this research is to formulate the learning observer system with the interval type-2 fuzzy technique that reconstructs the actuator faults as well as the immeasurable states of the addressed fuzzy based model. Further, the information of reconstructed actuator faults is incorporated in the developed controller with the imperfect premise variables for ensuring the stabilization of the system under consideration. At the same time, the H∞ technique is employed to reduce the impact of external disturbances in the considered model. In addition to that, the deception attacks are represented as a stochastic variable that satisfies the Bernoulli distributions. On the ground of this, a set of sufficient criteria is deduced in the context of linear matrix inequalities to affirm the stability of the addressed systems. Furthermore, the requisite gain matrices are computed by resolving the obtained linear matrix inequality based stability criteria. At last, two simulation examples, including the mass-spring-damper system are exhibited to demonstrate the usefulness of analytical findings of the developed strategy.

A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution.

In recent years, deep learning super-resolution models for progressive reconstruction have achieved great success. However, these models which refer to multi-resolution analysis basically ignore the information contained in the lower subspaces and do not explore the correlation between features in the wavelet and spatial domain, resulting in not fully utilizing the auxiliary information brought by multi-resolution analysis with multiple domains. Therefore, we propose a super-resolution network based on the wavelet multi-resolution framework (WMRSR) to capture the auxiliary information contained in multiple subspaces and to be aware of the interdependencies between spatial domain and wavelet domain features. Initially, the wavelet multi-resolution input (WMRI) is generated by combining wavelet sub-bands obtained from each subspace through wavelet multi-resolution analysis and the corresponding spatial domain image content, which serves as input to the network. Then, the WMRSR captures the corresponding features from the WMRI in the wavelet domain and spatial domain, respectively, and fuses them adaptively, thus learning fully explored features in multi-resolution and multi-domain. Finally, the high-resolution images are gradually reconstructed in the wavelet multi-resolution framework by our convolution-based wavelet transform module which is suitable for deep neural networks. Extensive experiments conducted on two public datasets demonstrate that our method outperforms other state-of-the-art methods in terms of objective and visual qualities.

Disturbance rejection for multi-weighted complex dynamical networks with actuator saturation and deception attacks via hybrid-triggered mechanism.

In this work, we address hybrid-driven-based robust synchronization problem for multi-weighted complex dynamical networks with actuator saturation and deception attacks. The hybrid-triggered mechanism, which combines a switch between the event-triggered scheme and the time-triggered scheme, is often used to reduce the data transmission and the alleviate network burden. Further, the equivalent-input-disturbance technique is applied to eliminate the unknown disturbance effect of the addressed system. Moreover, a memory controller is designed under actuator saturation to ensure that the resultant augmented system is asymptotically synchronized even in the presence of deception attacks. Finally, three numerical examples are given to show the validity of the obtained theoretical results.

Stability and dissipativity criteria for neural networks with time-varying delays via an augmented zero equality approach.

This work investigates the stability and dissipativity problems for neural networks with time-varying delay. By the construction of new augmented Lyapunov-Krasovskii functionals based on integral inequality and the use of zero equality approach, three improved results are proposed in the forms of linear matrix inequalities. And, based on the stability results, the dissipativity analysis for NNs with time-varying delays was investigated. Through some numerical examples, the superiority and effectiveness of the proposed results are shown by comparing the existing works.

Some Novel Results on Stability Analysis of Generalized Neural Networks With Time-Varying Delays via Augmented Approach.

This article proposes three new methods to enlarge the feasible region for guaranteeing stability for generalized neural networks having time-varying delays based on the Lyapunov method. First, two new zero equalities in which three states are augmented are proposed and inserted into the results of the time derivative of the constructed Lyapunov-Krasovskii functionals for the first time. Second, inspired by the Wirtinger-based integral inequality, new Lyapunov-Krasovskii functionals are introduced. Finally, by utilizing the relationship among the augmented vectors and from the original equation, newly augmented zero equalities are established and Finsler's lemma are applied. Through three numerical examples, it is verified that the proposed methods can contribute to enhance the allowable region of maximum delay bounds.

Equivalent-input-disturbance estimator-based event-triggered control design for master-slave neural networks.

This paper investigates the robust synchronization problem for a class of master-slave neural networks (MSNNs) subject to network-induced delays, unknown time-varying uncertainty, and exogenous disturbances. An equivalent-input-disturbance (EID) estimation technique is applied to compensate for the effects of unknown uncertainty and disturbances in the system output. In addition, to reduce the burden of the communication channel in the addressed MSNNs and improve the utilization of bandwidth an event-triggered control protocol is developed to obtain the synchronization of MSNNs. In particular, event-triggering conditions are verified periodically at every sampling instant in both sensors and actuators to avoid the Zeno behavior in the networks. By designing an appropriate low-pass filter in the EID estimator block, the accuracy of disturbance estimation performance is improved. Moreover, by concatenating the synchronization error, observer, and filter states as a single state vector, an augmented system is formulated. Then the tangible delay-dependent stability condition for that augmented system is established by employing the Lyapunov stability theory and reciprocally convex approach. Based on the feasible solutions of the derived stability conditions, the event-triggering parameters, controller, and observer gains are co-designed. Finally, two toy examples are given to illustrate the established theoretical findings.

Disturbance rejection in fuzzy systems based on two dimensional modified repetitive-control.

This paper concerns with the issues of designing an improved-equivalent-input-disturbance (IEID) based robust two dimensional modified repetitive control (2D MRC) for a class of fuzzy systems in the presence of aperiodic disturbances. Specifically, IEID-estimator is implemented to the 2D MRC systems that estimates all types of disturbances and compensates them for assuring robust stability. In particular, the proposed 2D MRC system has two different type of behaviours such as continuous control and discrete learning independently. To obtain gains of the observer and the controller, an adequate set of robust stability conditions is derived in the form of a linear-matrix-inequalities. Finally, simulation results for three numerical examples are provided to depict the efficacy of the proposed control technique.

Estimation and disturbance rejection performance for fractional order fuzzy systems.

This paper gives attention to the issues of output tracking and disturbance rejection performance for a class of fractional order Takagi-Sugeno fuzzy systems in the presence of time-varying delay and unknown external disturbances. More specifically, a new configuration of a fractional order modified repetitive controller that incorporates an improved equivalent-input-disturbance estimator and gain fluctuations in its design is proposed to perform disturbance rejection for the addressed system. By introducing a continuous frequency distributed equivalent model and using the Lyapunov-Krasovskii stability theory, a new set of sufficient conditions ensuring robust asymptotic stability of the resulting closed-loop system is obtained in the framework of linear matrix inequalities. Finally, a numerical example is presented to validate the developed theoretical results, where it is shown that the obtained conditions could force the considered system output to exactly track the given any kind of reference signal by compensating the unknown external disturbance.

Disturbance and uncertainty rejection performance for fractional-order complex dynamical networks.

This paper investigates the synchronization issue for a family of time-delayed fractional-order complex dynamical networks (FCDNs) with time delay, unknown bounded uncertainty and disturbance. A novel fractional uncertainty and disturbance estimator (FUDE) based feedback control strategy is proposed to not only synchronize the considered FCDNs but also guaranteeing the precise rejection of unmodelled system uncertainty and external disturbance. Especially, in FUDE-based approach, model uncertainties and external disturbance are integrated as a lumped disturbance and it does not require a completely known system model or a disturbance model. On the other hand, the design algorithm for the proposed control strategy is based on the state-space framework, rather than frequency-based design methodologies in the literature, which helps for predominant comprehension of the inner system behaviour. Also, by the temperance of Lyapunov stability theory and fractional calculus, a set of adequate conditions in the linear matrix inequality framework is obtained, which guarantees the robust synchronization of the closed-loop system. Furthermore, an iterative optimization algorithm is proposed to improve control robustness against the external disturbance and model uncertainties. Finally, two numerical illustrations including financial network model, where the influence of adjustment of macro-economic policies in the entire financial system are given to exhibit the rightness and important features of the acquired theoretical results.

Passivity and stability analysis of neural networks with time-varying delays via extended free-weighting matrices integral inequality.

This paper is concerned with the problem of passivity for uncertain neural networks with time-varying delays. First, the recently developed integral inequality called generalized free-matrix-based integral inequality is extended to estimate further tight lower bound of integral terms. By constructing a suitable augmented LKF, an enhanced passivity condition for the concerned network is derived in terms of linear matrix inequalities (LMIs). Here, the integral terms having three states in its quadratic form is estimated by the proposed Lemma. As special cases of main results, for neural networks without uncertainties, passivity and stability conditions are derived. Through three numerical examples, it will be shown that the developed conditions can promote the level of passivity and stability criteria.

Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation.

This paper addresses the problem of finite-time synchronization of stochastic coupled neural networks (SCNNs) subject to Markovian switching, mixed time delay, and actuator saturation. In addition, coupling strengths of the SCNNs are characterized by mutually independent random variables. By utilizing a simple linear transformation, the problem of stochastic finite-time synchronization of SCNNs is converted into a mean-square finite-time stabilization problem of an error system. By choosing a suitable mode dependent switched Lyapunov-Krasovskii functional, a new set of sufficient conditions is derived to guarantee the finite-time stability of the error system. Subsequently, with the help of anti-windup control scheme, the actuator saturation risks could be mitigated. Moreover, the derived conditions help to optimize estimation of the domain of attraction by enlarging the contractively invariant set. Furthermore, simulations are conducted to exhibit the efficiency of proposed control scheme.

Consensus of Nonlinear Complex Systems with Edge Betweenness Centrality Measure under Time-Varying Sampled-Data Protocol.

This paper proposes a new consensus criterion for nonlinear complex systems with edge betweenness centrality measure. By construction of a suitable Lyapunov-Krasovskii functional, the consensus criterion for such systems is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the effectiveness of the proposed methods.

Stochastic sampled-data control for state estimation of time-varying delayed neural networks.

This study examines the state estimation problem for neural networks with a time-varying delay. Unlike other studies, the sampled-data with stochastic sampling is used to design the state estimator using a novel approach that divides the bounding of the activation function into two subintervals. To fully use the sawtooth structure characteristics of the sampling input delay, a discontinuous Lyapunov functional is proposed based on the extended Wirtinger inequality. The desired estimator gain can be characterized in terms of the solution to linear matrix inequalities (LMIs). Finally, the proposed method is applied to two numerical examples to show the effectiveness of our result.

Nano-tribological properties of topographically undulated nanocrystalline diamond patterns.

Surface roughness-controlled nanocrystalline diamond film was fabricated as an undulated line and space pattern on a silicon oxide surface. To simulate a MEMS (Micro-/Electro-Mechanical System) and NEMS (Nano-/Electro-Mechanical System) patterned surface, 800 nm and 1 microm wide lines with a 200 nm wide space pattern were prepared on the substrate using E-beam lithography and an ESAND (Electrostatic Self-assembly of NanoDiamond) seeding layer lift-off process. Through this process, an undulated pattern of a nanocrystalline CVD diamond successfully formed by a conventional micro crystalline diamond growth system. The roughness of the deposited surface was controlled by regulating the size of the seeding nanodiamond particles. Crushing of the nanodiamond aggregates and dispersion of the nanodiamond solution was performed in an attrition milling system. An AFM (Atomic Force Microscopy) probe was used for the wear test and surface profiling of nanocrystalline diamond coatings. 2-D friction coefficient mapping by LFM (Lateral Force Microscopy) scanning showed a low friction coefficient (< 0.1) on the line-patterned diamond surface, and a higher friction coefficient (< 0.3) on a narrow area adjacent to the undulated pattern edges. With prolonged LFM scanning, the high coefficient of friction was reduced to less than 0.1. The bonding status of the nanocrystalline diamond was analyzed with Raman spectroscopy.

Ultra thin CVD diamond film deposition by electrostatic self-assembly seeding process with nano-diamond particles.

Ultra thin and smooth nano crystalline diamond films were fabricated with electrostatic self-assembly seeding of explosively synthesized nano-diamond particles. Hard aggregates of nano-diamond particles were crushed by high revolution attrition milling at 1000 RPM to regulate the particle size. Through this process, cationic nano-diamond particles were coated with anionic PSS (poly sodium 4-styrene sulfonate) electrolytes. Anionic Si(100) substrate was coated with cationic PDDA (poly diallyldimethyl ammonium chloride) solution. Si(100)/PDDA/PSS/ND (nano-diamond) layer-by-layer structure was formed as a seeding layer by the simple dipping and rinsing of positively charged substrate into anionic PSS/nano-diamond solution. Throughout the seeding process, neither mechanical damage nor chemical attack was observed on the substrate. Every stage of this preparation was carried out at room temperature and pressure. The effect of attrition milling was determined by changing the milling time from 1 hr to 5 hrs. Through the attritional milling and monolayer formation of the nano-diamond, nucleation density was increased up to 3 x 10(11)/cm2. Typical hot filament chemical vapor deposition system was used to coat the diamond film on the ESA (electrostatic self-assembly) seeded Si(100) substrate. Although typical diamond deposition conditions (90 torr/1% CH4 in H2/800 degrees C) were maintained, ultra thin (< 100 nm) and continuous nano crystalline diamond films were deposited. Regardless of metallic or ceramic substrate, ESND (ESA Seeding of nano-diamond) process is applicable if the substrate has any charge. This simple nano technology based process ensures high thickness uniformity of diamond coating without visible edge effect.