Weight Try-Once-Discard Protocol-Based L_2 L_infinity State Estimation for Markovian Jumping Neural Networks with Partially Known Transition Probabilities

It was the L_2 L_infinity performance index that for the first time is initiated into the discussion on state estimation of delayed MJNNs with with partially known transition probabilities, which provides a more general promotion for the estimation error.The WTOD protocol is adopted to dispatch the sensor nodes so as to effectively alleviate the updating frequency of output signals. The hybrid effects of the time delays, Markov chain, and protocol parameters are apparently reflected in the co-designed estimator which can be solved by a combination of comprehensive matrix inequalities

Asynchronous switching control for fuzzy Markov jump systems with periodically varying delay and its application to electronic circuits

This article focuses on addressing the issue of asynchronous H∞ control for Takagi-Sugeno (T-S) fuzzy Markov jump systems with generally incomplete transition probabilities (TPs). The delay is assumed to vary periodically, resulting in one monotonically increasing interval and one monotonically decreasing interval during each period. Meanwhile, a new Lyapunov-Krasovskii functional (LKF) is devised, which depends on membership functions (MFs) and two looped functions formulated for the monotonic intervals. Since the modes and TPs of the original system are assumed to be unavailable, an asynchronous switching fuzzy controller on the basis of hidden Markov model is proposed to stabilize the fuzzy Markov jump systems (FMJSs) with generally incomplete TPs. Consequently, a stability criterion with improved practicality and reduced conservatism is derived, ensuring the stochastic stability and H∞ performance of the closed-loop system. Finally, this technique is employed to the tunnel diode circuit system, and a comparison example is given, which verifies the practicality and superiority of the method

Double Asynchronous Switching Control for Takagi–Sugeno Fuzzy Markov Jump Systems via Adaptive Event-Triggered Mechanism

This article addresses the issue of adaptive event- triggered H∞ control for Markov jump systems based on Takagi-Sugeno (T-S) fuzzy model. Firstly, a new double asynchronous switching controller is presented to deal with the problem of the mismatch of premise variables and modes between the controller and the plant, which is widespread in real network environment. To further reduce the power consumption of communication, a switching adaptive event-triggered mechanism is adopted to relieve the network transmission pressure while ensuring the control effect. In addition, a new Lyapunov-Krasovskii functional (LKF) is constructed to reduce conservatism by introducing the membership functions (MFs) and time-varying delays informa- tion. Meanwhile, the invariant set is estimated to ensure the stability of the system. And the disturbance rejection ability is measured by the optimal H∞ performance index. Finally, two examples are presented to demonstrate the effectiveness of the proposed approach

Fault detection filter and fault accommodation controller design for uncertain systems

Model-based Fault Detection (FD) and Fault Accommodation (FA) approaches have been applied in a variety of cases. We propose several techniques to include uncertainties in the design process. First, we focus on the design of the Fault Detection Filter (FDF) and Fault Accommodation Controller (FAC) for Markovian Jump Linear Systems (MJLS). The MJLS framework allows us to include the network behavior (packet loss) during the design of the FDF and FAC.Second, we propose an FDF and FAC design for the MJLS, under the assumption that the Markov chain mode is not directly accessible. Since we are using the MJLS framework to model the network behavior, the assumption that the network state is not instantly accessible is useful because from a practical standpoint this is a truthful assumption. Third, from the results presented for the MJLS framework, we provided follow-up results using Lur'e Markov Jump System. This is compelling since on some occasions the non-linear behavior cannot be ignored. Therefore, applying the Lur'e MJS framework allows us to consider the same assumptions from MJLS, but now adds the non-linearities. Fourth, we propose the design Gain-Scheduled FDF and FAC for Linear Parameter Varying (LPV) systems, under the assumption that the schedule parameter is not directly acquired. We assume that the schedule parameter is subject to additive noise. This imprecision is included during the design, using change of variables and multi-simplex techniques. Finally, throughout the thesis, we provide some numerical examples to illustrate the viability of the proposed approaches

Scalable consensus filtering for uncertain systems over sensor networks with Round-Robin protocol

Alexander von Humboldt Foundation of Germany; China Postdoctoral Science Foundation. Grant Number: 2017M621242,2020T130092; Fundamental Research Funds for Provincial Undergraduate Universities of Heilongjiang Province of China. Grant Number: 2018QNL-05, KYCXTD201802; National Natural Science Foundation of China. Grant Number: 61873058, 61873148, 61933007, 62073070; Natural Science Foundation of Heilongjiang Province of China. Grant Number: F2018005; PetroChina Innovation Foundation. Grant Number: 2018D-5007-0302

Stochastic Event-Based Control and Estimation

Digital controllers are traditionally implemented using periodic sampling, computation, and actuation events. As more control systems are implemented to share limited network and CPU bandwidth with other tasks, it is becoming increasingly attractive to use some form of event-based control instead, where precious events are used only when needed. Forms of event-based control have been used in practice for a very long time, but mostly in an ad-hoc way. Though optimal solutions to most event-based control problems are unknown, it should still be viable to compare performance between suggested approaches in a reasonable manner. This thesis investigates an event-based variation on the stochastic linear-quadratic (LQ) control problem, with a fixed cost per control event. The sporadic constraint of an enforced minimum inter-event time is introduced, yielding a mixed continuous-/discrete-time formulation. The quantitative trade-off between event rate and control performance is compared between periodic and sporadic control. Example problems for first-order plants are investigated, for a single control loop and for multiple loops closed over a shared medium. Path constraints are introduced to model and analyze higher-order event-based control systems. This component-based approach to stochastic hybrid systems allows to express continuous- and discrete-time dynamics, state and switching constraints, control laws, and stochastic disturbances in the same model. Sum-of-squares techniques are then used to find bounds on control objectives using convex semidefinite programming. The thesis also considers state estimation for discrete time linear stochastic systems from measurements with convex set uncertainty. The Bayesian observer is considered given log-concave process disturbances and measurement likelihoods. Strong log-concavity is introduced, and it is shown that the observer preserves log-concavity, and propagates strong log-concavity like inverse covariance in a Kalman filter. A recursive state estimator is developed for systems with both stochastic and set-bounded process and measurement noise terms. A time-varying linear filter gain is optimized using convex semidefinite programming and ellipsoidal over-approximation, given a relative weight on the two kinds of error

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis