Publications

This paper proposes a numerical approach for finding an optimal control based on wavelet functions for vibration reduction of a base-isolated building subjected to actual earthquakes. The objective is two-fold: (1) to find a computational method using properties of Haar functions, and (2) to calculate controller gains approximately by solving only algebraic equations instead of solving the Riccati differential. Simulation results are included to demonstrate the validity and applicability of the technique.

This paper presents the design and implementation of a robust drive train torque control for rotor inertia emulation in a dynamometer test bench application. The controller is part of a hardware-in-the-loop (HiL) framework for a 50 kW small test rig that enables a real-time interaction between test rig drive train and a rotor model. The control objective is to compensate undesired dynamics of the test rig, in order to guarantee emulation of model dynamics through HiL simulation within a desired bandwidth. Therefore, a robust H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> controller has been implemented taking advantage of the design in frequency domain. Furthermore, a Kalman filter has been incorporated providing the unmeasured variables in the presence of measurement noise. For the purpose of control performance evaluation, simulation results as well as experimental tests on the hardware are presented.

An input–output approach to the stability and stabilization of uncertain Takagi–Sugeno (T–S) fuzzy systems with time-varying delay is proposed in this paper. The time-varying parameter uncertainties are assumed to be norm-bounded, and the delay is intervally time varying. A novel method is employed to approximate the time-varying delay, based on which the considered system is transformed into a feedback interconnection form. The new formulation of the system is comprised of a forward subsystem with constant time delay and a feedback subsystem embedding the uncertainties. By applying the scaled small-gain theorem to the converted system, less conservative stability and stabilization criteria are obtained. Moreover, the applicability of the proposed approach to the robust case is simpler since both delay and parameter uncertainties are processed in a unified framework. Numerical experiments are performed to illustrate the advantage of the proposed techniques.

An exponential H8 synchronization method is addressed for a class of uncertain master and slave neural networks with mixed time-delays, where the mixed delays comprise different neutral, discrete and distributed time-delays. An appropriate discretized Lyapunov-Krasovskii functional and some free weighting matrices are utilized to establish some delay-dependent sufficient conditions for designing a delayed state-feedback control as a synchronization law in terms of linear matrix inequalities under less restrictive conditions. The controller guarantees the exponential H8 synchronization of the two coupled master and slave neural networks regardless of their initial states. Numerical simulations are provided to demonstrate the effectiveness of the established synchronization laws.Request access from your librarian to read this chapter's full text.

In this paper, the problem of exponential stability is studied for a class of Markovian jump neutral nonlinear systems with mixed neutral and discrete time delays. By Lyapunov-Krasovskii function approach, a novel mean-square exponential stability criterion is derived for the situation that the system's transition rates are partially or completely accessible. Finally, some numerical examples are provided to illustrate the effectiveness of the proposed methods.

The central idea of individual fairness is based on an auspicious yet intuitive assertion: similar individuals should be treated similarly. Nevertheless, the fulfillment of individual fairness is hindered by three major obstacles. First, one needs to determine individuals who should receive similar treatment. Second, seamlessly formulating the notion of individual fairness in an ML learning process is another challenge. Third, effectively evaluating the notion of similar treatment in probabilistic classifiers is another challenge. To overcome these challenges, we propose a novel framework called FairMatch. Our proposed framework offers a new approach to pairing similar and dissimilar individuals using a causal analysis method called propensity score matching. Moreover, we formulate individual fairness as a representation learning problem where we incorporate similar and dissimilar pairs in a triplet-based loss function. Eventually, we devise a novel metric to evaluate individual fairness that captures the notion of similar treatment in probabilistic classifiers in a better way. Experimental results on four real-world datasets verify the superiority of FairMatch to existing solutions where we demonstrate it can deliver fairer decisions without scarifying the predictive performance.

Rolling bearings are crucial for ensuring the safe and stable operation of electromechanical systems. Although deep learning has been widely used in fault diagnosis of rolling bearings, it is unable to accurately diagnose faults when the system operates under multiple working conditions. Therefore, it is essential to conduct research on fault diagnosis of rolling bearings under multiple working conditions to ensure the reliable operation of electromechanical systems. The potential features related to working conditions may be reflected in the different layers of the deep neural network (DNN). However, information loss during the process of layer-by-layer feature extraction may result in the loss of potential features related to changes in working conditions, which in turn affects the fault diagnosis results. This study focused on developing a multiscale recursive fusion strategy for a DNN by designing a new attention model with a lower computational burden. The proposed multiscale recursive fusion strategy guided by the attention mechanism can help correctly characterize the potential features related to variations in working conditions by allocating more attention to useful information and less attention to useless information on the adjacent layers of the DNN. Experimental tests for fault diagnosis of rolling bearings verified that the proposed method is superior to existing methods for fault diagnosis when the system is operated under multiple working conditions.

This paper proposes an observer-based robust guaranteed cost control method for thrust-limited rendezvous in near-circular orbits. Treating the noncircularity of the target orbit as a parametric uncertainty, a linearized motion model derived from the two-body problem is adopted as the controlled plant. Based on this model, a robust guaranteed cost observer-controller is synthesized with a less conservative saturation control law, and sufficient condition for the existence of this observer-based rendezvous controller is derived. Finally, an illustrative example with immeasurable velocity states is presented to demonstrate the advantages and effectiveness of the control scheme.

This paper considers the problem of adaptive fuzzy backstepping-based output-feedback controller design for a class of uncertain switched nonlinear stochastic systems in lower-triangular form without the measurements of the system states. By combining fuzzy logic systems' universal approximation ability and dynamic surface control technique in the adaptive backstepping recursive design with a modified average dwell-time scheme, a new adaptive fuzzy control approach is presented for the switched system. More specifically, a switched observer is constructed to reduce the conservativeness aroused by the employ of a common observer, and individual coordinate transformations for subsystems are given up by adopting a common coordinate transformation of all subsystems. It is proved that the overall closed-loop system is stable in the sense of semi-globally uniformly ultimately bounded in mean square, and the output of the switched system converges to a small neighborhood of the origin with appropriate choice of design parameters. Finally, simulation studies are provided to demonstrate the validity of the proposed control method.

No abstract is provided for this article.

Abstract — This paper considers the problem of stability analysis for a class of production networks of autonomous work systems with delays in the capacity changes. The system under consideration does not share information between work systems and the work systems adjust capacity with the objective of maintaining a desired amount of local work in progress (WIP). Attention is focused to derive explicit sufficient delay-dependent stability conditions for the network using properties of matrix norm. Finally, numerical results are provided to demonstrate the proposed approach. I.

In this paper we present a model in which the time and length are considered quantized. We try to explain the internal structure of the elementary particles in a new way. In this model a super-dimension is defined to separate the beginning and the end of each time and length quanta from another time and length quanta. The beginning and the end of the dimension of the elementary particles are located in this super-dimension. This model can describe the basic concepts of inertial mass and internal energy of the Elementary particles in a better way. By applying this model, some basic calculations mentioned below, can be done in a new way: 1- The charge of elementary particles such as electrons and protons can be calculated theoretically. This quantity has been measured experimentally up to now. 2- By using the equation of the particle charge obtained in this model, the energy of the different layers of atoms such as hydrogen and helium is calculated. This approach is simpler than using Schrodinger equation. 3- Calculation of maximum speed of particles such as electrons and positrons in the accelerators is given.

This paper deals with output ℌ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control synthesis for a linear parameter-varying model of a floating wind turbine. A nonlinear model is introduced and linearized to obtain a linear model for each desired azimuth angle using the wind turbine software FAST. The main contributions of this paper are threefold. Firstly, the family of linear models are represented based on an affine parameter-varying model structure. Secondly, the bounded parameter-varying parameters are removed using upper bounded inequalities in the control design process. Thirdly, the control problem is formulated in terms of linear matrix inequalities (LMIs). The simulation results show a comparison between a controller design based on a constant linear model and a controller based on the linear parameter-varying model. The results show the effectiveness of our proposed design technique.

No abstract is provided for this article.

This article presents a new approach to the vibration mitigation problem in structures subject to seismic motions. These kinds of structures are characterised by the uncertainties of the parameters that describe their dynamics, such as stiffness and damping coefficients. Moreover, the dampers used to mitigate the vibrations caused by earthquakes are usually nonlinear devices with frictional or hysteretic dynamics. We propose an adaptive backstepping controller to account for the uncertainties and the nonlinearities. The controller is formulated in such a way that it satisfies an H ∞ performance. It is designed for a 10-storey building whose base is isolated with a frictional damper (passive device) and a magnetorheological damper (semiactive device). Controller performance is analysed through numerical simulations.