Publications

In this paper, first, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is proposed. By using a radial basis function NN (RBFNN), a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. Then, an observer-based adaptive controller based on RBFNN is designed to stabilize uncertain nonlinear systems with immeasurable states. The state-feedback and observer-based controllers are based on Lyapunov and strictly positive real-Lyapunov stability theory, respectively, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems.

This report considers attempts to develop dummy motorcyclists with breakable legs. Material characteristics are discussed. The variation in the scatter fracture load of different materials is compared using the Weibull modulus. The materials used in the different dummy legs have been calibrated statically and uni-axially whereas in crash tests multi-axial dynamic loads are sustained. The Independent Action criterion is used to show that: (1) compressive and torsion loads have only a small effect on bending; and (2) differences in results from different laboratories is the result of scatter in the material characteristics. The effect that leg fracture has on dummy trajectory is described using previously published experimental pedestrian impacts, motorcycle crash tests and pedestrian and car occupant computer simulation studies. Head trajectory is shown to be largely unaffected by leg fracture. For the covering abstract of the conference see IRRD 864606.

This paper deals with a convex optimization approach to the problem of robust network-based H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control for linear systems connected over a common digital communication network with norm-bounded parameter uncertainties. Firstly, we investigate the effect of both the output quantization levels and the network conditions under static quantizers. Secondly, by introducing a descriptor technique, using Lyapunov-Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-range-dependent linear matrix inequalities for the existence of the desired network-based quantized controllers with simultaneous consideration of network induced delays and measurement quantization. The explicit expression of the controllers is derived to satisfy both asymptotic stability and a prescribed level of disturbance attenuation for all admissible norm bounded uncertainties. One example is utilized to illustrate the design procedure proposed in this paper.

This paper is concerned with the state feedback control problem for a class of two-dimensional (2D) discrete-time stochastic systems with time-delays, randomly occurring uncertainties and nonlinearities. Both the sector-like nonlinearities and the norm-bounded uncertainties enter into the system in random ways, and such randomly occurring uncertainties and nonlinearities obey certain mutually uncorrelated Bernoulli random binary distribution laws. Sufficient computationally tractable linear matrix inequality–based conditions are established for the 2D nonlinear stochastic time-delay systems to be asymptotically stable in the mean-square sense, and then the explicit expression of the desired controller gains is derived. An illustrative example is provided to show the usefulness and effectiveness of the proposed method.Keywords: 2D stochastic systemsrandomly occurring uncertaintiesrandomly occurring nonlinearitieslinear matrix inequality (LMI) AcknowledgementsThe third author would like to thank the Alexander-von-Humboldt Foundation for providing the support to this research possible. The authors are also grateful to the anonymous reviewers for their helpful comments.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under [grant number 61273120].Notes on contributorsZhaoxia DuanZhaoxia Duan was born in Sichuan Province, China, in 1989. She received her BS degree in Automation from Nanjing University of Science and Technology (NUST), Nanjing, China, in 2011. Now she is pursuing a PhD degree in Control Theory and Control Engineering from NUST, Nanjing, China. Her current research interests include robust control and filtering, switched systems, two-dimensional systems and nonlinear systems. Display full sizeZhengrong XiangZhengrong Xiang received his PhD degree in Control Theory and Control Engineering at Nanjing University of Science and Technology, Nanjing, China, in 1998. Since 1998, he has been faculty member and he is currently full professor at Nanjing University of Science and Technology. He was appointed as Lecturer in 1998 and Associate Professor in 2001 at Nanjing University of Science and Technology. He is a member of the IEEE, member of the Chinese Association for Artificial Intelligence. His main research interests include switched systems, nonlinear control, robust control and networked control systems. Display full sizeHamid Reza KarimiHamid Reza Karimi is a Professor in Control Systems at the Faculty of Engineering and Science of the University of Agder in Norway. His research interests are in the areas of nonlinear systems, networked control systems, robust control/filter design, time-delay systems, wavelets and vibration control of flexible structures with an emphasis on applications in engineering. He is a senior member of IEEE and serves as chairman of the IEEE chapter on control systems at IEEE Norway section. He is also serving as an editorial board member for some international journals, such as Mechatronics, Neurocomputing, Information Sciences, Asian Journal of Control, Journal of Franklin Institute, Journal of Systems and Control Engineering, and International Journal of Control, Automation and Systems. He is a member of IEEE Technical Committee on Systems with Uncertainty, IFAC Technical Committee on Robust Control and IFAC Technical Committee on Automotive Control. Display full size

This paper presents an adaptive neural control approach for nonstrict-feedback nonlinear systems in presence of unmodeled dynamics, unknown control directions and input dead-zone nonlinearity. To handle the difficulty due to uncertain control directions, Nussbaum gain functions are applied. Based on the structural characteristic of radial basis function neural networks, a backstepping-based adaptive neural control algorithm is developed. The main contributions of this paper lie in the fact that a backstepping-based neural control algorithm is developed for nonstrict-feedback nonlinear systems with unmodeled dynamics, unknown control directions and actuator dead-zone, and the total number of adaptive laws is not greater than the order of control system. As a beneficial result, the controller is much easier to be implemented in practice with less computational burden. A simulation example is given to reveal the viability of the presented approach. It is demonstrated by both theoretical analysis and simulation study that the presented control strategy ensures the semiglobally uniform ultimate boundedness of all closed-loop system signals.

This paper presents application of physical models composed of springs, dampers and masses joined together in various arrangements to simulation of a real car collision with a rigid pole. Equations of motion of those systems are being established and subsequently solutions to obtained differential equations are formulated. We start with a general model consisting of two masses, two springs, and two dampers, and illustrate its application to represent fore-frame and aft-frame of a vehicle. Hybrid models, as being particular cases of two mass-spring-damper model, are elaborated afterwards and their application to predict results of real collision is shown. Models’ parameters are obtained by fitting their response equations to the real vehicle's crush coming from the acceleration measurement analysis. For full-scale experiment and created models we perform both: kinematic and energy responses comparative analysis.

In the practical thickener cone systems, the underflow concentration is hard to measure through physical sensors while there exist the high cost and significant measurement delay. This paper presents a novel and deeply efficient long short-time memory (DE-LSTM) method for concentration prediction in the deep cone thickener system. First, the DE-LSTM for thicker systems is developed for feature learning and long temporal preprocessing. Then, the feedforward and reverse LSTM subnetworks are employed to learn the robust information without loss. At last, the experimental verification of an industrial deep cone thicker demonstrates the proposed DE-LSTM’s performance outperforms other state-of-the-art methods.

The Designs Travel Award for 2018 has been granted to Mr. David Schmelzeisen, a PhD student from Institut für Textiltechnik (ITA), RWTH Aachen University, Aachen, Germany. [...]

This paper reports on a study undertaken within the CEPT SE42 project team, with the objective of deriving regulatory out-of-block radiation limits for mitigating the impact of adjacent-channel interference between TDD and FDD terminal stations (TSs) in the 2.6 GHz band. We present a novel stochastic approach for the calculation of TS block-edge mask (BEM) out-of-block (or baseline) limits. Appropriate BEM baseline limits are subsequently derived via Monte-Carlo simulations based on interferer TS spatial densities commensurate with those observed in busy hot-spots. It is shown that, for a typical activity factor of up to 12.5%, a TS BEM baseline limit of -22.5 dBm/MHz is sufficient to ensure that the victim TS is desensitized by 3 dB with a probability of only 5%.

European administrations are currently in the process of defining national spectrum sharing frameworks to enable coexistence between new entrant Mobile/Fixed Communication Networks (MFCNs) and the incumbent Fixed Satellite Service (FSS) downlink and Fixed Service (FS) in the 3.6-3.8 GHz band. In this paper we propose a methodology for establishing such frameworks whereby administrations define criteria for the protection of the incumbent users in the form of maximum permitted interference levels at the input of the FSS and FS receivers. These limits are then used to calculate either geographic exclusion zones or the maximum permitted radiated powers of MFCN base stations, so as to avoid harmful interference to the FSS and FS. The above calculations can be performed by the MFCN operators, the administration, or a third party. We also set out a number of novel options to account for interference aggregation and the partitioning of the interference budget across multiple MFCN base stations and operators. There is always a trade-off between the simplicity of a spectrum sharing framework and spectrum sharing efficiency. The proposed methodology provides the flexibility for administrations to exploit this trade-off according to their specific national circumstances.

This paper is concerned with the problem of robust reliable control for a class of uncertain discrete impulsive switched systems with state delays, where the actuators are subjected to failures. The parameter uncertainties are assumed to be norm-bounded, and the average dwell time approach is utilized for the stability analysis and controller design. Firstly, an exponential stability criterion is established in terms of linear matrix inequalities (LMIs). Then, a state feedback controller is constructed for the underlying system such that the resulting closed-loop system is exponentially stable. A numerical example is given to illustrate the effectiveness of the proposed method.

This paper deals with the wavelet-based performance analysis of the safety barrier for use in a full-scale test. The test involves a vehicle, a Ford Fiesta, which strikes the safety barrier at a prescribed angle and speed. The vehicle speed before the collision was measured. Vehicle accelerations in three directions at the centre of gravity were measured during the collision. The yaw rate was measured with a gyro meter. Using normal speed and high-speed video cameras, the behavior of the safety barrier and the test vehicle during the collision was recorded. Based upon the results obtained, the tested safety barrier, has proved to satisfy the requirements for an impact severity level. By taking into account the Haar wavelets, the property of integral operational matrix is utilized to find an algebraic representation form for calculate of wavelet coefficients of acceleration signals. It is shown that Haar wavelets can construct the acceleration signals well.

One of the common problems of power quality is the occurrence of voltage sags due to different types of balanced and unbalanced short-circuit faults in electrical distribution systems. Dynamic Voltage Restorer (DVR) is the most effective equipment used for voltage recovery in power distribution systems and it injects voltage in series with line voltage for voltage recovery. In this paper, the structure and general components of this equipment are presented. In addition, by applying a control scheme based on Synchronous Reference Frame Theory (SRFT) in its control system, the effective role of this equipment in compensating and maintaining the voltage of a power distribution system under the occurrence of balanced three-phase short-circuit fault and unbalanced single-phase to ground short-circuit fault is investigated and analyzed using Simulink/Matlab software.