A feedback control method based on an extended state observer (ESO) method is implemented to vibration reduction in a typical semiactive suspension (SAS) system using a magnetorheological (MR) damper as actuator. By considering the dynamic equations of the SAS system and the MR damper model, an active disturbance rejection control (ADRC) is designed based on the ESO. Numerical simulation and real-time experiments are carried out with similar vibration disturbances. Both the simulation and experimental results illustrate the effectiveness of the proposed controller in vibration suppression for a SAS system.
This paper addresses the problem of stochastic dynamic output feedback (SDOF) stabilization for a class of stochastic continuous-time state-delayed systems with norm-bounded nonlinear uncertainties. The aim is to design a linear, delayless, and SDOF control for all admissible uncertainties. The designed control ensures stochastically exponentially stability in the mean square, independent of the deterministic time delay. Using the Finsler's lemma, the necessary and sufficient conditions for the existence of such a control are proposed in terms of certain linear matrix inequalities. These results are illustrated with a simple example to demonstrate the applicability of the proposed design approach.
Vehicle crash modeling and reconstruction is an important field for research since the safety statistics from many countries show that the fatality rate of passenger vehicle occupants involved in road accidents is high. In particular, side impact are considered to be a serious problem. For this reason, in this paper, there is presented a methodology to reconstruct a given vehicle to road safety barrier oblique collision. An easy to analyze, viscoelastic model is established to represent a vehicle crash event. The reasonable modeling simplifications are assumed (namely: the vehicle is rigid and deformation of the safety barrier is negligible) which let the computational efficiency of the proposed approach improve, whereas simultaneously does not affect the accuracy of the simulation results. The Levenberg–Marquardt algorithm is applied to estimate parameters of the two-dimensional Maxwell model which represents a vehicle oblique collision. The obtained results verify that the fidelity of such a model is high and its behavior closely resembles the kinematics of the reference fullscale vehicle.
This paper deals with modelling and adaptive output tracking of a Transverse Flux Permanent Magnet Machine (TFPM) as a non-linear system with unknown nonlinearities by utilizing High Gain Observer (HGO) and Radial Basis Function (RBF) networks. The technique of feedback linearization and H ∞ control are used to design an adaptive control law for compensating the unknown nonlinearity parts, such the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method is shown in the simulation results.
Vehicle crash test is considered to be the most direct and common approach to assess the vehicle crashworthiness. However, it suffers from the drawbacks of high experiment cost and huge time consumption. Therefore, the establishment of a mathematical model of vehicle crash which can simplify the analysis process is significantly attractive. In this paper, we present the application of LPV-ARMAX model to simulate the car-to-pole collision with different initial impact velocities. The parameters of the LPV-ARMAX are assumed to have dependence on the initial impact velocities. Instead of establishing a set of LTI models for vehicle crashes with various impact velocities, the LPV-ARMAX model is comparatively simple and applicable to predict the responses of new collision situations different from the ones used for identification. Finally, the comparison between the predicted response and the real test data is conducted, which shows the high fidelity of the LPV-ARMAX model.
In this paper, the problem of robust synchronization and fault detection for a class of master-slave systems subjected to some nonlinear perturbations and mixed neutral and discrete time-varying delays is investigated based on an H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance condition. 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-dependent linear matrix inequalities to synthesize the residual generation scheme. The explicit expression of the synchronization law is derived for the fault such that both asymptotic stability and a prescribed level of disturbance attenuation are satisfied for all admissible nonlinear perturbations. A numerical example with simulation results illustrates the effectiveness of the methodology.
Focusing on the issue of nonlinear stability control system about the single-stage inverted pendulum, the T-S fuzzy model is employed. Firstly, linear approximation method would be applied into fuzzy model for the single-stage inverted pendulum. At the same time, for some nonlinear terms which could not be dealt with via linear approximation method, this paper will adopt fan range method into fuzzy model. After the T-S fuzzy model, the PDC technology is utilized to design the fuzzy controller secondly. Numerical simulation results, obtained by Matlab, demonstrate the well-controlled effectiveness based on the proposed method for the model of T-S fuzzy system and fuzzy controller.
This paper studies the problem of guaranteed cost control for spacecraft evacuation. The relative dynamic model is established based on Clohessy-Wiltshire (C-W) equations. The paper has taken parameter uncertainty, output tracking, disturbance attenuation, and fuel cost into consideration. The paper introduces a new Lyapunov approach, so the controller design problem can be transferred into a convex optimization problem subject to linear matrix inequality (LMI) constraints. By using the controller, the spacecraft evacuation can be completed in a safe extent. Meanwhile, the fuel cost also has an upper bound. Then the paper analyzes the approach of evacuation and discusses possible initial states of the spacecraft for the controller design. An illustrative example is applied to show the effectiveness of the proposed control design method, and different performances caused by different initial states of spacecraft (-V-bar, -R-bar, and +H-bar) are simulated.
As a complex process, vehicle crash is challenging to be described and estimated mathematically. Although different mathematical models are developed, it is still difficult to balance the complexity of models and the performance of estimation. The aim of this work is to propose a novel scheme to model and estimate the processes of vehicle-barrier frontal crashes. In this work, a piecewise model structure is predefined to represent the accelerations of vehicle in frontal crashes. Each segment in the model is corresponding to the energy absorbing component in the crashworthiness structure. With the help of Ensemble Empirical Mode Decomposition (EEMD), a robust scheme is proposed for parameter identification. By adjusting the model structure and parameters according to the initial velocity, crash processes in different conditions are estimated effectively. The estimation results exhibit good agreement with finite element (FE) simulations in three different cases. It is shown that, the proposed model keeps low complexity. Furthermore, the structure information of vehicle is involved in improving the accuracy and ability of crash estimation.
Notice of Violation of IEEE Publication Principles <br><br> After careful consideration by a duly constituted committee, an author of this article, Hamid Reza Karimi, was found to have acted in violation of the IEEE Principles of Ethical Publishing by artificially inflating the number of citations to this article. <br/> This paper addresses the issue of robust fuzzy sliding mode control for continuous-time nonlinear Takagi-Sugeno fuzzy systems with semi-Markovian switching. The focus is on designing a novel fuzzy integral sliding surface without assuming that the input matrices are the same with full column rank and then developing a fuzzy sliding-mode controller for stochastic stability purpose. Based on Lyapunov theory, a set of newly developed linear matrix inequality conditions are established for stochastic stability of the sliding-mode dynamics with generally uncertain transition rates, and then extended to where the input matrix is plant-rule-independent, as discussed in most existing literatures. Furthermore, finite-time reachability of the sliding surface is also guaranteed by the proposed fuzzy sliding-mode control laws. A practical example is provided to demonstrate the effectiveness of the established method numerically.
This paper investigates the stability problem for a class of uncertain genetic regulatory networks (GRNs) with time-varying delay via delta operator approach. Both the parameter uncertainty and the generalized activations are considered in the model under study. By constructing an appropriate Lyapunov–Krasovskii functional, the stability and robust stability conditions of GRNs are presented under the delta operator frame. These conditions can be expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is employed to illustrate the effectiveness of the proposed results.
Classifier fusion is used to combine multiple classification decisions and improve classification performance. While various classifier fusion algorithms have been proposed in literature, average fusion is almost always selected as the baseline for comparison. Little is done on exploring the potential of average fusion and proposing a better baseline. In this paper we empirically investigate the behavior of soft labels and classifiers in average fusion. As a result, we find that; by proper sampling of soft labels and classifiers, the average fusion performance can be evidently improved. This result presents sampling based average fusion as a better baseline; that is, a newly proposed classifier fusion algorithm should at least perform better than this baseline in order to demonstrate its effectiveness.
In this paper, the problem of robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filtering for stochastic networked control systems is investigated. The parameter uncertainties are time-varying norm-bounded and appear in both the state and input matrices. In the networked control systems (NCSs), the problems of measurement quantization, signal transmission delay and data packet dropout are considered. H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> filter is designed such that the filtering error system is asymptotically stable in the mean square, and the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -induced gain from the noise signal to the estimation error is guaranteed to be less than a prescribed level. Finally, an example is given to show the effectiveness of the theoretical results.
This paper is concerned with the leader-following consensus problem in mean-square for a class of discrete-time multiagent systems. The multiagent systems under consideration are the directed and contain arbitrary discrete time-delays. The communication links are assumed to be time-varying and stochastic. It is also assumed that some agents in the network are well informed and act as leaders, and the others are followers. By introducing novel Lyapunov functionals and employing some new analytical techniques, sufficient conditions are derived to guarantee the leader-following consensus in mean-square for the concerned multiagent systems, so that all the agents are steered to an anticipated state target. A numerical example is presented to illustrate the main results.
