Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathematical point of view. In order to establish a mathematical model of a vehicle crash, one needs to consider various areas of research. For this reason, to simplify the analysis and improve the modeling process, in this paper, a novel adaptive neurofuzzy inference system (ANFIS-based) approach to reconstruct kinematics of colliding vehicles is presented. A typical five-layered ANFIS structure is trained to reproduce kinematics (acceleration, velocity, and displacement) of a vehicle involved in an oblique barrier collision. Subsequently, the same ANFIS structure is applied to simulate different types of collisions than the one which was used in the training stage. Finally, the simulation outcomes are compared with the results obtained by applying different modeling techniques. The reliability of the proposed method is evaluated thanks to this comparative analysis.
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The paper proposes a performance metric evaluation for a distributed detection wireless sensor network with respect to IEEE 802.15.4 standard. A distributed detection scheme is considered with presence of the fusion node and organized sensors into the clustering and non-clustering networks. Sensors are distributed in clusters uniformly and nonuniformly and network has multilevel fusion centers. Fusion centers act as heads of clusters for decision making based on majority-like received signal strength (RSS) with comparison the optimized value of the common threshold. IEEE 802.15.4 Markov chain model derived the performance metric of proposed network architecture with MAC, PHY cross-layer parameters, and Channel State Information (CSI) specifications while it is including Path-loss, Modulation, Channel coding and Rayleigh fading. Simulation results represent significant enhancement on performance of network in terms of reliability, packet failure, average delay, power consumption, and throughput.
This paper presents a novel controller design strategy, which allows computing static output-feedback controllers by solving two successive linear matrix inequality (LMI) optimization problems. This strategy is conceptually simple, easy to implement, computationally efficient, and it can be potentially applied to a wide variety of control problems, in which effective state-feedback controllers can be synthesized by using standard LMI optimization tools. To illustrate the application of the new approach in the field of structural vibration control of large structures, a static velocity-feedback H1 controller for the seismic protection of a five-story building is designed with excellent results.
This article investigates the neural network-based finite-time control issue for a class of nonstrict feedback nonlinear systems, which contain unknown smooth functions, input saturation, and error constraint. Radial basis function neural networks and an auxiliary control signal are adopted to identify unknown smooth functions and deal with input saturation, respectively. The issue of error constraint is solved by combining the performance function and error transformation. Based on the backstepping recursive technique, a neural network-based finite-time control scheme is developed. The developed control scheme can ensure that the closed-loop system is semi-globally practically finite-time stable. Finally, the validity of theoretical results is verified via simulation studies.
No abstract is provided for this article.
This paper investigates the problems of robust stabilization and robust control for the secondary component speed control system with parameters uncertainty and load disturbance. The aim is to enhance the control performance of hydraulic system based on Common Pressure Rail (CPR). Firstly, a mathematical model is presented to describe the hydraulic control system. Then a novel observer is proposed, and an observed-based control strategy is designed such that the closed-loop system is asymptotically stable and satisfies the disturbance attenuation level. The condition for the existence of the developed controller can by efficiently solved by using the MATLAB software. Finally, simulation results are provided to demonstrate the effectiveness of the proposed method.
No abstract is provided for this article.
This work deals with the problem of optimal residual generation for fault detection (FD) in linear discrete time-varying (LDTV) systems subject to uncertain observations. By introducing a generalized fault detection filter (FDF) with four parameter matrices as the residual generator, a novel FDF design scheme is formulated as two bi-objective optimization problems such that the sensitivity of residual to fault is enhanced and the robustness of residual to unknown input is simultaneously strengthened. A generalized operator based optimization approach is proposed to deduce solutions to the corresponding optimization problems in operator forms, where the related H ∞/H ∞ or H − / H ∞ FD performance index is maximized. With the aid of the addressed methods, the connections among the derived solutions are explicitly announced. The parameter matrices of the FDF are analytically derived via solving simple matrix equations recursively. It is revealed that our proposed results establish an operator-based framework of optimal residual generation for some kinds of linear discrete-time systems. Illustrative examples are given to show the applicability and effectiveness of the proposed methods.
This paper focuses on the stability and stabilization problem for a neutral impulsive switching system with time-varying delay. Based on LMI method and optimization technologies, some stability criteria are derived for this kind of system. Some example and numerical simulation are given to demonstrate the effectiveness of our theoretical results.
A policy-iteration-based algorithm is presented in this article for optimal control of unknown continuous-time nonlinear systems subject to bounded inputs by utilizing the adaptive dynamic programming (ADP). Three neural networks (NNs), called critic network, actor network, and quasi-model network, are utilized in the proposed algorithm to give approximations of the control law, the cost function, and the function constituted by partial derivatives of value functions with respect to states and unknown input gain dynamics, respectively. At each iteration, based on the least sum of squares method, the parameters of critic and quasi-model networks will be tuned simultaneously, which eliminates the necessity of separately learning the system model in advance. Then, the control law is improved by satisfying the necessary optimality condition. Then, the proposed algorithm's optimality and convergence properties are exhibited. Finally, the simulation results demonstrate the availability of the proposed algorithm.
No abstract is provided for this article.
This paper investigates the problem of network-based H ∞ output feedback control for a class of stochastic nonlinear systems. A novel model is proposed to describe the systems taken into two sides of communication channels in the network environment, which is more general than one side of communication channel. The design procedure of observer-based controller is presented, which guarantees the asymptotic stability in the mean square of the resulting closed-loop system with an H ∞ performance. Finally, a crane example is utilized to show the effectiveness and potential of the developed techniques.
Turbo-encoded multiple-input multiple-output (MIMO) radio links have recently been proposed for the support of high-speed downlink packet access (HSDPA) in UMTS, where the re-use of spreading codes across the transmitter antennas results in high levels of interference. The state-of-the-art receiver chain for such a link incorporates space-time channel equalization, de-spreading, pre-whitening and finally a posteriori probability (APP) detection. A multi-stage partial parallel interference canceller (MS-PPIC) is considered as a low complexity alternative to the APP detector and its max-log variant. Nonlinear cancellation metrics are derived for the MS-PPIC and its performance is compared with the APP detector for flat and dispersive channels. It is shown that the MS-PPIC can provide similar performance compared to APP and, for low coding rates, superior performance compared to max-log-APP, at a substantially lower computational complexity.
The following work entails the problem of regenerating the hysteresis loop in the Magnetorheological (MR) dampers. The collected data from tests are not sufficient neither efficient for designing optimal controls compensating for the hysteresis in the dampers. This work presents an iterative based approach for estimating the hysteresis parameters, the method however can be generalized for different kind of dampers or actuators hence the hysteresis loop can be generalized using available test data. Some assumptions can be introduced in order to facilitate the underlines of the parameters estimation, one of the assumptions in this work is to use predetermined hysteresis parameters and regenerate the actual data using continuous state space model (SSM). The SSM can be based on verified models like Bouc-Wen, Lugre or Dahl models. In this work, Bouc-Wen model is used to generate the actual hysteresis data. The core of this work is to use the iterative approach based on Particle Swarm Optimization (PSO). The PSO algorithm is used along with other algorithms like the Root Mean Square (RMS) error which is used to evaluate the convergence of the results at each of the iterations for a defined number of iterations. The trade-off relation is the basis of evaluation when using the PSO based algorithm, dependency on initial guessing, number of iterations and desired estimation accuracy. However, the PSO algorithm tend to estimate the hysteresis parameters close enough to generate the actual hysteresis enabling a ground to develop similar algorithms for different kind of actuators or dampers.
The synthesis of optimal controllers for vibrational protection of large‐scale structures with multiple actuation devices and partial state information is a challenging problem. In this study, the authors present a design strategy that allows computing this kind of controllers by using standard linear matrix inequality optimisation tools. To illustrate the main elements of the new approach, a five‐story structure equipped with two interstory actuation devices and subjected to a seismic disturbance is considered. For this control setup, three different controllers are designed: an ideal state‐feedback H ∞ controller with full access to the complete state information and two static output‐feedback H ∞ controllers with restricted neighbouring state information. To assess the performance of the proposed controllers, the corresponding frequency responses are investigated and a proper set of numerical simulations are conducted, using the full scale North‐South El Centro 1940 seismic record as ground acceleration input. The obtained results indicate that, despite the severe information constraints, the proposed static output‐feedback controllers attain a level of seismic protection that is very similar to that achieved by the ideal state‐feedback controller with complete state information.
In this article, the problem of event-triggered <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> control design is investigated for a class of continuous-time switched Takagi–Sugeno (T–S) fuzzy systems. Specifically, the nonweighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance is guaranteed for the system with mode-dependent average dwell-time (MDADT) switching, which is more general than the weighted one in most existing results. Meanwhile, the existence of asynchronous phenomenon between event-triggered instants is taken into account, which is more practical and complicated in the system under consideration. It is compulsive in most existing results that the candidate controllers are synchronous with the subsystems, while this constraint is released to deal with the case that the system switches more than once between two adjacent event-trigger instants. First of all, by verifying the existence of the minimal inter-execution time, it is demonstrated that the adopted event-triggered mechanism can exclude the Zeno behavior. Next, an improved criterion on stability and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance is derived for switched T–S fuzzy systems with MDADT switching. On this basis, the mode-dependent event-triggered mechanism and controllers are co-designed. Finally, two simulations are provided to illustrate the effectiveness of the developed event-triggered control scheme.
This paper is devoted to the issue of observer-based adaptive sliding mode control of distributed delay systems with deterministic switching rules and stochastic jumping process, simultaneously, through a neural network approach. Firstly, relying on the designed Lebesgue observer, a sliding mode hyperplane in the integral form is put forward, on which a desired sliding mode dynamic system is derived. Secondly, in consideration of complexity of real transition rates information, a novel adaptive dynamic controller that fits to universal mode information is designed to ensure the existence of sliding motion in finite-time, especially for the case that the mode information is totally unknown. In addition, an observer-based neural compensator is developed to attenuate the effectiveness of unknown system nonlinearity. Thirdly, an average dwell-time approach is utilized to check the mean-square exponential stability of the obtained sliding mode dynamics, particularly, the proposed criteria conditions are successfully unified with the designed controller in the type of mode information. Finally, a practical example is provided to verify the validity of the proposed method.
Vehicle crash are considered to be events with high complexity from the mathematical points of view. The high experiment cost and huge time-consumption make the establishment of a mathematical model of vehicle crash which can simplify the analysis process in great demand. In this work, 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 be functions of 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 those used for identification. The comparison between the predicted response and the real test data is conducted, which shows the high fidelity of the LPV-ARMAX model.
