For characterizing the series augmented railgun launching process, the study from analysis of muzzle voltage was insufficient since armature velocity, resistance and voltage drop of armature were also important to the...
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Based on Lyapunov stability theorem, a MRAC system was researched to solve that differential value of plant output is unknown. Meanwhile an improved design for MRAC system aimed on the factor of restricting convergenc...
Bearings are the most frequently used components in a wind turbine. As such, bearing Fault Detection is an imperative part of preventive maintenance procedures of a wind turbine. This paper presents a Maximum likeliho...
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Considering the slow-moving and energy-consuming issues that tend to occur when truck fleets drive, this paper proposes a hierarchical energy management strategy (EMS) for the hybrid electrical truck (HET) fleet, incl...
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Recent progress in Kalman filters to estimate states and parameters in nonlinear systems has provided the possibility of applying such approaches to neural systems. We here apply the nonlinear method of unscented Kalm...
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Recent progress in Kalman filters to estimate states and parameters in nonlinear systems has provided the possibility of applying such approaches to neural systems. We here apply the nonlinear method of unscented Kalman filters (UKFs) to observe states and estimate parameters in a neural mass model that can simulate distinct rhythms in electroencephalography (EEG) including dynamical evolution during epilepsy seizures. We demonstrate the efficiency of the UKF in estimating states and parameters. We also develop an UKF-based control strategy to modulate the dynamics of the neural mass model. In this strategy the UKF plays the role of observing states, and the control law is constructed via the estimated states. We demonstrate the feasibility of using such a strategy to suppress epileptiform spikes in the neural mass model.
Compound wave defect occurs in flatness during tandem cold rolling when high-end automobile and household appliance plate are produced, which has several impact on line speed and quality of final product. In this pape...
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T-S fuzzy inverse model identification based on particle swarm optimization(PSO) algorithm is proposed for a class of nonlinear process, and it is applied to direct adaptive inverse control of nonlinear system. In the...
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T-S fuzzy inverse model identification based on particle swarm optimization(PSO) algorithm is proposed for a class of nonlinear process, and it is applied to direct adaptive inverse control of nonlinear system. In the first, the fuzzy space is divided by fuzzy grid diagonal method and the Gaussian function with uncertain center and width is used as the membership function, the fuzzy inverse model is obtained by using PSO algorithm to optimize both of the antecedent and consequent parameters in the process of inverse modeling in off-line manner. Afterwards the initial inverse model is connected to the plant in series and the consequent parameters of the inverse model are tuned by the variable-step least mean square(VSLMS) algorithm in on-line manner while its copy is connected to the plant as system controller. This approach realizes the adaptive inverse control of nonlinear system based on PSO algorithm. Simulation results on a hybrid nonlinear system and level control of a spherical tank show the validity of this method.
In this paper, we present the inverse kinematical of a novel 6-DOF orthogonal parallel mechanism. The displacements Δli (i=1,...,6) of steering gears were deduced from the position and orientation of the moving platf...
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In this paper, we study the distributed containment control problem for multi-robot system with dynamic leaders in the presence of parametric uncertainties. The binocular vision system is used as the sensing device to...
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Fuzzy model based chaotic time series prediction has been extensively studied. However, traditional type-1 fuzzy system, whose membership functions are type-1 fuzzy set, has its limitation in handling uncertainties. T...
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