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In this paper, pioneering Adaptive Neuro Fuzzy Inference System (ANFIS) which is trained with well known traditional Proportional, Integral and Derivative (PID) controller data for Half Car (HC) model is proposed to improve the travelling comfort. The travelling performance is generally assessed at the design stage in automobile industries by simulating the vehicle response to various road excitations...
In this paper the modeling and simulation of a class of discrete-time stochastic bilinear systems are studied, the extended least squares (ELS) algorithm are used to estimate unknown parameters of the system, and two simulation examples show that the ELS algorithms of the modeling and simulation are effective for discrete-time stochastic bilinear systems with correlated noises and unknown parameters...
This article applies iterative learning control (ILC) to road simulation test system and simulates the control system to reproduce a stochastic pavement profile. With uniform white noise as input, using actual measured input-output data and dynamic neural network, system nonlinear autoregressive moving average model (NARMA) was established. Regarding road simulator control mission as a perfect tracking...
A class of discrete-time nonlinear system and measurement equations involving incrementally conic nonlinearities with finite energy disturbances is considered. A linear matrix inequality based design approach is presented that guarantees the satisfaction of a variety of performance criteria ranging from simple estimation error boundedness to dissipativity. Simple simulation examples are included to...
An innovative dual version of the direct adaptive pole-placement controller (APPC) is designed, using bicriterial optimization. A new performance index for control optimization of adaptive pole-placement systems is suggested. In contrast to the well-known direct APPC, based on the certainty equivalence (CE) assumption, the accuracy of the parameter estimation and necessity of an optimal excitation...
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