This paper is concerned with the problem of designing a non-fragile state estimator for a class of uncertain discrete-time neural networks with time-delays. The norm-bounded parameter uncertainties enter into all the system matrices, and the network output is of a general type that contains both linear and nonlinear parts. The additive variation of the estimator gain is taken into account that reflects the possible implementation error of the neuron state estimator. The aim of the addressed problem is to design a state estimator such that the estimation performance is non-fragile against the gain variations and also robust against the parameter uncertainties. Sufficient conditions are presented to guarantee the existence of the desired non-fragile state estimators by using the Lyapunov stability theory and the explicit expression of the desired estimators is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design approach.

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Vehicles driven by in-wheel motors have received more and more attention. However, due to the introduction of inwheel motors, the ratio between unsprung and sprung mass is increased. In this article, to study the influence of this change on ride comfort of vehicles driven by in-wheel motors, an 11 degrees of freedom of vehicle ride comfort model will be presented and studied with MATLAB/Simulink. Then, road tests will be conducted to corroborate the simulation results. It can be obtained that the vehicle ride comfort becomes poor with the increasing unsprung mass. Finally, semi-active air-suspension proportional-integral-derivative control system will be proposed to improve the vehicle ride comfort. Through the simulation results, one can come to a conclusion that the proportional-integral-derivative control system for air suspension is feasible and effective to improve the ride comfort of the vehicles driven by in-wheel motors.

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