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Considering the time-delay in control input channel and the nonlinear spring stiffness characteristics of suspension, a quarter-vehicle magneto rheological active suspension nonlinear model with time-delay is established in this paper. Based on the time-delay nonlinear model, an adaptive neural network structure for magneto rheological active suspension is presented. By recognizing and training the adaptive neural network, the adaptive neural network active suspension controller is obtained. Simulation results show that the presented method can guarantee that the quarter-vehicle magneto rheological active suspension system has satisfying performance on the E_level very poor ground.
This work is concerned with stabilization control of a class of linear switching systems with time-delays in both the input and the communication channels. It is observed that the time-delay in communication channel leads to the mismatch between the plant and the controller. Such a phenomenon can be accounted for by reconstructing switching signal for the overall closed-loop system. Therefore, we derive some sufficient stability conditions by using multiple Lyapunov functions approach and, moreover, present a robust controller design methodology. A numerical example is presented to demonstrate the effectiveness of the proposed method.
The problem of the gravity information which can not be obtained in advance for bilateral teleoperation is studied. In outer space exploration, the gravity term changes with the position changing of the slave manipulator. So it is necessary to design an adaptive regulator controller to compensate for the unknown gravity signal. Moreover, to get a more accurate position tracking performance, the controller is designed in the task space instead of the joint space. Additionally, the time delay considered in this paper is not only time varying but also unsymmetrical. Finally, simulations are presented to show the effectiveness of the proposed approach.