An adaptive dynamic sliding mode control via a backstepping approach for a microelectro mechanical system (MEMS) vibratory z-axis gyroscope is presented in this paper. The time derivative of the control input of the dynamic sliding mode controller (DSMC) is treated as a new control variable for the augmented system which is composed of the original system and the integrator. This DSMC can transfer discontinuous terms to the first-order derivative of the control input, and effectively reduce the chattering. An adaptive dynamic sliding mode controller with the method of backstepping is derived to real-time estimate the angular velocity and the damping and stiffness coefficients and asymptotical stability of the designed systems can be guaranteed. Simulation examples are investigated to demonstrate the satisfactory performance of the proposed adaptive backstepping sliding mode control.
An adaptive H-infinity tracking control is proposed for a z-axis microgyroscope with system nonlinearities. All the signals can be guaranteed in a bounded range, and tracking error is uniformly ultimately bounded, an H-infinity tracking performance is also achieved to a prescribed level. Adaptive control methodology is integrated with H-infinity control technique to achieve robust adaptive control, and adaptive algorithm is used to estimate the unknown system parameters. Simulation studies for microgyroscope are conducted to prove the validity of the proposed control scheme with good performance and robustness.
A fuzzy multiple hidden layer neural sliding mode control with multiple feedback loop (FMHLNSMCMFL) is proposed for a single-phase active power filter (APF), where a sliding mode controller is designed to make the current tracking error converge to zero and a new neural network with multiple feedback loops is introduced to approximate unknown dynamics. At the same time, the fuzzy neural network can eliminate chattering, improve the control accuracy and reduce the current distortion rate of APF. Moreover, the proposed double feedback fuzzy double hidden layer recurrent neural network is the weighted sum of fuzzy network and double hidden layer network and has strong global learning ability. The adaptive parameters obtained by Lyapunov function can ensure the asymptotic stability of the system. Simulation and hardware experiments verify the introduced FMHLNSMCMFL scheme is a viable control solution for the APF.INDEX TERMS Fuzzy multiple hidden layer neural sliding mode control, multiple feedback loop, sliding mode control , active power filter.
This paper proposes a non-singular fast terminal sliding mode control (NFTSMC) method for micro gyroscopes with unknown uncertainty based on gated recurrent fuzzy neural networks (GRFNNs). First, taking advantage of non-singular fast terminal sliding control, a sliding hyperplane is designed with a nonlinear function to ensure that the tracking error of the system converges to zero within a specified finite time. Then, the unknown model parameters of the micro gyroscope are estimated using a GRFNN. Since the GRFNN can adaptively adjust the base width, center vector, gated recurrent unit parameters, and outer gains, it can achieve accurate approximation to unknown models, enhancing the robustness and accuracy. In addition, due to the introduction of gated recurrent units, The GRFNN can effectively utilize the previous data and avoid the problem of gradient disappearance. The comparison of the simulation results with traditional neural sliding mode control shows that the proposed method can achieve better tracking performance and more accurate estimation of unknown models.
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