Adaptive neural nonsingular terminal sliding mode control for MEMS gyroscope based on dynamic surface controller
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ORIGINAL ARTICLE
Adaptive neural nonsingular terminal sliding mode control for MEMS gyroscope based on dynamic surface controller Dandan Lei1 · Juntao Fei1
Received: 19 July 2016 / Accepted: 18 January 2017 © Springer-Verlag Berlin Heidelberg 2017
Abstract A novel adaptive dynamic surface control (DSC) method for the micro-electromechanical systems gyroscope, which combined the approaches of a radial basis function neural networks (RBFNN) and a nonsingular terminal sliding mode (NTSM) controller was proposed in this paper. In the DSC, a first-order filter was introduced to the conventional adaptive backstepping technique, which not only maintains the advantage of original backstepping technique, but also reduces the number of parameters and avoids the problem of parameters expansion. The RBFNN is an approximation to the gyroscope’s dynamic characteristics and external disturbances. By introducing a nonsingular terminal sliding mode controller which ensuring the control system could reach the sliding surface and converge to equilibrium point in a finite period of time from any initial state. Finally, simulation results prove that the proposed approach could reduce the chattering of inputs, improve the timeliness and effectiveness of tracking in the presence of model uncertainties and external disturbances, demonstrating the excellent performance compared to nonsingular terminal sliding mode control (NTSMC). Keywords Dynamic surface control (DSC) · Radial basis function neural networks (RBFNN) · Nonsingular terminal sliding mode control (NTSMC)
* Dandan Lei [email protected] Juntao Fei [email protected] 1
College of IOT Engineering, Hohai University, Changzhou 213022, China
1 Introduction Recently, as inertial sensors for measuring angular velocity, MEMS gyroscopes have received increasing attention in many fields such as inertial navigation, automobile and consumer electronics because of theirs compact size, cheapness and much more energy efficiency than conventional macro-sized devices [1–3]. However, considering the manufacturing errors, parameter uncertainties and external disturbances etc, which can cause deviations from the desired qualities and degrade their performance, it is necessary to solve these challenges by utilizing advanced control methods to improve the system robustness in the presence of disturbances, time-varying parameters, and input nonlinearity. Especially in this paper, the proposed method can play a very good role in handling the above problems. During last several years, various control methods have been commonly discussed in MEMS gyroscope to ensure expected performance. Leland [4] presented adaptive controllers for tuning the natural frequency of the oscillation axes of MEMS gyroscopes. The adaptive controllers reported drive both axes of vibration, and control the entire operation of the gyroscope [5, 6]. The comparative studies of system identification of MEMS gyroscope was investigated in [7]. Neural network (NN)’s learning ability to approximate arbitrary smooth nonlinear
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