Abstract
In this paper, a dynamic fractional order sliding mode control method based on a double feedback fuzzy neural network controller is proposed to deal with the unknown parameters and upper bound of uncertainty. Firstly, the switching function of the dynamic fractional order sliding mode control is designed, which not only fixes switching function of the ordinary sliding mode control, but also increases the fractional order, so that the switching function has a higher degree of freedom. In addition, the expert experience of fuzzy logic and the self-learning ability of neural network are used to improve the control accuracy and estimate the upper bound of uncertainty. Meanwhile, by using Lyapunov stability theory, the adaptive laws of unknown parameters in the system are derived to realize online adjustment, which increases the robustness of the system. Finally, the simulation results show that the proposed control method is more effective than the ordinary adaptive sliding mode control method in terms of convergence speed, parameter fitting effect, output signal tracking speed and tracking error.
Highlights
Gyroscope is an angular motion detection device, which was first used in navigation
Based on the discussion of the advantages of the above methods, this paper proposes a neural network dynamic sliding mode control method based on fractional calculus for micro gyroscope
DESIGN OF DYNAMIC FRACTIONAL ORDER SLIDING MODE CONTROLLER WITH DOUBLE FEEDBACK FUZZY NEURAL NETWORK Using the neural network designed in the previous section, the upper bound of the lumped uncertainty of the system is approximated: ρ = W T l where the input of the neural network is x = q q T, q and qare the measurable signals in the system, Wis the estimated value of the fuzzy neural network weight, and lis the function about x, c, b, r, Wro
Summary
Gyroscope is an angular motion detection device, which was first used in navigation. With the development of science and technology, the types of gyroscopes have increased, and the application fields have become more and more extensive, such as automobile safety, smartphone [1], aircraft, etc. Micro gyroscope has become one of the important directions for the future development of gyroscopes. Due to the limitation of the technology, its accuracy is far lower than that of traditional gyroscopes, and various control methods can be used to improve its accuracy and performance. In [2], by comparing the working state of the gyroscope with the reference model, the key system parameters can be estimated online using adaptive control method. In [3], a sliding mode control method based on system identification is presented to control autonomous
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