Robust Adaptive Control of MEMS Triaxial Gyroscope Using Fuzzy Compensator

Abstract

In this paper, a robust adaptive control strategy using a fuzzy compensator for MEMS triaxial gyroscope, which has system nonlinearities, including model uncertainties and external disturbances, is proposed. A fuzzy logic controller that could compensate for the model uncertainties and external disturbances is incorporated into the adaptive control scheme in the Lyapunov framework. The proposed adaptive fuzzy controller can guarantee the convergence and asymptotical stability of the closed-loop system. The proposed adaptive fuzzy control strategy does not depend on accurate mathematical models, which simplifies the design procedure. The innovative development of intelligent control methods incorporated with conventional control for the MEMS gyroscope is derived with the strict theoretical proof of the Lyapunov stability. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive fuzzy control scheme and demonstrate the satisfactory tracking performance and robustness against model uncertainties and external disturbances compared with conventional adaptive control method.

Eigen Value Analysis of Optimal Controller Design of Wheeled Autonomous Mobile Robot

“…Comparisons between model based and non-model base controllers and also conventional PID controller for a balancing the robot is a common research interest and has been presented by various researchers. Fuzzy Logic Controllers are non-model based performs better than the LQR and PID controllers in terms faster response and less overshoot, but has higher energy consumption than the other two [2,7,23]. A detailed description for derivation of dynamic mathematical model is discussed and employed in [24].…”

Hybrid Temperature Compensation Model of MEMS Gyroscope Based on Genetic Particle Swarm Optimization Variational Modal Decomposition and Improved Backpropagation

“…Fei et al adopted a robust adaptive control strategy based on a fuzzy compensator, and the effectiveness of the strategy was verified by numerical analysis. (18) In Ref. 19, a radial basis function neural network based on the genetic algorithm (GA) and Kalman filter was adopted, which greatly improved the bias instability of a gyroscope.…”

Accelerated Adaptive Backstepping Control of the Chaotic MEMS Gyroscope by Using the Type-2 Sequential FNN

“…With continuous in-depth research, many practical and effective strategies have been proposed, such as adaptive control, backstepping control, sliding mode control, and fuzzy control (Fei and Zhou, 2012;Luo and Song, 2016;Ouakad, Nayfeh, Choura, and Najar, 2015;Xu, Zhang, Li, He, and Shi, 2019). Aiming at a new 3-D chaotic system with an axeshaped curve of equilibrium points, Vaidyanathan, Sambas, and Mamat (2018)constructed the analog circuits to reveal the dynamic characteristics of this system, and then designed an adaptive synchronization controller to carry out the stable control of this system.…”

“…The integral sliding mode control was designed to synchronize a new 2-scrill chaotic system with four quadratic nonlinear terms, and an analog circuit of the new 2-scroll chaotic system was constructed to check the feasibility of the model (Sambas, Vaidyanathan, Mamat, and Mohamed, 2020). For a threeaxis MEMS gyroscope, Fei and Zhou (2012) discussed a robust adaptive control strategy through the coupling of fuzzy and sliding mode controls. In order to address the control problem of the MEMS resonator, Luo and Song (2016) proposed an adaptive backstepping control method based on RBF neural networks with output constraints and uncertain time delays.…”