Adaptive tracking control of an MEMS gyroscope with H-infinity performance

Abstract

Microelectromechanical systems (MEMSs) pose unique measurement and control problems compared with conventional ones because of their small size, low cost, and low power consumption. The vibrating gyroscope is one of those MEMS devices that have significant potential in many industry applications. When the MEMS gyroscope system is considered simultaneously with the coupling terms, the exogenous disturbances and the parameter variations, the controller design of this system becomes very challenging. This paper investigates the primary control problem of a perturbed vibrating MEMS gyroscope. A nonlinear robust adaptive control scheme is proposed for the drive axis of a vibrating MEMS gyroscope. By combining the dynamic surface control (DSC) method with the H-infinity disturbance attenuation technique, a simpler systematic design procedure is developed. The derived H-infinity controller has a simplified structure, and it can drive the drive axis to resonance, regulate the output amplitude of the drive axis to a desired value, and attenuate the generalized disturbances. The features of the derived controller are discussed and illustrated by the simulation of a closed-loop system. The analysis and simulation show that the obtained controller possesses good adaptability and robustness to system uncertainties.

Adaptive control for unknown HOFA nonlinear systems without overparametrization

“…The adaptive control for nonlinear systems is usually based on state space models of nonlinear systems, almost all of which are high‐order differential equations due to well‐known physical laws, for example, Newton's laws, Kirchhoff's laws and so on 1‐9 . For example, the models of one‐link manipulators, 5 quadrotors, 6 fixed wing aircrafts 7 and poppet valves 9 are all three‐order differential equations. In order to design controllers, high‐order differential equations always have to be converted to several first‐order differential equations, which is computationally complex.…”

“…This paper, for the first time, designs an adaptive control scheme for HOFA nonlinear systems with parametric uncertainties based on the tuning function. Compared with the existing methods of controller design, [1][2][3][4][5][6][7][27][28][29][30][31][32][33][34][35][36] the controller is constructed directly for the first subsystem without transform high-order differential equations into several first-order differential equations, which can reduce the complexity of controller design obviously. Although the remaining subsystems are handled by the conventional backstepping method step by step, the stability analysis is achieved for each subsystem as a whole, such that the proof is greatly simplified.…”

Robust tracking and consensus control of a MEMS gyroscope multi-agent systems with H-infinity performance: A transfer function approach

“…According to the author's knowledge, existing techniques focused on the nonlinear consensus control of MEMS gyroscope multi-agent systems in the presence of disturbances, noises, and uncertainties. Further, to identify the fabrication imperfections, an adaptive force balancing filtered error-based control scheme has been proposed in [13]. The proposed work aimed to provide additional richness of excitation to the MEMS gyroscope so that estimate of drive axis tuning, quadrature compensation, ion, tuning and the closed-loop rotation is achievable instead of measuring input and output phase change.…”

“…Moreover, transient response was improved by applying magnitude constraints. To focus on the same issue as in [13], an adaptive force balancing control of the MEMS gyroscope by using the trajectory-switching algorithm was presented in [14]. The proposed technique in [14] identified and reduced the zero-rate output due to cross-damping terms.…”

Design of a fuzzy PID controller for a MEMS tunable capacitor for noise reduction in a voltage reference source

“…:(0123456789) [26]. Moreover, some of the latest research on adaptive and robust control have addressed modeling deficiencies in MEMS, their parametric uncertainties and disorganized dynamics; some have approached fuzzy algorithms [16,21] and some have used ANN-based SMC control [41] and others have benefited robust techniques [11,25]. Mehrnezhad et al [27] employed a state space model in order to apply various advanced control techniques for MEMS tunable capacitors.…”