Research Article  Open Access
Adaptive Sliding Mode Control of MEMS AC Voltage Reference Source
Abstract
The accuracy of physical parameters of a tunable MEMS capacitor, as the major part of MEMS AC voltage reference, is of great importance to achieve an accurate output voltage free of the malfunctioning noise and disturbance. Even though strenuous endeavors are made to fabricate MEMS tunable capacitors with desiderated accurate physical characteristics and ameliorate exactness of physical parameters’ values, parametric uncertainties ineluctably emerge in fabrication process attributable to imperfections in micromachining process. First off, this paper considers applying an adaptive sliding mode controller design in the MEMS AC voltage reference source so that it is capable of giving off a wellregulated output voltage in defiance of jumbling parametric uncertainties in the plant dynamics and also aggravating external disturbance imposed on the system. Secondly, it puts an investigatory comparison with the designed model reference adaptive controller and the poleplacement state feedback one into one’s prospective. Not only does the tuned adaptive sliding mode controller show remarkable robustness against slow parameter variation and external disturbance being compared to the poleplacement state feedback one, but also it immensely gets robust against the external disturbance in comparison with the conventional adaptive controller. The simulation results are promising.
1. Introduction
Basic principal of AC and DC MEMSbased voltage references was provided by [1] for the first time. They introduced a microsized parallelmoving plate capacitor which is used in a simple circuit to provide a reference voltage. The characteristic of this capacitor which is based on high mechanical stability of single crystalline silicon causes precise output voltage across its plates. Furthermore, low production cost and small size make these voltage references unique devices for electronic applications. After Suhonen, many other researchers started to work on these voltage reference sources to improve their accuracy. Since the fabrication characteristics of MEMS tunable capacitor as the main element of MEMSbased voltage references are very critical, there are several papers which are focused on micromachining process of the MEMS tunable capacitors [2–7]. The main purpose of these works is to propose and fabricate new designs of microsized tunable capacitors and show the improvement of obtained MEMSbased voltage references.
Utilizing tunable capacitor in MEMSbased voltage references is associated with pullin phenomenon. This property stems from electromechanical coupling between the electrostatic force being applied on the capacitor plates and the spring force. As it is illustrated in Figures 1(a) and 1(b), the electrostatic force intensifies as the electrical charge across the capacitor plates accumulates. Thus, movable plate of capacitor is steered towards the fixed plate gently, until the spring force can no longer withstand the electrostatic force. This point is acquainted as the pullin point. Near the pullin point, the voltage alteration across capacitor plates is proportional to the square of deflection in the movable plate. Hence, this capacitor can furnish a regulated voltage while working around the pullin point. Consequently, the MEMSbased voltage references are exploited near the pullin point of their MEMS tunable capacitors. According to [8], mechanical stress and electrical charge over dielectric layer are two reasons which cause instability around the pullin point. They stated that using AC voltage, instead of DC one, decreases the charge aggregation across the dielectric layer. Using AC voltage as the input resulted in appearance of MEMSbased AC voltage reference sources. In this paper, the operation of these MEMSbased AC voltage reference sources is considered. Nowadays, the advanced microfabrication technology helps us to create MEMS tunable capacitors with highly accurate dimensions and geometries and less mechanical stress. Nevertheless, applying controllers to MEMSbased voltage reference sources is necessary because it has great impact on increasing the long term stability and accuracy of reference voltage. It is not blowing it out of proportion that applying various control algorithms to MEMS devices to yield error subside and a better performance is a nonnegligible problematic issue [9–14]. In addition, adaptive and robust control methods lie in the center of meticulousness and challenge due to deficiency in modeling of MEMS devices and the parametric uncertainties in fabrication procedures [15–22].
(a)
(b)
(c)
(d)
A state space description was proposed in [23] to apply various control algorithms to MEMS tunable capacitors. The contemplation of the controller is to place and maintain the movable plate of the capacitor in the pullin point while the capacitor’s parameters, like spring stiffness, get changed during the time. When the system’s parameters are deemed steady, this aim is obtained utilizing a simple poleplacement state feedback controller. Nevertheless, the problem arises when we would know that there are some ineluctable uncertainties in the system parameters. For instance, the exact value of spring stiffness is not known.
This paper considers application of an adaptive sliding mode (ASM) controller design [24] to control the system and attain perfect tracking so that the position trajectory of the movable plate would flawlessly track the desired pullin trajectory in defiance of jumbling uncertain parameters and also aggravating external disturbance; it is beneficial when showing conceded robustness against the disturbance which is imposed on the system from the input channel. It also shows up remarkable robustness against the slow parameter variation; the designed presented scheme is also capable of perfect tracking when the parameters vary slowly. It is worth notifying that the spring stiffness and damping coefficients could alter gradually as the environmental conditions, like temperature, get changed.
2. Operating Principles
In this section, operating principles of the MEMS tunable capacitor are investigated and its characteristics are illustrated. A tunable capacitor has a fixed plate and a movable one. In this paper, one of the typical tunable capacitors, depicted on Figures 1(c) and 1(d), is conversed. It is a MEMS parallel plate tunable capacitor and incorporates a movable plate which alters the interplate gap. As it got mentioned in Introduction, the challenging issue, that we are dealing with in this paper, is assuaging the tracking performance quality of the electrically aggregated plate in the tunable capacitor in the presence of parameter uncertainty. It is depicted by simulating the parallel plate in Abaqus software that hardly does the plate, designed according to the structural parameters given in [26], displace so that it would not get maintained in the desiderated pullin point, as seen in Figure 2. It is conjectured that the simplifying assumption in modeling procedure could be one source of parameter uncertainty in stiffness factor of the suspending beams. One would refer to the depletion of desired mechanical characteristic due to aging and alteration of working condition which consequently results in parameter uncertainty. Unfortunately, defects in fabrication procedure can be another source of slight uncertainty in dominating parameters like stiffness and damping factors. That is why contemplating a control system which would cope with parameter uncertainty could not be deprived of worthwhile challenge and attention.
(a)
(b)
Capacitance of a MEMS tunable capacitor changes with the gap; it is defined byThe parameters in (1), , , , and , consecutively symbolize the permittivity of the medium, the electrode area, the initial gap between plates, and the movable plate deviation. The capacitor is actuated with an AC current defined byElectrostatic force captivates electrodes towards each other whereas spring force, defined by , attempts to impede plates from appealing to each other. These two forces are balanced through (3) looking at Figure 1(d) [27]. This balance strive is depicted in following equation [26]:The variables and parameters in (3), , , , , , and , are, respectively, the electrostatic force generated by the AC current, the electrostatic energy, plate distance, spring constant, mass of the movable electrode, and damping coefficient due to existing gas between plates. The actuation current frequency, , is nearly supposed to be much higher than the mechanical resonance frequency defined by . Moreover, the movement of the movable plate gets damped when the stable position is at the access. The damping coefficient is related to gas damping of the component. The mechanical quality factor of the moving plate capacitor, called , can be controlled by adjusting the gas pressure inside the component enclosure:Neglecting the slide film damping with regard to movement constraint, is charged for squeeze film one in subsequent simulation; however, this effect is handled physically by certain fabrication precautions. For a quantitative analysis the compressibility of the gas needs to be taken into account. The precautionary actions go as follows regarding [26]: (i)Device is operated at a frequency much higher than the resonance frequency since the gas has enough time to flow away from the gap causing dissipation in low frequencies. Increasing the frequency, the gas film is trapped and squeezed between the electrodes and behaves like a spring with low dissipation.(ii)The sensor is encapsulated in an inertial protective gas such as 1 atm nitrogen or inert gases like argon. High gas pressure works as a mechanical damper; the pressure inside the cavity is high enough to overdamp the cantilever movement in such a way that the mechanical value is well below .(iii)Nice sealing packages such as metal packages (TOcan and DIL) and Low Temperature Cofired Ceramic (LTCC) are applied on the hermetic package not only to hamper gas leakage and pressure fall but also to reduce sensitivity to changes in environmental humidity and pressure.(iv)Before the final lid seal, a bake is usually performed to drive out any trapped gas or moisture since metal surfaces can be very hydrophilic and any residual chemicals from the manufacturing process may react chemically and cause voltage drift.(v)An optimizing movable plate mass calculation should be done to minimize molecular vibration of the damping gas and the consequent mechanical noise of the component. The molecules in any substance at a temperature above absolute zero are permanently vibrating which causes arbitrary micrometer displacements resulting in noise floor of the voltage reference. Other solutions such as electronic filtration and bulkmicromachining instead of surface μM and the seesaw electrode which is damped by atmospheric pressure all could contribute to ameliorating rejection of voltage floor noise.Position of the movable electrode is approximately constant during sinusoidal alterations in the AC current. Taking this into account, the electrostatic force in (3) can be substituted with the mean value of the electrostatic force. This brings about (5). Note that and in the steady state, around the pullin point, and approximate to zero.The electrostatic force which is applied on the movable plate can also get calculated byIn the above equation, represents the Root Mean Square (RMS) of the AC voltage across the plates and is the electrostatic energy. Considering in terms of from (5) and supplanting in (6) with the latter result yield (7) which asserts in terms of [23].Taking derivative of with respect to results inWe may attain the value of setting (8) to zero. It is equal to . Supplanting in (7) with yields called the reference or pullin voltage:The value of the corresponding is obtained by (10) considering (5) and (6) and also taking into account the attained maximum and its corresponding [23].
The major working point of MEMS tunable capacitors, in voltage reference source circuits, is the pullin point. Consequently, small deviation in the input current around the pullin point and its influence over the output voltage should be studied scrupulously. Using instead of in (7) results inEquation (12) is achieved by dividing the above equation by .
Considering (12), it can be asserted that the voltage variations in the pullin point are approximately proportional to the square of the current variations. Hence, small variations in the input current with respect to the pullin current bring about a very small alteration in the pullin voltage. This demonstrates that the tunable capacitor can be utilized as the major element in a voltage reference circuit. As mentioned before, assuming the actuation frequency to be much higher than the mechanical resonance frequency, the electrostatic force in (3) could be superseded with the mean value of the electrostatic force. This results inThe describing equation of the plant can be modified as a nondimensional one by adopting as and also dividing it by :Dividing the above equation by results inVariables and parameters are defined as follows; , , , , , and ; the describing plant equation comes asOmitting the prime symbol just to simplify representation of the above equation, the following dynamics come to describe the plant:in which , , and are different from the ones in (13) or (14).
3. Controller Design
Considering the parametric uncertainties due to deficiencies in fabrication procedures and physical dimensions as and and remarking the disturbance imposed on the system from the input channel as , the system dynamics are modified as follows:All the parametric uncertainties and disturbance can be lumped by and the dynamics will consequently change intoAssuming parametric uncertainties and disturbance to be confined so that , , and , the lumped uncertainty will be bounded like this:The reason is that We can also consider reference model describing equation asto generate the desired trajectory. Note that the polynomial, , is a Hurwitz one in terms of which guarantees the stability of the reference model. One would be able to tune the transient response specification adjusting and . Unaffectedly, the tracking error is defined byThe sliding surface is also considered asin which is a positive constant. Now, we would achieve timederivative of the sliding surface:Remarking (19) and (22), the timederivative of the sliding surface gets equal toin which and are consecutively the regressor (trajectory) vector and the parameters’ one. They are defined by the following terms: is a function of trajectories. It is delineated since it is comprised of the accessible (measurable) states [23]. Considering (26), the following equivalent control law is adopted to zero variation of the sliding surface relative to time [28].Regarding the unknown parametric uncertainties and disturbance, the control law is modified asin which is defined byand is the estimation of the unknown system parameters. In (31), is a positive constant which would be defined later and is the sliding surface in (24). It is noteworthy that the other terms in the adopted control law are defined or accessible ones. Now, we would simplify the timederivative of the sliding surface regarding the control law in (30) and try to make the product of the regressor and parameters’ estimation error vectors appear in the timederivative of the sliding surface:In (32), is the parameters’ estimation error vector.
At this stage, we consider an augmented system of estimation and tracking errors so a Lyapunov candidate, as a function of the parameters’ estimation error and the tracking error of the desired trajectory (sliding surface), is proposed. We may try to proffer the adaptive law (parameters’ update law) so that the selected Lyapunov candidate function, , grows negative semidefinite. It brings about the boundedness of the tracking and parameters’ estimation errors and consequently the stability of the system. The Lyapunov candidate function is proposed asin which is a symmetric positive definite matrix. Differentiating the Lyapunov candidate relative to time leads to the following equation:Regarding (32), is obtained asIt could be more simplified in the following: We may zero the abovementioned signindefinite term trying to make the timederivative of the Lyapunov function negative finally:Note that is a symmetric matrix so . The reason is illustrated below:Considering the environmental effects, the physical properties, and the working conditions of the device, it is assumed that the parameters vary very slowly; . Therefore, (37) turns intoAccording to (36), (37), and (39), the timederivative of the Lyapunov candidate equalsWe may write the following inequality:and consequentlyIf the positive constant is adopted so that , (42) would change into
Equation (43) is the sliding condition which vouches for the trajectories to reach the sliding surface in finite time and remain there [28]. It also demonstrates that the Lyapunov function is negative semidefinite and the tracking and the parameters’ estimation errors inevitably remain bounded. Therefore, . Regarding (20), (32), and (27), we may conclude that . In other words,On the other hand, is negative semidefinite so is nonincreasing:Integrating over the sliding condition, (43), we may write is bounded, is nonincreasing, and consequently inequality (46) turns intoTherefore, . Now, regarding the results, , , and , Barbalet’s Lemma may be used to prove the zeroconvergence of the composite error or the same sliding surface [28]:
Ultimately, we have attained the goal of perfect tracking of the desired trajectory of the capacitive plate regarding (24) and (23); the plate would be placed in its right desired position in spite of the parametric uncertainties and the external disturbance.
The block diagram of the control scheme of the poleplacement state feedback controller is also displayed in Figure 3(a). The aim is controlling the movement of the capacitive plate so that it tracks the desired trajectory. Design of this controller is based on the convergence and stability of the tracking error [29]. The states are practically at the access for designing the closed loop control system. The capacitive plate should move in micrometers following the desired trajectory. Considering the desired trajectory and its first and second derivatives as , , and , the control input may be constructed by feedback of displacement and velocity of plate, and , as follows: is a tuning constant to adjust the response settling time and . Utilizing the control input and the feedback of the states, the control law would be stated by the following equation:
(a)
(b)
(c)
(d)
It is of great significance that the computed force described by (50) vouches for the stability of the error dynamics so that the tracking error tends to zero bringing about perfect tracking of the desired trajectory. If one substitutes in (50) for in (17), he would achieve stable error dynamics. Here is the verification of the statements: Equation (51) represents a Hurwitz characteristic equation so that tends to zero promisingly.
4. Simulation Results
The overall poleplacement state feedback controller system, displayed in Figure 3(a), is mainly comprised of 4 parts: plant dynamics (MEMS_AC_V_Ref or MEMS tunable cap.), reference model or desired trajectory generator, control input (ctrinput), and control law (ctrlaw). The poleplacement state feedback controller is simulated using parameter values of Table 1. The simulation results are seen in Figure 3(b). The red and blue curves are consecutively desired and real trajectories. The simulation is carried out under normal operating conditions. As it is observed, capacitive plate has tracked the desired trajectory. The point is that when the parameter uncertainty or the disturbance is considered in the plant dynamics, the overall control system loses its accuracy in tracking the desired trajectory or gets unstable. These claims are to be demonstrated through simulation in the following.

The overall control scheme of the adaptive sliding mode controller is seen in Figure 3(c). It is composed of 4 main blocks: the plant, the model reference, the control law, and the parameters’ update law (estimator). The adaptive sliding mode controller is also simulated using the parameter values in Table 1. In the simulations, a comparison between the adaptive sliding mode controller and the poleplacement and conventional adaptive ones has been illustrated. The controllers are simulated under the same operating conditions.
In the first comparative simulation, malfunctioning effect of parametric uncertainty is investigated. The overall adaptive sliding mode controller system is simulated with parameter values of ninety percent of the nominal ones. As it is observed, the adaptive controller has reached the goal of perfect tracking of desired position of the capacitive plate but the poleplacement controller has not. See Figures 4(a) and 4(b). The red and blue curves, in these figures, are correspondingly the defined tracking error and sliding surface on the first one and tracking error on the second. The estimated values of the plant parameters, damping and stiffness factors, remain bounded. The estimated value of stiffness converges to real one.
(a)
(b)
(c)
(d)
In the second comparative simulation, malfunctioning effect of slow parameter variation is theoretically investigated. The overall adaptive sliding mode controller system is simulated with the parameter variation pattern of . As it is observed, the tracking performance has not collapsed seriously being compared with the tracking performance of the poleplacement state feedback controller. See Figure 4(c). The poleplacement controller is simulated considering the same parameter variation pattern. As it is seen in Figures 4(c) and 4(d), the tracking performance in the poleplacement controller has got ruined much more apparently and the goal of perfect tracking of desired position of the capacitive plate is out of reach. The capacitive plate is oscillating in an unacceptable range and it is not placed in its right desired position. The estimation errors remain bounded proving the adaptive system stability. The red and blue curves in Figures 4(c) and 4(d) have the same meaning correspondingly in Figures 4(a) and 4(b).
In the third comparative simulation, the two systems are simulated being subjected to the external constant disturbances which are imposed on the systems from the input channel. The tracking performance has got ruined in the presence of external constant disturbance in the poleplacement state feedback controller comparing Figures 5(a) and 5(b). The red and blue curves in these figures, on the upper graphs, come in affinity with the ones in Figures 4(c) and 4(d). On the lower graphs, they are consecutively disturbances and control efforts. As it can be observed, the poleplacement controller has lost its tracking performance being subjected to a much less disturbance relatively in comparison to the adaptive sliding mode controller. The tracking errors are seen in Figures 5(a) and 5(b). It is noteworthy that the promising simulation results demonstrate that the poleplacement controller gets unstable as the imposed disturbance grows higher. Again, the estimation errors remain bounded proving the stability of the adaptive sliding mode controller and the stiffness factor gets converged to the real value.
(a)
(b)
In the fourth comparative simulation, the systems are simulated being prone to the external random disturbances. They are imposed on the system from the input channel. It would be concluded that the tracking performance has collapsed intensively in the presence of external random disturbance in the poleplacement state feedback controller; an intuitive comparison can be made between Figures 6(a) and 6(b). Again, the red and blue curves are the familiar plots presenting the tracking error, the sliding surface, disturbance, and control effort. The poleplacement controller is deprived of perfect tracking investigating the tracking error displayed in Figure 6(b). The simulation results pledge the stability of the adaptive sliding mode controller bringing about boundedness of estimation errors.
(a)
(b)
In the fifth and final comparative simulation, the robustness of the adaptive sliding mode controller and that of the conventional adaptive one against external disturbance are also compared. The conventional adaptive controller scheme [25] is displayed in Figure 3(d). As it can be seen in Figure 7(a), this controller is incapable of perfect tracking of desired position of the capacitive plate. The simulation is carried out using parameter values of Table 1. The disturbance imposed on the adaptive sliding mode controller is considered greater than the disturbance imposed on the conventional adaptive one. The red curves are correspondingly the desired trajectory and the disturbance imposed on the system and the blue curves are the real trajectories and the control efforts, in Figures 7(a) and 7(b). The switching control effort, in (31), is the main cause of reinforced disturbance rejection mechanism. Decreasing chattering effect, other socalled “smooth transient switching functions” like or , which is used in the simulations, could be used in the proposed adaptive sliding mode controller [28].
(a)
(b)
The last important issue which is worthwhile being notified here is that, from a mechanical engineering vantage point, the physical system should keep up with the control effort vacillation and its consequent frequency impact on the mechanical behaviour. According to the modal analysis of the tunable capacitor carried out by Abaqus software, the socalled “starting mode” or the first mode is cycles/s. See Figures 8(a) and 8(b). As it is depicted in Figure 8(c), the control effort signal in frequency domain does not comprise the troublemaking frequency of the first mode and beyond, inhibiting vulnerability of deformation. It does not make any difference if the disturbance is considered or both disturbance and parameter uncertainty are taken into account.
(a)
(b)
(c)
5. Conclusion
MEMS tunable capacitors can be modeled as the major elements of the AC voltage reference sources. The movable plate of capacitor should be maneuvered towards the pullin point obtaining the desired output voltage. The uncertainties in the dynamics of this device make it difficult to accomplish the aim of perfect tracking utilizing the simple poleplacement state feedback controller or other classic ones. Meticulous endeavors are devoted to fabrication procedures to obtain the requested physical characteristics of the tunable capacitor. Nonetheless, some parametric uncertainties ineluctably transpire in some parameters of the device like stiffness and damping coefficients. The suggested adaptive sliding mode controller is competent of flawless tracking of desiderated position trajectory of the movable plate in defiance of the parametric uncertainties and also external disturbance. As a result, a wellregulated output voltage is given off by the voltage reference source. The overall system has the benefit of disturbance rejection being compared with the conventional adaptive controller; it also shows remarkable robustness against the exacerbating disturbance imposed on the system and the slow parameter variation.
6. Future Work
Leaving no stone in micrometer dimensions unturned, it should be notified that the damping coefficient may function as the displacement in a nonlinear manner. According to [30], the following equation comes to describe squeezed film damping.where is the ambient pressure, and are odd integers, and and for a plate with width and length . Plate separation is and squeeze number works as a function of frequency :in which is the gas effective viscosity. For larger frequencies, it seems that damping coefficient functions as the frequency nonlinearly:In this way, damping factor keeps constant but whether this approximating conclusion holds to be true needs verification research to be carried out. Another issue which requires one’s prospective attention is parameter identification working alongside the adaptive controller to estimate stiffness and damping factors for catalogue purposes. Furthermore, design and tuning a fuzzy adaptive sliding mode controller to bring more robustness to the controller and investigation of its possible landmark effect over rejection of disturbance and reduction of voltage noise floor could be deemed as other upcoming research field.
Disclosure
Ali Mehrnezhad is currently a Ph.D. student in Electronics area in Electrical Engineering: MicroScaled Biomedical Device Lab, Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA 70803, USA.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Acknowledgments
The authors wish to acknowledge the support of MEMS/NEMS Dynamics Research Lab in AUT.
References
 M. Suhonen, H. Seppa, A. Oja, M. Heinila, and I. Nakki, “AC and DC voltage standards based on silicon micromechanics,” in Proceedings of the Conference on Precision Electromagnetic: Measurements, pp. 2324. View at: Google Scholar
 A. Karkkainen, N. Pesonen, M. Suhonen et al., “AC voltage reference based on a capacitive micromechanical component,” in Proceedings of the 2004 Conference on Precision Electromagnetic Measurements, pp. 119120, June 2004. View at: Publisher Site  Google Scholar
 A. Kärkkäinen, P. Pekko, J. Dekker et al., “Stable SOI micromachined electrostatic AC voltage reference,” Microsystem Technologies, vol. 12, no. 12, pp. 169–172, 2005. View at: Publisher Site  Google Scholar
 F. Blard, A. Bounouh, D. Bélières, and H. Camon, “Very high stability achievement in MEMS based AC voltage references,” in Proceedings of the 24th IEEE International Conference on Micro Electro Mechanical Systems, MEMS '11, pp. 656–659, January 2011. View at: Publisher Site  Google Scholar
 A. Bounouh, H. Camon, D. Bélières, F. Blard, and F. Ziadé, “MEMS AC voltage reference for miniaturized instrumentation and metrology,” Computer Standards and Interfaces, vol. 33, no. 2, pp. 159–164, 2011. View at: Publisher Site  Google Scholar
 A. Bounouh, H. Camon, and D. Bélières, “MEMS based AC voltage references with very high stability,” in Proceedings of the 2012 Conference on Precision Electromagnetic Measurements, CPEM '12, pp. 552553, July 2012. View at: Publisher Site  Google Scholar
 A. Bounouh, H. Camon, and D. Bélières Denis, “Wideband high stability MEMSbased AC voltage references,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 6, pp. 1646–1651, 2013. View at: Publisher Site  Google Scholar
 A. Kärkkäinen, A. Oja, J. Kyynäräinen, H. Kuisma, and H. Seppä, “Stability of electrostatic actuation of MEMS,” in Proceedings of the 20th Nordic Semiconductor Meeting, NSM20, pp. 193194, August 2003. View at: Publisher Site  Google Scholar
 J. Fei and C. Batur, “A novel adaptive sliding mode control with application to MEMS gyroscope,” ISA Transactions, vol. 48, no. 1, pp. 73–78, 2009. View at: Publisher Site  Google Scholar
 C. Batur, T. Sreeramreddy, and Q. Khasawneh, “Sliding mode control of a simulated MEMS gyroscope,” ISA Transactions, vol. 45, no. 1, pp. 99–108, 2006. View at: Publisher Site  Google Scholar
 N. SadeghzadehNokhodberiz and J. Poshtan, “Loosely coupled fusion of camera and inertial sensors for distributed error compensation in strapdown inertial navigation system,” Transactions of the Institute of Measurement and Control, vol. 38, no. 11, pp. 1283–1297, 2016. View at: Publisher Site  Google Scholar
 B. Parsi, M. Bahrami, A. M. Esfahani, and B. S. Sany, “Calibration verification of a lowcost method for MEMS accelerometers,” Transactions of the Institute of Measurement and Control, vol. 36, no. 5, pp. 579–587, 2014. View at: Publisher Site  Google Scholar
 Q. Xu, P.K. Wong, M. Jia, M. Rakotondrabe, and L. Zhang, “Advanced control in micro/nanosystems,” Journal of Control Science and Engineering, vol. 2012, Article ID 827479, 2 pages, 2012. View at: Publisher Site  Google Scholar
 M. Meenakshi and M. S. Bhat, “Realtime fixedorder lateral H2 controller for micro air vehicle,” Journal of Control Science and Engineering, vol. 1, 15 pages, 2011. View at: Google Scholar
 J. Fei, “Robust adaptive vibration tracking control for a microelectromechanical systems vibratory gyroscope with bound estimation,” IET Control Theory and Applications, vol. 4, no. 6, pp. 1019–1026, 2010. View at: Publisher Site  Google Scholar
 J. Fei, M. Xin, and W. Juan, “Adaptive fuzzy sliding mode control using adaptive sliding gain for MEMS gyroscope,” Transactions of the Institute of Measurement and Control, vol. 35, no. 4, pp. 551–558, 2013. View at: Publisher Site  Google Scholar
 W. Juan and J. Fei, “Adaptive fuzzy approach for nonlinearity compensation in MEMS gyroscope,” Transactions of the Institute of Measurement and Control, vol. 35, no. 8, pp. 1008–1015, 2013. View at: Publisher Site  Google Scholar
 D. Wu and J. Fei, “Adaptive neural sliding control of MEMS gyroscope with robust feedback compensator,” Transactions of the Institute of Measurement and Control, vol. 38, no. 4, pp. 414–424, 2015. View at: Publisher Site  Google Scholar
 A. Modirrousta, M. Shokrian Zeini, and T. Binazadeh, “Nonlinear optimal fuzzy control synthesis for robust output tracking of uncertain microelectromechanical systems,” Transactions of the Institute of Measurement and Control, vol. 39, no. 8, pp. 1146–1160, 2016. View at: Publisher Site  Google Scholar
 D. Derawi, N. D. Salim, H. Zamzuri, M. A. A. Rahman, and K. Nonami, “Robust attitude control design for a lowcost hexarotor micro aerial vehicle (MAV),” Transactions of the Institute of Measurement and Control, 2016. View at: Publisher Site  Google Scholar
 S. Choura, N. Aouni, and S. ElBorgi, “On the control of vibratory MEMS gyroscopes,” Smart Structures and Systems, vol. 6, no. 7, pp. 793–810, 2010. View at: Publisher Site  Google Scholar
 F. A. Hassane, A. F. Payam, and M. Fathipour, “Design of a smart MEMS accelerometer using nonlinear control principles,” Smart Structures and Systems, vol. 6, no. 1, pp. 1–16, 2010. View at: Publisher Site  Google Scholar
 A. Mehrnezhad, A. A. Suratgar, S. Khatami, and S. Sobhiyeh, “A mathematical dynamic model for static and dynamic behaviours of MEMSbased AC voltage reference source,” in Proceedings of the 2013 21st Iranian Conference on Electrical Engineering, ICEE '13, May 2013. View at: Publisher Site  Google Scholar
 J. Fei, Advanced Control Design of MEMS Vibratory Gyroscope, Nova Science Publishers, New York, NY, USA, 2012.
 E. Ranjbar, A. Mehrnezhad, A. A. Suratgar, and S. Khatami, “Adaptive control of MEMSbased AC voltage reference source,” in Proceedings of the 22nd Iranian Conference on Electrical Engineering, ICEE '14, pp. 1336–1341, May 2014. View at: Publisher Site  Google Scholar
 A. Kärkkäinen and A. Maija, Dissertation for the degree of Doctor of Philosophy to be presented with due permission of the Department of Electrical and Communications Engineering for public examination and debate in the Large Seminar Hall of Micronova at Helsinki University of Technology, VTT Technical Research Centre of Finland, Finland, 2006.
 L. Castaner, J. Pons, R. NadalGuardia, and A. Rodriguez, “Analysis of the extended operation range of electrostatic actuators by currentpulse drive,” Sensors and Actuators, A: Physical, vol. 90, no. 3, pp. 181–190, 2001. View at: Publisher Site  Google Scholar
 J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, Upper Saddle River, New Jersey, NJ, USA, 1991.
 W. L. Brogan, Modern Control Theory, PrenticeHall, Upper Saddle River, New Jersey, NJ, USA, 3rd edition, 1991.
 C. Acar and A. Shkel, MEMS Vibratory Gyroscopes; Structural Approaches to Improve Robustness, Springer, New York, NY, USA, 2008.
Copyright
Copyright © 2017 Ehsan Ranjbar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.