Indirect adaptive neural network dynamic surface control for uncertain time-delay electrostatic micro-actuator with state constraint

This study presents tracking control problem for a class of uncertain time-delay electrostatic micro-actuator systems which can be actively driven bidirectionally. By introducing indirect adaptive neural network (NN) algorithm into dynamic surface control (DSC) framework, some assumptions, which the uncertain time-delays must be satisfied in previous works, are removed. Also, by using the tangent barrier function, the proposed controller can ensure the system state tracks a reference trajectory and keeps in a desired region to prevent contact between the movable and fixed electrodes, while not violating the constraint during the whole dynamic response process. In addition, simulation results show that the effectiveness of the proposed results.

[1]  Jordan M. Berg,et al.  Control of an Electrostatic Microelectromechanical System Using Static and Dynamic Output Feedback , 2005 .

[2]  P.X. Liu,et al.  Robust adaptive tracking control of electrostatic micro-actuators with uncertainty , 2008, 2008 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[3]  Peter X. Liu,et al.  Robust adaptive tracking control of uncertain electrostatic micro-actuators with H-infinity performance , 2009 .

[4]  D. H. S. Maithripala,et al.  A general modelling and control framework for electrostatically actuated mechanical systems , 2005 .

[5]  Yan Lin,et al.  Global stabilization of high-order nonlinear time-delay systems by state feedback , 2014, Syst. Control. Lett..

[6]  Xin-Ping Guan,et al.  Output feedback control for interconnected time-delay systems with prescribed performance , 2014, Neurocomputing.

[7]  Guchuan Zhu,et al.  Robust Output Feedback Control of an Electrostatic Micro-Actuator , 2007, 2007 American Control Conference.

[8]  F.L. Lewis,et al.  Solving the "Pull-in" Instability Problem of Electrostatic Microactuators using Nonlinear Control Techniques , 2007, 2007 2nd IEEE International Conference on Nano/Micro Engineered and Molecular Systems.

[9]  Guchuan Zhu,et al.  Flatness-Based Control of Electrostatically Actuated MEMS With Application to Adaptive Optics: A Simulation Study , 2006, Journal of Microelectromechanical Systems.

[10]  L. Weihua,et al.  A dynamic macromodel for distributed parameter magnetic microactuators , 2008 .

[11]  Peter Xiaoping Liu,et al.  Backstepping Control for Nonlinear Systems With Time Delays and Applications to Chemical Reactor Systems , 2009, IEEE Transactions on Industrial Electronics.

[12]  Xinping Guan,et al.  Brief paper: adaptive backstepping fault-tolerant control for unmatched non-linear systems against actuator dead-zone , 2010 .

[13]  Guchuan Zhu,et al.  Improving the Performance of an Electrostatically Actuated MEMS by Nonlinear Control: Some Advances and Comparisons , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[14]  Luo Xiaoyuan,et al.  Dynamic surface control for constrained electrostatic micro-actuators with uncertain time delays , 2012, Proceedings of the 31st Chinese Control Conference.

[15]  X. Guan,et al.  Dynamic surface control for a class of state-constrained non-linear systems with uncertain time delays , 2012 .

[16]  Xinping Guan,et al.  Neural network-based adaptive tracking control for nonlinearly parameterized systems with unknown input nonlinearities , 2012, Neurocomputing.

[17]  Jean Lévine,et al.  Stabilization of an electrostatic MEMS including uncontrollable linearization , 2007, 2007 46th IEEE Conference on Decision and Control.

[18]  Keng Peng Tee,et al.  Adaptive Control of Electrostatic Microactuators With Bidirectional Drive , 2009, IEEE Transactions on Control Systems Technology.