Fuzzy wavelet neural control with improved prescribed performance for MEMS gyroscope subject to input quantization

Abstract In this paper, a fuzzy wavelet neural control scheme with improved prescribed performance is investigated for micro-electro-mechanical system (MEMS) gyroscope in the presence of uncertainties and input quantization. A hysteresis quantizer (HQ) is introduced in the controller design to generate input signal in a finite set, which can greatly reduce the actuator bandwidth without decreasing the control accuracy, and avoid the undesirable chattering occurring universally in other quantizers. To guarantee the output tracking with better prescribed transient behavior, a modified prescribed performance control (MPPC) consisting of asymmetric performance boundaries and an error transformation function is explored, such that arbitrarily small overshoot can be assured without retuning design parameters. Unlike the traditional neural network that suffers from explosion of learning, a fuzzy wavelet neural network (FWNN) based on minimal-learning-parameter (MLP) is designed to identify uncertainties with slight computational burden. A robust quantized control scheme is synthesized to compensate for quantization error and achieve prescribed ultimately uniformly bounded (UUB) tracking. Finally, extensive simulations are presented to verify the effectiveness of proposed control scheme.

[1]  Yaonan Wang,et al.  Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network , 2014, Fuzzy Sets Syst..

[2]  Swaroop Darbha,et al.  Dynamic surface control for a class of nonlinear systems , 2000, IEEE Trans. Autom. Control..

[3]  Abdesselem Boulkroune,et al.  Flatness-based adaptive fuzzy control of electrostatically actuated MEMS using output feedback , 2016, Fuzzy Sets Syst..

[4]  Tasawar Hayat,et al.  Application of reproducing kernel algorithm for solving second-order, two-point fuzzy boundary value problems , 2017, Soft Comput..

[5]  Bin Xu,et al.  Minimal-Learning-Parameter Technique Based Adaptive Neural Sliding Mode Control of MEMS Gyroscope , 2017, Complex..

[6]  Mehran Hosseini-Pishrobat,et al.  Robust output regulation of a triaxial MEMS gyroscope via nonlinear active disturbance rejection , 2018 .

[7]  Di Cao,et al.  Adaptive Dynamic Surface Control of MEMS Gyroscope Sensor Using Fuzzy Compensator , 2016, IEEE Access.

[8]  Ke Wang,et al.  A prescribed performance control approach guaranteeing small overshoot for air-breathing hypersonic vehicles via neural approximation , 2017 .

[9]  Charalampos P. Bechlioulis,et al.  A low-complexity global approximation-free control scheme with prescribed performance for unknown pure feedback systems , 2014, Autom..

[10]  Mohammad Mehdi Arefi,et al.  Prescribed performance adaptive neural output control for a class of switched nonstrict‐feedback nonlinear time‐delay systems: State‐dependent switching law approach , 2019, International Journal of Robust and Nonlinear Control.

[11]  Rickey Dubay,et al.  Nonlinear inversion-based control with adaptive neural network compensation for uncertain MIMO systems , 2012, Expert Syst. Appl..

[12]  Zhitao Liu,et al.  Robust adaptive output feedback control for uncertain nonlinear systems with quantized input , 2017 .

[13]  Guang-Hong Yang,et al.  Adaptive Backstepping Stabilization of Nonlinear Uncertain Systems With Quantized Input Signal , 2014, IEEE Transactions on Automatic Control.

[14]  Tieshan Li,et al.  Modular Adaptive Control for LOS-Based Cooperative Path Maneuvering of Multiple Underactuated Autonomous Surface Vehicles , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[15]  Yang Wei,et al.  Estimator-based MLP neuroadaptive dynamic surface containment control with prescribed performance for multiple quadrotors , 2020 .

[16]  Meng Zhang,et al.  Network-based fuzzy control for nonlinear Markov jump systems subject to quantization and dropout compensation , 2019, Fuzzy Sets Syst..

[17]  Huaguang Zhang,et al.  A fuzzy adaptive tracking control for a class of uncertain strick-feedback nonlinear systems with dead-zone input , 2018, Neurocomputing.

[18]  Hongye Su,et al.  Quantized Feedback Control of Fuzzy Markov Jump Systems , 2019, IEEE Transactions on Cybernetics.

[19]  Zhongke Shi,et al.  Composite Neural Learning-Based Nonsingular Terminal Sliding Mode Control of MEMS Gyroscopes , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[20]  Dan Wu,et al.  Adaptive control of MEMS gyroscope using fully tuned RBF neural network , 2015, Neural Computing and Applications.

[21]  Lili Dong,et al.  Drive-Mode Control for Vibrational MEMS Gyroscopes , 2009, IEEE Transactions on Industrial Electronics.

[22]  Shixi Hou,et al.  Adaptive Neural Backstepping PID Global Sliding Mode Fuzzy Control of MEMS Gyroscope , 2019, IEEE Access.

[23]  Mohammad Pourmahmood Aghababa,et al.  Adaptive T-S fuzzy control design for fractional-order systems with parametric uncertainty and input constraint , 2019, Fuzzy Sets Syst..

[24]  Cheng Lu,et al.  Backstepping control of MEMS gyroscope using adaptive neural observer , 2017, Int. J. Mach. Learn. Cybern..

[25]  Claudio De Persis,et al.  Stability of quantized time-delay nonlinear systems: a Lyapunov–Krasowskii-functional approach , 2008, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[26]  Chong Shen,et al.  Robust dynamic surface trajectory tracking control for a quadrotor UAV via extended state observer , 2018 .

[27]  Yongduan Song,et al.  Prescribed Performance Control of Uncertain Euler–Lagrange Systems Subject to Full-State Constraints , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Qiuye Sun,et al.  Adaptive critic design-based robust neural network control for nonlinear distributed parameter systems with unknown dynamics , 2015, Neurocomputing.

[29]  Mou Chen,et al.  Adaptive neural prescribed performance tracking control for near space vehicles with input nonlinearity , 2016, Neurocomputing.

[30]  Peng Shi,et al.  Finite-Time Distributed State Estimation Over Sensor Networks With Round-Robin Protocol and Fading Channels , 2018, IEEE Transactions on Cybernetics.

[31]  Guang-Hong Yang,et al.  Observer-based adaptive fuzzy quantized control of uncertain nonlinear systems with unknown control directions , 2019, Fuzzy Sets Syst..

[32]  Qing-Long Han,et al.  Input-to-State Stabilization in Probability for Nonlinear Stochastic Systems Under Quantization Effects and Communication Protocols , 2019, IEEE Transactions on Cybernetics.

[33]  Nicola Elia,et al.  Stabilization of linear systems with limited information , 2001, IEEE Trans. Autom. Control..

[34]  Charalampos P. Bechlioulis,et al.  Trajectory Tracking With Prescribed Performance for Underactuated Underwater Vehicles Under Model Uncertainties and External Disturbances , 2017, IEEE Transactions on Control Systems Technology.

[35]  Okyay Kaynak,et al.  Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study , 2008, IEEE Transactions on Industrial Electronics.

[36]  Tianping Zhang,et al.  Adaptive output feedback tracking control of stochastic nonlinear systems with dynamic uncertainties , 2015 .

[37]  Yi Shi,et al.  Neural Adaptive Control for MEMS Gyroscope With Full-State Constraints and Quantized Input , 2020, IEEE Transactions on Industrial Informatics.

[38]  Simon X. Yang,et al.  An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics , 2013, Expert Syst. Appl..

[39]  Yan-Jun Liu,et al.  Neural Network Controller Design for a Class of Nonlinear Delayed Systems With Time-Varying Full-State Constraints , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Baoquan Li,et al.  Adaptive fuzzy decentralized control for a class of nonlinear systems with different performance constraints , 2019, Fuzzy Sets Syst..

[41]  Tshilidzi Marwala,et al.  A new T-S fuzzy model predictive control for nonlinear processes , 2017, Expert Syst. Appl..

[42]  Fang Wang,et al.  Adaptive Prescribed Performance Fault Tolerant Control for a Flexible Air-Breathing Hypersonic Vehicle With Uncertainty , 2019, IEEE Access.

[43]  Xiangwei Bu,et al.  A new prescribed performance control approach for uncertain nonlinear dynamic systems via back-stepping , 2018, J. Frankl. Inst..

[44]  Xingling Shao,et al.  Neuroadaptive integral robust control of visual quadrotor for tracking a moving object , 2020 .

[45]  Omar Abu Arqub,et al.  Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations , 2017, Neural Computing and Applications.

[46]  Yongming Li,et al.  Adaptive output-feedback control design with prescribed performance for switched nonlinear systems , 2017, Autom..

[47]  Celal Batur,et al.  A novel adaptive sliding mode control with application to MEMS gyroscope. , 2009, ISA transactions.

[48]  Charalampos P. Bechlioulis,et al.  Robust Adaptive Control of Feedback Linearizable MIMO Nonlinear Systems With Prescribed Performance , 2008, IEEE Transactions on Automatic Control.

[49]  Shaocheng Tong,et al.  Fuzzy Adaptive Output Feedback Control of MIMO Nonlinear Systems With Partial Tracking Errors Constrained , 2015, IEEE Transactions on Fuzzy Systems.

[50]  Xingling Shao,et al.  High-order ESO based output feedback dynamic surface control for quadrotors under position constraints and uncertainties , 2019, Aerospace Science and Technology.