Neural Adaptive Control for MEMS Gyroscope with Full-State Constraints and Quantized Input

Shao, Xingling; Shi, Yi

doi:10.1109/tii.2020.2968345

Cited by 76 publications

(49 citation statements)

References 44 publications

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“…Following the backstepping design framework [36], [37], [45], [46], the design procedure of presented controller contains the following two steps:…”

Section: Control Designmentioning

confidence: 99%

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

2020

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This paper investigates an event-triggered output feedback control problem with guaranteed tracking performance for microelectromechanical system (MEMS) gyroscope subject to the lumped disturbances, including parameter uncertainties, coupling between driving mode and sensing mode, and unknown external disturbances. Firstly, to make the system output tracking errors evolve within the predetermined performance envelopes, a prescribed performance control (PPC) is employed in our controller design, such that a prescribed trajectory tracking with preselected settling time and steady-state tracking accuracy can be ensured. Besides, to simultaneously realize disturbance rejection and estimation of the immeasurable velocity state, linear extended state observers (LESOs) are designed to reconstruct the lumped disturbances and immeasurable velocity states for both MEMS gyroscope driving and sensing modes. In general, excessively energy and resource consumption are fundamentally neglected in most of the existing output feedback controllers for MEMS gyroscope, which will dramatically reduce the operational time of the close-loop system. To address this problem, we embed a fixed threshold event-triggered mechanism into the controller design procedure, such that the communication data size can be drastically reduced without sacrificing the tracking accuracy. Finally, to obtain the close-loop stability for MEMS gyroscope, robust control laws are designed to compensate for the measurement error and estimation error, meanwhile Zeno phenomena can be effectively eliminated. The simulation results and comparisons validate the effectiveness of the proposed scheme. INDEX TERMS Output feedback, event-triggered control, prescribed performance, MEMS gyroscope.

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“…Following the backstepping design framework [36], [37], [45], [46], the design procedure of presented controller contains the following two steps:…”

Section: Control Designmentioning

confidence: 99%

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

2020

Self Cite

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“…Although most existing works [22]- [24] can realize high-accuracy identification of unknown disturbances by means of universal approximation of NNs, it is worth pointing out that almost all of them suffer from the problem of learning explosion. Inspired by the minimum-learning-parameter (MLP) technique in [26], [27] and [36], we employ MLP to reconstruct the lumped disturbances consisting of external disturbances, parametric uncertainties and coupling between two operation modes, which greatly reduces learning dimension, such that the computational load is remarkably eased without decreasing the identification accuracy. What's more, the application of DSC technique effectively eliminates the problem of term explosion caused by analytical differentiation, such that the feasibility of algorithm is notably improved.…”

Section: Introductionmentioning

confidence: 99%

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

Shao

Zhang

2020

IEEE Access

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In this paper, a minimum-learning-parameter (MLP) based neural control method is proposed for micro-electro-mechanical system (MEMS) gyroscope with prescribed performance and input quantization. For the first time, a logarithmic quantizer (LQ) is employed to generate smooth input control signal for MEMS gyroscope, which greatly reduces the communication data size as well as actuator bandwidth. To improve the performance of MEMS gyroscope in the presence of quantization error, a prescribed performance control scheme consisting of preselected performance boundaries and an error transformation is utilized, such that preselected transient and steady-state properties can be assured. In contrast to the neural control strategies subject to the issue of learning explosion, a MLP-based neural network (NN) is introduced to estimate the unknown uncertainties using the norm of neural weight. To eliminate the effect of quantization error induced by LQ, a robust quantized control is designed to further ensure the closed-loop system suffering from discontinuous dynamics with prescribed ultimately uniformly bounded (UUB) performance. In the end, a series of simulations are presented to validate the superiority of the proposed control methodology.

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“…In Ref. [13][14][15][16], a new sampling rule-based cubature Kalman filter (CKF) is proposed. The CKF algorithm adopts the third-order radial spherical cubature principle to optimize the sampling point selection method, so as to solve the problem that the filtering effect of UKF algorithm is not ideal in high-dimensional system.…”

Section: Introductionmentioning

confidence: 99%

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

Jin

Guo

2020

IEEE Access

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In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.

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Neural Adaptive Control for MEMS Gyroscope with Full-State Constraints and Quantized Input

Cited by 76 publications

References 44 publications

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

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