Input-and-Measurement Event-Triggered Output-Feedback Chattering Reduction Control for MEMS Gyroscopes

Shao, Xingling; Shi, Yi; Zhang, Wendong

doi:10.1109/tsmc.2021.3129275

Cited by 46 publications

(32 citation statements)

References 37 publications

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“…In this section, we will evaluate the proposed neural network sliding mode approach on the lumped MEMS gyroscope sensor model 1,4,5 In order to make the numerical simulation more easily realized and simplify the design of the controller, the dimensionless procedure for the micro gyroscope is implemented, Since the general displacement range of the micro gyroscope sensor in each axis is submicrometer level, it is reasonable to choose 1 mm as the reference length q 0 . Given that the usual natural frequency of a vibratory MEMS gyroscope sensor is in the KHz range, v 0 is chosen as 1kHz.…”

Section: Simulation Studymentioning

confidence: 99%

“…In this section, we will evaluate the proposed neural network sliding mode approach on the lumped MEMS gyroscope sensor model 1,4,5 by using MATLAB/SIMULINK. Parameters of the MEMS gyroscope sensor are as follows:…”

Section: Simulation Studymentioning

confidence: 99%

“…Recently, researchers developed advanced control approaches to control the MEMS gyroscopes. [1][2][3][4][5] Sliding mode controller (SMC) is a nonlinear control strategy with strong robustness. [6][7][8] Terminal sliding mode controllers and super-twisting sliding mode controller have developed for PMSM drives and microgyrosocpe.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and neural sliding mode control of mems triaxial gyroscope

Fang

Ding

et al. 2022

Advances in Mechanical Engineering

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In this paper, a neural sliding mode control approach is developed to adjust the sliding gain using a radial basis function (RBF) neural network (NN) for the tracking control of Microelectromechanical Systems (MEMS) triaxial vibratory gyroscope. First a sliding mode control with a fixed sliding gain is proposed to assure the asymptotic stability of the closed loop system. Then a RBF neural network is derived to adjust the sliding gain using a gradient method in a switching control law. With the adaptive sliding gain using the learning function of RBF neural network, the chattering phenomenon is eliminated. Numerical simulation is investigated to verify the effectiveness of the proposed neural sliding mode control scheme.

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Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

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Modeling and neural sliding mode control of mems triaxial gyroscope

Fang

Ding

et al. 2022

Advances in Mechanical Engineering

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“…Assumption 1 [30,31]: The differential of unknown perturbations is deemed to be bounded, and there exists an unknown constant holding δ ≥ .…”

Section: Unknown System Dynamics Estimator Designmentioning

confidence: 99%

Unknown System Dynamics Estimator-Based Anti-Disturbance Attitude Funnel Control for Quadrotors with Experimental Verifications

Zhen

Zhang

2022

Electronics

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In the article, an unknown system dynamics estimator-based anti-disturbance attitude funnel control technique is considered for quadrotors to ensure tracking performance when experiencing parametric uncertainties and external perturbations. To reinforce the anti-disturbance ability, an unknown system dynamics estimator was established by constructing the filtering operation-based invariant manifold, resulting in a more concise design framework, lower computational consumption and an asymptotic error convergence. Additionally, a funnel control policy was employed to regulate angle-tracking errors within a minor overshoot, a faster convergence time and a lower steady-state error by devising the funnel variables, where an exponential decaying function was used to construct the funnel boundary. The great improvements beyond the available quadrotor control policies are related to satisfied disturbance mitigation and performance guarantees as a priority despite uncertainties. The error arguments comprising of angle and angular rate for quadrotors were ultimately uniformly bounded and the angles controlling the deviations were restricted to the funnel boundary. Finally, the simulations and experiments verified the superiority of the proposed control technique in terms of reduced control actions and higher precision, as well as shorter settling time.

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“…In the work of [35], an unknown system dynamics estimator is developed to neutralize the adverse effect caused by exogenous disturbances and system uncertainties. Moreover, the fault case is [36] and the event-triggered scheme is proposed in [37], which has developed fruitful results for the analysis of uncertainties. According to the literatures above, it can be inferred that the existing results about robust consensus cannot realize online-learning scheduling and the transient convergence performance cannot be guaranteed.…”

mentioning

confidence: 99%

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

Cheng

Song

Xu³

et al. 2022

International Journal of Aerospace Engineering

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This paper is devoted to H ∞ consensus design and online scheduling for homogeneous multiagent systems (MASs) with switching topologies via deep reinforcement learning. The model of homogeneous MASs with switching topologies is established based on switched systems theory, in which the switching of topologies is viewed as the switching among subsystems. By employing linear transformation, the closed-loop systems of MASs are converted into reduced-order systems. The problem of H ∞ consensus design can be transformed to the issue of H ∞ control. It is supposed that the consensus protocol is composed of two parts: dynamics-based protocol and learning-based protocol, where dynamics-based protocol is provided to guarantee the convergence and weighted attenuation and learning-based protocol is proposed to improve the transient performance. Then, the multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to ensure the stability and weighted H ∞ disturbance attenuation index of reduced-order systems. The sufficient existing conditions of dynamics-based protocol are given through the feasible solutions of linear matrix inequalities (LMIs). Moreover, the online scheduling is formulated as a Markov decision process, and the deep deterministic policy gradient (DDPG) algorithm in the framework of actor-critic is proposed for the compensation of disturbance to explore optimal control policy. The online scheduling of parameters of MASs is viewed as bounded compensation of dynamics-based protocol, whose stability can be guaranteed by nonfragile control theory. Finally, simulation results are provided to illustrate the effectiveness and superiority of the proposed method.

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Input-and-Measurement Event-Triggered Output-Feedback Chattering Reduction Control for MEMS Gyroscopes

Cited by 46 publications

References 37 publications

Modeling and neural sliding mode control of mems triaxial gyroscope

Modeling and neural sliding mode control of mems triaxial gyroscope

Unknown System Dynamics Estimator-Based Anti-Disturbance Attitude Funnel Control for Quadrotors with Experimental Verifications

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

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Input-and-Measurement Event-Triggered Output-Feedback Chattering Reduction Control for MEMS Gyroscopes

Cited by 46 publications

References 37 publications

Modeling and neural sliding mode control of mems triaxial gyroscope

Modeling and neural sliding mode control of mems triaxial gyroscope

Unknown System Dynamics Estimator-Based Anti-Disturbance Attitude Funnel Control for Quadrotors with Experimental Verifications

H ∞ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning

Contact Info

Product

Resources

About

$H_{\infty}$ Consensus Design and Online Scheduling for Multiagent Systems with Switching Topologies via Deep Reinforcement Learning