Modeling and Adaptive Output Feedback Control of Butterfly-Like Hysteretic Nonlinear Systems With Creep and Their Applications

Zhang, Xiuyu; Xu, Hongzhi; Chen, Xinkai; Li, Zhi; Su, Chun‐Yi

doi:10.1109/tie.2022.3187583

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…Thus, the shape estimation p(θ t+1 ) can be derived from (15). Note that the off-diagonal elements in Σ t+1 are nonzero during update due to (15b).…”

Section: B Unscented Kalman Filters With Gaussian Process Modelsmentioning

confidence: 99%

“…Compensation strategies are desired to resolve this issue. Operator-based techniques identify the hysteresis behavior using the hysteresis operator [13]- [15], which models hysteresis as a cumulative memory effect of delayed relay components. These models share a similar operating principle, in which the parameters of highly nonlinear basis functions are fitted through a complex identification process.…”

Section: Introductionmentioning

confidence: 99%

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Edge-Enabled Adaptive Shape Estimation of 3-D Printed Soft Actuators With Gaussian Processes and Unscented Kalman Filters

Tan

Feng

et al. 2024

IEEE Trans. Ind. Electron.

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Soft actuators have the advantages of compliance and adaptability when working with vulnerable objects, but the deformation shape of the soft actuators is difficult to measure or estimate. Soft sensors made of highly flexible and responsive materials are promising new approaches to the shape estimation of soft actuators, but suffer from highly nonlinear, hysteresis, and time-variant properties. A nonlinear and adaptive state observer is essential for the shape estimation from soft sensors. Current state estimation methods rely on complex nonlinear datafitting models, and the robustness of the estimation methods is questionable. This study investigates the soft actuator dynamics and the soft sensor model as a stochastic process characterized by the Gaussian Process (GP) model. The unscented Kalman filter (UKF) is applied to the GP model for more reliable variance adjustment during the sequential state estimation process than conventional methods. In addition, a major limitation of the GP model is its computational complexity during online inference. To improve the real-time performance while guaranteeing accuracy, we introduce an edge server to decrease the onboard computational and memory overhead. The experiments showcase a significant improvement in estimation accuracy and real-time performance compared to baseline methods.

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“…Thus, the shape estimation p(θ t+1 ) can be derived from (15). Note that the off-diagonal elements in Σ t+1 are nonzero during update due to (15b).…”

Section: B Unscented Kalman Filters With Gaussian Process Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Edge-Enabled Adaptive Shape Estimation of 3-D Printed Soft Actuators With Gaussian Processes and Unscented Kalman Filters

Tan

Feng

et al. 2024

IEEE Trans. Ind. Electron.

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show abstract

“…In addition, in adaptive control schemes, neural networks [5,17,18,36] or fuzzy systems [19,29] are often used to compensate for uncertainty and unknown dynamics in the system online. In [37], a novel reference tracking control method has been proposed for servo system, and the experimental results show that the Grey Wolf Optimizer method can solve the main shortcomings of the Gradient Descent scheme in Reinforcement Learning -based control problems.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Adaptive Quantized Dynamic Surface Control for a Class of Flexible Hypersonic Flight Vehicles with Input Quantization

Zhao¹,

Lu²,

et al. 2023

Machines

Self Cite

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A control strategy for a certain class of hypersonic flight aircraft dynamic models with unknown parameters is proposed in this article. The strategy is adaptive dynamic surface input quantization control. To address the issues in conventional inversion control, a first-order low-pass filter and an adaptive parameter minimum learning law are introduced in the control system design process. This method has the following features: (1) it solves the problem of repeated differentiation of the virtual control law in the conventional back-stepping method, greatly simplifying the control law structure; (2) by using the norm of the neural network weight vector as the adaptive adjustment parameter instead of updating each element online, the number of adaptive adjustment parameters is significantly reduced, improving the execution efficiency of the controller; (3) the introduced hysteresis quantizer overcomes the disadvantage of the quantization accuracy deterioration when the input value is too low in the logarithm quantizer, improving the accuracy of the quantizer. Stability analysis has shown that all signals in the closed-loop system are semi-globally uniformly bounded, and simulation results have verified the effectiveness of the proposed adaptive quantized control scheme.

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“…It is common to model the system of a piezoactuator as a system model cascaded by a hysteresis dynamics system and an internal dynamics (including creep dynamics and vibration dynamics) system, and to construct the inverse of the model by identifying the system plant [7][8][9]. Further, more adaptive control schemes [10][11][12][13][14][15][16][17][18][19][20][21] are proposed to compensate for the hysteresis nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tracking Control Schemes for Fuzzy Approximation-Based Noncanonical Nonlinear Systems with Hysteresis Inputs

et al. 2023

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In this paper, the tracking control problem of a class of fuzzy approximation-based noncanonical nonlinear systems with hysteresis inputs is investigated, where the fuzzy weight matrix is not available for measurement, and the hysteresis nonlinearities are modeled by the Prandtl–Ishlinskii operator. Due to the coupling effects, the plant input containing hysteresis is unknown. To solve the problem, two adaptive control schemes are developed. The first is a Lyapunov-based scheme, and the second is a gradient-based scheme. For convenience, only the relative-degree-one case is taken into account in design and analysis. With the proposed schemes, it can be proved that all signals in the closed-loop system are bounded, and the tracking error converges to a small region around zero. Simulation results show that the maximum steady-state error converges to [−0.0131,0.0183]μm and [−0.0139,0.0161]μm with two control schemes, which confirms the obtained results.

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Modeling and Adaptive Output Feedback Control of Butterfly-Like Hysteretic Nonlinear Systems With Creep and Their Applications

Cited by 9 publications

References 36 publications

Edge-Enabled Adaptive Shape Estimation of 3-D Printed Soft Actuators With Gaussian Processes and Unscented Kalman Filters

Edge-Enabled Adaptive Shape Estimation of 3-D Printed Soft Actuators With Gaussian Processes and Unscented Kalman Filters

Decentralized Adaptive Quantized Dynamic Surface Control for a Class of Flexible Hypersonic Flight Vehicles with Input Quantization

Adaptive Tracking Control Schemes for Fuzzy Approximation-Based Noncanonical Nonlinear Systems with Hysteresis Inputs

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