| 1. |
- Nie, Linlin, et al.
(författare)
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Improved Nonlinear Extended Observer Based Adaptive Fuzzy Output Feedback Control for a Class of Uncertain Nonlinear Systems With Unknown Input Hysteresis
- 2023
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Ingår i: IEEE Transactions on Fuzzy Systems. - 1941-0034 .- 1063-6706. ; 31:10, s. 3679-3689
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Tidskriftsartikel (refereegranskat)abstract
- This study focuses on the problem of adaptive fuzzy dynamic surface output feedback control for a class of uncertain nonlinear systems subjected to unknown input hysteresis. A Prandtl-Ishlinskii (PI) model is applied to the uncertain nonlinear system for describing the unknown input hysteresis, making the controller design feasible. In addition, a nonlinear extended state observer (NESO) is designed for simultaneously estimating the unmeasurable states and generalized disturbances, including the nonlinear hysteresis term of the PI model and external disturbances. In addition, a novel nonlinear function is designed to replace fal(·) function of the general NESO to address a modification that increases the convergence speed. Considering the incorporation of the improved nonlinear extended state observer (INESO), an adaptive output feedback control scheme is proposed based on fuzzy logic system and dynamic surface techniques. A command filter is employed to avoid the 'explosion of complexity' problem inherent in the backstepping technique, while compensating the filtering error caused by adopting the filter. The Lyapunov approach is used to demonstrate the stability of the entire closed-loop system. Experiments regarding a piezoelectric micropositioning stage are conducted, the results of which illustrate that the proposed adaptive fuzzy output feedback control method can guarantee a satisfactory tracking performance.
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| 2. |
- Wang, Yifan, et al.
(författare)
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Composite Data Driven-based Adaptive Control for a Piezoelectric Linear Motor
- 2022
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - 1557-9662 .- 0018-9456. ; 71
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Tidskriftsartikel (refereegranskat)abstract
- Piezoelectric linear motors play an important role in ultra-precision manufacturing technology. However, the complex nonlinear relationship between the input and output of the piezoelectric linear motors limits their further application. In this paper, to achieve precise motion control for a piezoelectric linear motor, a composite data driven-based adaptive control method is proposed, consisting of a correction controller, model free adaptive controller (MFAC), and low pass filter. The proposed control method addresses the demand for a precise model of the piezoelectric linear motor and solely relies on the linear model and input/output measurement data. First, an experimental test is implemented to analyze the complex nonlinearity between input and output signals of the controlled system, and a correction control is employed based on the dynamic linear sub-model of the piezoelectric linear motor to improve its dynamic and static characteristics. Then, to avoid the influence of unmodeled dynamics, such as inherent nonlinearity and external vibration, a MFAC is established as a feedback controller using data driven technology. In addition, a low pass filter is incorporated into the feedback loop to eliminate high frequency measurement noise in the system, thus improving the transient response of the MFAC method. Finally, the theoretical analysis of the error convergence is presented. The effectiveness of the proposed method is verified via comparisons with a correction control method, correction control-based digital sliding-mode control method, and correction control-based MFAC method. The experimental results indicate that the proposed control method is suitable for engineering applications. In particular, the root-mean-square error (RMSE) for the third-order S-curve tracking using the proposed is reduced by more than 15%, compared with the RMSEs for the cases with contrast control methods.
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| 3. |
- Wang, Yifan, et al.
(författare)
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Time delay recursive neural network-based direct adaptive control for a piezo-actuated stage
- 2023
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Ingår i: Science China Technological Sciences. - 1869-1900 .- 1674-7321. ; 66:5, s. 1397-1407
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Tidskriftsartikel (refereegranskat)abstract
- Piezo-actuated stage is a core component in micro-nano manufacturing field. However, the inherent nonlinearity, such as rate-dependent hysteresis, in the piezo-actuated stage severely impacts its tracking accuracy. This study proposes a direct adaptive control (DAC) method to realize high precision tracking. The proposed controller is designed by a time delay recursive neural network. Compared with those existing DAC methods designed under the general Lipschitz condition, the proposed control method can be easily generalized to the actual systems, which have hysteresis behavior. Then, a hopfield neural network (HNN) estimator is proposed to adjust the parameters of the proposed controller online. Meanwhile, a modular model consisting of linear submodel, hysteresis submodel, and lumped uncertainties is established based on the HNN estimator to describe the piezo-actuated stage in this study. Thus, the performance of the HNN estimator can be exhibited visually through the modeling results. The proposed control method eradicates the adverse effects on the control performance arising from the inaccuracy in establishing the offline model and improves the capability to suppress the influence of hysteresis on the tracking accuracy of piezo-actuated stage in comparison with the conventional DAC methods. The stability of the control system is studied. Finally, a series of comparison experiments with a dual neural networks-based data driven adaptive controller are carried out to demonstrate the superiority of the proposed controller.
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| 4. |
- Yu, Yewei, et al.
(författare)
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Neural network based iterative learning control for magnetic shape memory alloy actuator with iteration-dependent uncertainties
- 2023
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Ingår i: Mechanical Systems and Signal Processing. - : Elsevier BV. - 0888-3270 .- 1096-1216. ; 187
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Tidskriftsartikel (refereegranskat)abstract
- The magnetic shape memory alloy based actuator (MSMA-BA) is an indispensable component mechanism for high-precision positioning systems as it possesses the advantages of high precision, low energy consumption, and large stroke. However, hysteresis is an intrinsic property of MSMA material, which seriously affects the positioning accuracy of MSMA-BA. In this study, we propose a multi meta-model approach incorporating the nonlinear auto-regressive moving average with exogenous inputs (NARMAX) and Bouc–Wen (BW) models to describe the complex dynamic hysteresis of MSMA-BA. In particular, the BW model is introduced into the NARMAX model as an exogenous variable function, and a wavelet neural network (WNN) is adopted to construct the nonlinear function of the multi meta-model. In addition, iterative learning control is combined with a WNN to improve its convergence speed. A two-valued function is employed in the controller design process, so as to make use of history iteration information in updating control input. The main contribution of this study is the convergence analysis of the proposed iteration learning controller with iteration-dependent uncertainties (non-strict repetition of the initial state and varying iteration length). The experiments conducted on the MSMA-BA illustrate the validity of the proposed control scheme.
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