Neuro‐adaptive practical prescribed‐time control for pure‐feedback nonlinear systems without accurate initial errors

Tan, Yan; Wu, Liucang

doi:10.1002/acs.3554

Cited by 7 publications

(3 citation statements)

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“…[13][14][15] It is well established that adaptive control, endowed with online estimation/learning capabilities through identification/compensation mechanisms, is highly effective in dealing with unknown parameters and improving system performance. [16][17][18][19] Existing research has shown that adaptive event-triggered control can improve the transient performance of closed-loop systems while reducing data transmission by setting suitable triggering conditions. [20][21][22][23][24][25][26] Based on the different triggering objects, these existing results are generally divided into two categories: signal transmission triggering 20,21 and parameter estimation triggering.…”

Section: Introductionmentioning

confidence: 99%

“…It is well established that adaptive control, endowed with online estimation/learning capabilities through identification/compensation mechanisms, is highly effective in dealing with unknown parameters and improving system performance 16–19 . Existing research has shown that adaptive event‐triggered control can improve the transient performance of closed‐loop systems while reducing data transmission by setting suitable triggering conditions 20–26 .…”

Section: Introductionmentioning

confidence: 99%

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Adaptive event‐triggered control for strict‐feedback systems With time‐varying parameters

Tan,

Wu,

Liu

2023

Intl J Robust & Nonlinear

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In this article, we develop a new adaptive event‐triggered asymptotic control scheme for strict‐feedback systems with fast time‐varying parameters. To deal with time‐varying parameters with unknown variation boundaries in the feedback path and the input path, we construct three adaptive laws for parameter estimation, two for the uncertain parameters in the feedback path and one for the uncertain parameters in the input path. In particular, two sets of tuning functions are introduced to avoid over‐parametrization. Additionally, an event‐triggering mechanism is embedded in this adaptive control framework to reduce the data transmission from the controller to the actuator. We also introduce a soft sign function to handle the perturbations caused by sampling errors to achieve asymptotic stability and avoid the so‐called parameter drift. The stability analysis shows that the closed‐loop system is globally uniformly asymptotically stable and the Zeno behavior can be excluded. Simulation results verify the effectiveness and performance of the proposed adaptive scheme.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive event‐triggered control for strict‐feedback systems With time‐varying parameters

Tan,

Wu,

Liu

2023

Intl J Robust & Nonlinear

Self Cite

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show abstract

“…The convergence rate and the final tracking error range, two crucial metrics for assessing the controller, must frequently be taken into account when developing a controller. The tracking error needs to converge to a specified precision within a given period for several application scenarios that have higher criteria for safety and reliability, such as target interception, spacecraft docking, and auto parts assembly [1,8]. The latest results of prescribed time control [9][10][11][12][13] solve the problem of system stability in a limited time.…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Tracking Control of Wind Turbines Based on a New Prescribed Performance Function

Qian

Tian

et al. 2023

Energies

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The primary control goals of a wind turbine (WT) are structural load shedding, maximum wind energy capture in the underpowered situation, and consistent power production in the full power condition. A crucial component of the control problem for wind turbines with varying speeds is maximum power tracking control. Conventional maximum power tracking control tracks the ideal blade tip speed ratio to provide the most wind power at the specified wind speeds. However, because of the wind turbine’s great nonlinearity and the significant external disturbances it encounters, it is difficult to react quickly to variations in wind speed, and the tracking speed is sluggish, which lowers the amount of electricity produced annually. In light of this, this work develops a novel preset performance controller for a wind power system maximum power tracking control. With this technique, the convergence rate and tracking precision may be set. In particular, based on the concept of time-varying feedback, a time-varying function, known as the preset performance function, is first created to allow the convergence speed and accuracy to be predetermined; then this time-varying function is used to transform the actual specified time problem of the original system into a bounded time problem of the new system; finally, a direct robust controller design strategy with pre-defined performance is suggested based on the design concept of the backstepping technique. The plan may maximize the rotor power coefficient by altering the wind turbine speed, track the ideal blade tip speed ratio for a given tracking accuracy and speed, and get the most wind power to produce the most power with the strongest robustness. The simulation results show that the recommended control technique works.

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Decentralized adaptive practical prescribed‐time control via command filters

Zhang,

Zhang

2024

Adaptive Control & Signal

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SummaryThis paper proposes a command filter‐based decentralized adaptive backstepping practical prescribed‐time (PPT) tracking control scheme for a class of non‐strict feedback interconnected systems with time varying parameters, unknown control coefficients, unmodeled dynamics, input deadzone and saturation. By the aid of the characteristics of Gaussian functions, the obstacles arising from the non‐strict feedback terms are successfully solved. By constructing a novel time‐varying scaling function and utilizing nonlinear mapping, the PPT tracking control is developed. The estimations of dynamical uncertainties resulting from unmodeled dynamics are accomplished by employing auxiliary signals, while the unknown continuous terms are characterized by the aid of radial basis function neural networks (RBFNNs). A superposition of two hyperbolic tangent functions is utilized to approximate input nonlinearity. Utilizing the compact set defined in the command filtered backstepping technique, the problem of unknown control direction is solved without using the Nussbaum gain technique. All the signals involved are proved to be semi‐global uniform ultimate bounded, and the tracking error can enter the pre‐specified convergence region within a pre‐specified time. Simulation results are used to demonstrate the effectiveness of the proposed control approach.

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Neuro‐adaptive practical prescribed‐time control for pure‐feedback nonlinear systems without accurate initial errors

Cited by 7 publications

References 50 publications

Adaptive event‐triggered control for strict‐feedback systems With time‐varying parameters

Adaptive event‐triggered control for strict‐feedback systems With time‐varying parameters

Maximum Power Tracking Control of Wind Turbines Based on a New Prescribed Performance Function

Decentralized adaptive practical prescribed‐time control via command filters

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