Gain-Adaptive Robust Backstepping Position Control of a BLDC Motor System

Ba, Dang Xuan; Yeom, Hoyeon; Kim, Jihoon; Bae, Joonbum

doi:10.1109/tmech.2018.2864187

Cited by 33 publications

(2 citation statements)

References 38 publications

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“…The backstepping control method can decompose the complex nonlinear system into several sub systems, and the corresponding Lyapunov function of the whole system can be derived step by step [31,32]. According to the characteristics of the actual EMA mathematical model, this paper proposes an adaptive backstepping control method that uses an RBF neural network to approximate and adaptively cancel the unknown parts of the system.…”

Section: Control Strategymentioning

confidence: 99%

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Zhang

Hui

2019

Algorithms

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This paper proposes an adaptive backstepping control algorithm for electric braking systems with electromechanical actuators (EMAs). First, the ideal mathematical model of the EMA is established, and the nonlinear factors are analyzed, such as the deformation of the reduction gear. Subsequently, the actual mathematical model of the EMA is rebuilt by combining the ideal model and the nonlinear factors. To realize high performance braking pressure control, the backstepping control method is adopted to address the mismatched uncertainties in the electric braking system, and a radial basis function (RBF) neural network is established to estimate the nonlinear functions in the control system. The experimental results indicate that the proposed braking pressure control strategy can improve the servo performance of the electric braking system. In addition, the hardware-in-loop (HIL) experimental results show that the proposed EMA controller can satisfy the requirements of the aircraft antilock braking systems.

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Section: Control Strategymentioning

confidence: 99%

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Zhang

Hui

2019

Algorithms

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“…In addition to auto gain‐tuning methods, some gain‐adaptive methods can achieve similar purposes. A gain‐learning algorithm was developed in Reference 6, which adopts two separate parts of gains, variable gains and fixed gains. The variable gains are updated with the gain‐learning algorithm while the fixed gains are still selected by trial and errors.…”

Section: Introductionmentioning

confidence: 99%

A gain‐tuning method for almost disturbance decoupling problems of nonlinear systems with zero dynamics

Liu

Wang

2022

Intl J Robust & Nonlinear

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In this paper, a gain-tuning method for almost disturbance decoupling problems of nonlinear systems with zero dynamics is developed. Firstly, a linear subsystem is formed by linearizing the nonlinear system. Then, a linear matrix inequality can be formed for the linear subsystem. After that, a linear state-feedback controller can be obtained by solving the linear matrix inequality. A nonlinear state-feedback controller can be obtained for the original nonlinear system by using backstepping design method. Another linear state-feedback controller can be derived by linearizing the nonlinear state-feedback controller. Finally, the backstepping gains can be solved by equating the two linear controllers. The detailed derivations of the method are provided. Some comparisons with the existing techniques are discussed. Moreover, the designed method is verified by simulations and some comparisons are made accordingly.

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An intelligent sliding mode controller of robotic manipulators with output constraints and high‐level adaptation

2022

Intl J Robust & Nonlinear

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Fast transient responses and excellent steady-state control performances with flexibility in control operation are core motivations to promote a vast of research in the robotic control sector nowadays. As a sequence, in this paper, we present a learning nonlinear controller for motion control of robotic manipulators with output constraints. The constrained control objectives are first transformed to new free variables using nonlinear synthetization profiles. Boundedness of the main control objectives within feasible ranges of the physical outputs is next strictly guaranteed under a simple sliding-mode control policy. To improve the control performance, dominant terms of the system dynamics are learnt by a neural network with a nonlinear adaptation law. The systematic deviation remaining is then estimated by an adaptive disturbance observer. A novel high-level adaptation rule is designed to maximize the learning ability and the control quality. Both an asymptotic control performance and estimation effectiveness are theoretically thoroughly proven by Lyapunov-based analyses. Feasibility of the overall system is carefully investigated by intensive comparative simulations.

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Gain-Adaptive Robust Backstepping Position Control of a BLDC Motor System

Cited by 33 publications

References 38 publications

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

Backstepping Adaptive Neural Network Control for Electric Braking Systems of Aircrafts

A gain‐tuning method for almost disturbance decoupling problems of nonlinear systems with zero dynamics

An intelligent sliding mode controller of robotic manipulators with output constraints and high‐level adaptation

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