Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Wang, Shubo; Yu, Haisheng; Gao, Xuehui; Wang, Na

doi:10.1155/2018/8925838

Cited by 4 publications

(3 citation statements)

References 61 publications

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“…During the last decades, more attention has been paid to UAVs by the engineering communities because of their potential for commercial, military, and academic platforms [1][2][3]. To support military and civilian applications, the ability of UAVs to hover stably, maneuver sharply, and operate safely is considerably important, especially in takeoff and landing stages [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Nonlinear Fixed-Time Adaptive Practical Tracking Control for Quadrotor Unmanned Aerial Vehicles

Zhang

Fei

2020

Complexity

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This brief addresses the position and attitude tracking fixed-time practical control for quadrotor unmanned aerial vehicles (UAVs) subject to nonlinear dynamics. First, by combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, a NN adaptive control scheme is developed for UAVs. Then, a fixed-time adaptive law is proposed for neural networks to achieve fixed-time stability, and convergence time is dependent only on control gain parameters. Based on Lyapunov analyses and fixed-time stability theory, it is proved that the fixed-time adaptive neural network control is finite-time stable and convergence time is dependent with control parameters without initial conditions. The effectiveness of the NN fixed-time control is given through a simulation of the UAV system.

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Section: Introductionmentioning

confidence: 99%

Neural Network-Based Nonlinear Fixed-Time Adaptive Practical Tracking Control for Quadrotor Unmanned Aerial Vehicles

Zhang

Fei

2020

Complexity

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“…One of these techniques is the fuzzy logic controller [7] that has been studied in this paper. Fuzzy logic theory played a vital role in designing smart controllers that can eliminate or manipulate undesirable effects found in servo systems and improve their performance [8].…”

Section: Introductionmentioning

confidence: 99%

Design of Hybrid Fuzzy and Position-Velocity Controller for Precise Positioning of a Servo System

Abdelhameed

Mohamed

Hamed

et al. 2020

International Journal of Applied Energy Systems

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Nowadays, new technologies demand precision servo systems that have ability to provide high accuracy motion and to reject system disturbance such as load disturbance. In our previous paper (2018), the control performance of a servo system (a cart driven by a DC motor) has been improved through scheduling the parameters of a position-velocity controller according to the concept of fuzzy logic scheme. However, the targeted performance requirements such as settling time and peak overshoot still a challenging point. In order to provide high positioning accuracy, a hybrid fuzzy and position-velocity controller has been proposed in this study as a robust control scheme against the load disturbance. The effectiveness of the proposed scheme has been investigated using a numerical simulation. The obtained results are discussed and compared with those of our previous study.

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“…Traditionally, Quadratic Lyapunov Functions are in common used to construct for analysis and control design of linear and nonlinear even time delay dynamic systems [12,13]. There also exist some other formats of Lyapunov functions, such as integral Lyapunov function [14], barrier Lyapunov function [15,16], and vector Lyapunov functions [17,18]. Such attempts have enhanced Lyapunov function applications in control system design [19], for example, integral Lyapunov function for controller singularity problems and barrier Lyapunov function for output constraint problems.…”

Section: Introductionmentioning

confidence: 99%

Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure-Feedback Nonlinear Systems

Zhang

Zhu

et al. 2019

Complexity

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This paper proposes a new scheme for solving finite time neural networks adaptive tracking control issue for the nonaffine pure-feedback nonlinear system. The procedure, based on homeomorphism mapping and backstepping, effectively deals with constraint control and design difficulty induced by pure-feedback structure. The most outstanding novelty is that finite time adaptive law is proposed for training weights of neural networks. Furthermore, by combining finite time adaptive law and Lyapunov-based arguments, a valid finite time adaptive neural networks controller design algorithm is presented to ensure that system is practical finite stable (PFS) rather than uniformly ultimately bounded (UUB). Because of using the finite time adaptive law to training weights of neural networks, the closed-loop error system signals are in assurance of bounded in finite time. Benchmark simulations have well demonstrated effectiveness and efficiency of the proposed approach.

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Adaptive Barrier Control for Nonlinear Servomechanisms with Friction Compensation

Cited by 4 publications

References 61 publications

Neural Network-Based Nonlinear Fixed-Time Adaptive Practical Tracking Control for Quadrotor Unmanned Aerial Vehicles

Neural Network-Based Nonlinear Fixed-Time Adaptive Practical Tracking Control for Quadrotor Unmanned Aerial Vehicles

Design of Hybrid Fuzzy and Position-Velocity Controller for Precise Positioning of a Servo System

Homeomorphism Mapping Based Neural Networks for Finite Time Constraint Control of a Class of Nonaffine Pure-Feedback Nonlinear Systems

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