scholarly journals Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Shifen Shao ◽  
Kaisheng Zhang ◽  
Jun Li ◽  
Jirong Wang
Keyword(s):  
Neural Control ◽  
Dead Zone ◽  
Tracking Error ◽  
Transient Behavior ◽  
Robotic Manipulators ◽  
System Stability ◽  
Control Effect ◽  
Tracking Errors ◽  
Control Scheme ◽  
Funnel Control

This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.

scholarly journals Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Qing Zhou ◽  
Xiaobo Qu
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Flight Vehicle ◽  
Model Uncertainties ◽  
Adaptive Neural Control ◽  
Prescribed Performance ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Control Scheme ◽  
Robust Adaptive

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

scholarly journals Backstepping Control Based on Constrained Command Filter for Hypersonic Flight Vehicles with AOA and Actuator Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Junbao Wei ◽  
Haiyan Li ◽  
Ming Guo ◽  
Jing Li ◽  
Huang Huang
Keyword(s):  
Tracking Error ◽  
Lyapunov Stability Theory ◽  
Backstepping Control ◽  
Control Input ◽  
Tracking Errors ◽  
Hypersonic Flight ◽  
Flight Vehicles ◽  
Control Scheme ◽  
Actuator Constraints ◽  
Command Filter

An antisaturation backstepping control scheme based on constrained command filter for hypersonic flight vehicle (HFV) is proposed with the consideration of angle of attack (AOA) constraint and actuator constraints of amplitude and rate. Firstly, the HFV system model is divided into velocity subsystem and height subsystem. Secondly, to handle AOA constraint, a constrained command filter is constructed to limit the amplitude of the AOA command and retain its differentiability. And the constraint range is set in advance via a prescribed performance method to guarantee that the tracking error of the AOA meets the constraint conditions and transient and steady performance. Thirdly, the proposed constrained command filter is combined with the auxiliary system for actuator constraints, which ensures that the control input meets the limited requirements of amplitude and rate, and the system is stable. In addition, the tracking errors of the system are proved to be ultimately uniformly bounded based on the Lyapunov stability theory. Finally, the effectiveness of the proposed method is verified by simulation.

scholarly journals Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Huanqing Wang ◽  
Qi Zhou ◽  
Xuebo Yang ◽  
Hamid Reza Karimi
Keyword(s):  
Neural Control ◽  
Large Scale ◽  
Controller Design ◽  
Dead Zone ◽  
Design Procedure ◽  
Rbf Neural Networks ◽  
Large Scale Systems ◽  
Dead Zones ◽  
Stabilization Control ◽  
Control Scheme

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

scholarly journals Dual-Motor Synchronization Control Design Based on Adaptive Neural Networks Considering Full-State Constraints and Partial Asymmetric Dead-Zone

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4261
Author(s):  
Chunhong Jin ◽  
Mingjie Cai ◽  
Zhihao Xu
Keyword(s):  
Neural Networks ◽  
State Constraints ◽  
Controller Design ◽  
Dead Zone ◽  
Tracking Error ◽  
Servo System ◽  
Zone Model ◽  
Control Scheme ◽  
Full State ◽  
Full State Constraints

This paper proposes a command filtering backstepping (CFB) scheme with full-state constraints by leading into time-varying barrier Lyapunov functions (T-BLFs) for a dual-motor servo system with partial asymmetric dead-zone. Firstly, for the convenience of the controller design, the conventional partial asymmetric dead-zone model was replaced with a new smooth differentiable model owing to its non-smoothness. Secondly, neural networks (NNs) were utilized to approximate the nonlinearity that exists in the dead-zone model, improving the control performance. In addition, CFB was utilized to deal with the inherent computational explosion problem of the traditional backstepping method, and an error compensation mechanism was introduced to further reduce the filtering errors. Then, by applying the T-BLF to the CFB process, the states of the system never violated the prescribed constraints, and all signals in the dual-motor servo system were bounded. The tracking error and synchronization error could converge to a small desired neighborhood of the origin. In the end, the effectiveness of the proposed control scheme was verified through simulations.

scholarly journals Adaptive Neural Control with Prespecified Tracking Accuracy for a Class of Switched Systems Subject to Input Delay

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xu Zhang ◽  
Jian Wu ◽  
Wu Ai ◽  
Jing Li
Keyword(s):  
Switched Systems ◽  
Neural Control ◽  
A Priori ◽  
Tracking Error ◽  
Input Delay ◽  
Tracking Accuracy ◽  
Adaptive Tracking Control ◽  
Control Scheme ◽  
Switching Functions ◽  
Adaptive Neural Controller

This paper is concerned with the adaptive tracking control design for a class of uncertain switched systems subject to input delay. Unlike the existing results on uncertain switched systems, the new proposed control scheme ensures that the tracking error converges to the accuracy given a priori according to the requirement. To achieve this aim, some nonnegative switching functions are introduced to replace the conventional Lyapunov function. In addition, neural networks are used to approximate the unknown simultaneous domination functions. By combining the backstepping technique and some common nonnegative switching functions, a stable adaptive neural controller is established. It can be shown that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and the tracking error satisfies the predefined accuracy. The effectiveness of the proposed control scheme is verified by a simulation example.

A Robustly Stable Multiestimation-Based Adaptive Control Scheme for Robotic Manipulators

2005 ◽  
Vol 128 (2) ◽  
pp. 414-421 ◽  
Author(s):  
A. Ibeas ◽  
M. de la Sen
Keyword(s):  
Adaptive Systems ◽  
Closed Loop ◽  
Sudden Change ◽  
Robotic Manipulators ◽  
Adaptive Controller ◽  
Time Instant ◽  
Loop System ◽  
System Stability ◽  
Closed Loop System ◽  
Control Scheme

A multiestimation-based robust adaptive controller is designed for robotic manipulators. The control scheme is composed of a set of estimation algorithms running in parallel along with a supervisory index proposed with the aim of evaluating the identification performance of each one. Then, a higher-order level supervision algorithm decides in real time the estimator that will parametrize the adaptive controller at each time instant according to the values of the above supervisory indexes. There exists a minimum residence time between switches in such a way that the closed-loop system stability is guaranteed. It is verified through simulations that multiestimation-based schemes can improve the transient response of adaptive systems as well as the closed-loop behavior when a sudden change in the parameters or in the reference input occurs by appropriate switching between the various estimation schemes running in parallel. The closed-loop system is proved to be robustly stable under the influence of uncertainties due to a poor modeling of the robotic manipulator. Finally, the usefulness of the proposed scheme is highlighted by some simulation examples.

scholarly journals Neural network–based nonaffine control of air-breathing hypersonic vehicles with prescribed performance

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141875524 ◽  
Author(s):  
Xiangwei Bu ◽  
Qing Wang
Keyword(s):  
Steady State ◽  
Neural Control ◽  
Tracking Error ◽  
Computational Cost ◽  
Design Procedure ◽  
Hypersonic Vehicles ◽  
Tracking Errors ◽  
Air Breathing ◽  
Prescribed Performance ◽  
Transient And Steady State

This article investigates a novel nonaffine control strategy using neural networks for an air-breathing hypersonic vehicle. Actual actuators are regarded as additional state variables and virtual control inputs are derived from low-computational cost neural approximations, while a new altitude control design independent of affine models is addressed for air-breathing hypersonic vehicles. To further reduce the computational load, an advanced regulation algorithm is applied to devise adaptive laws for neural estimations. Moreover, a new prescribed performance mechanism is exploited, which imposes preselected bounds on the transient and steady-state tracking error performance via developing new performance functions, capable of guaranteeing altitude and velocity tracking errors with small overshoots. Unlike some existing neural control methodologies, the proposed prescribed performance-based nonaffine control approach can ensure tracking errors with preselected transient and steady-state performance. Meanwhile, the complex design procedure of backstepping is also avoided. Finally, simulation results are presented to validate the design.

scholarly journals Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Min Wang ◽  
Huiping Ye ◽  
Zhiguang Chen
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Dynamic Surface Control ◽  
Dynamic Surface ◽  
Flexible Joint ◽  
Persistent Excitation ◽  
Neural Learning ◽  
Control Scheme ◽  
Flexible Joint Manipulator

This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

New Control Topology for Railway Static Power Conditioner

2014 ◽  
Vol 596 ◽  
pp. 625-630
Author(s):  
Ying Pan ◽  
Juan Bao
Keyword(s):  
Reactive Power ◽  
Dead Zone ◽  
System Stability ◽  
Loop Control ◽  
Traction Power Supply System ◽  
Power Conditioner ◽  
Three Phase ◽  
Control Scheme ◽  
Static Power ◽  
Traction Power Supply

The traction power supply system of electric railway has the disadvantages of heavy unbalanced three phase, large harmonics and reactive power. Based on back-to-back converter, railway static power conditioner (RPC) can effectively balance the load between two arms, compensate the harmonic current and reactive power. As the conventional PI control is difficult to trace the waveform, a dual-loop control scheme was applied. The control scheme reduces the influences of the factors, such as sampling and calculation delay, dead-zone, parameters’ shift, on the system stability and enhances the robustness of the whole system. It can also eliminate the negative sequence current and three-phase voltage fluctuations of the primary side, improve the power factor and harmonic filter, so that electrified railway power quality problems can be resolved perfectly. The whole design was provided. Analysis and simulation results testify the effectiveness of the proposed control scheme.

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