scholarly journals Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Yang Liu ◽  
Qing Zhou
Keyword(s):  
Neural Control ◽  
Switching Function ◽  
Parameter Uncertainties ◽  
Adaptive Neural Control ◽  
Remarkable Feature ◽  
Performance Control ◽  
Prescribed Performance ◽  
Prescribed Performance Control ◽  
Control Scheme ◽  
Smooth Switching

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.

scholarly journals Fuzzy-approximation-based prescribed performance control of air-breathing hypersonic vehicles with input constraints

2019 ◽  
Vol 103 (1) ◽  
pp. 003685041987735
Author(s):  
Xingge Li ◽  
Gang Li ◽  
Yan Zhao ◽  
Xuchao Kang
Keyword(s):  
Control Method ◽  
Hypersonic Vehicle ◽  
Hypersonic Vehicles ◽  
Input Constraints ◽  
Air Breathing ◽  
Performance Control ◽  
Prescribed Performance ◽  
Fuzzy Approximation ◽  
Prescribed Performance Control ◽  
Control Scheme

In this article, aiming at the longitudinal dynamics model of air-breathing hypersonic vehicles, a fuzzy-approximation-based prescribed performance control scheme with input constraints is proposed. First, this article presents a novel prescribed performance function, which does not depend on the sign of initial tracking error. And combining prescribed performance control method with backstepping control, the control scheme can ensure that system can converge at a prescribed rate of convergence, overshoot, and steady-state error. In order to solve the problem that backstepping control method needs to be differentiated multiple times, fuzzy approximators are used to estimate the unknown functions, and norm estimation approach is used to simplify the computation of fuzzy approximator. Aiming at the problem of input saturation of actuator in subsystem of air-breathing hypersonic vehicle, the new auxiliary system is designed to ensure the stability and robustness of air-breathing hypersonic vehicle system under input constraints. Finally, the effectiveness of the proposed control strategy is verified by simulation analysis.

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 Design of decentralized adaptive control approach for large-scale nonlinear systems subjected to input delays under prescribed performance

2021 ◽  
Author(s):  
Yu-Qun Han
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Large Scale ◽  
Control Method ◽  
Performance Control ◽  
Prescribed Performance ◽  
Control Approach ◽  
Prescribed Performance Control ◽  
Control Scheme ◽  
Input Delays

Abstract For the first time, the issue of input delays and prescribed performance control is investigated in the same framework for large-scale nonlinear systems in this study, and a original adaptive decentralized control method is proposed take advantage of multi-dimensional Taylor network (MTN) method. Firstly, the problem of input delays is solved by introducing new variables, and a new form of coordinate transformation is introduced before controller design, which simplified the control system. Secondly, the problem of prescribed performance control is coped with by integrating the idea of prescribed performance into the Lyapunov functions of first step of backstepping of each subsystem. Thirdly, MTNs are employed to evaluate the combination of unknown functions, and then a decentralized MTN-based adaptive control scheme is developed by way of backstepping technology. The theoretical analysis indicates that the proposed control scheme can implement the expected tracking goals under the condition of meeting the prescribed performance control. Finally, one numerical example is given to show the validity and rationality of the proposed control method.

scholarly journals Composite Learning Prescribed Performance Control of Nonlinear Systems

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fang Zhu ◽  
Wei Xiang ◽  
Chunzhi Yang
Keyword(s):  
Tracking Error ◽  
Original System ◽  
Feedback System ◽  
Closed Loop System ◽  
Performance Control ◽  
Prescribed Performance ◽  
Persistent Excitation ◽  
Prescribed Performance Control ◽  
Control Scheme ◽  
The Stability

This paper investigates a composite learning prescribed performance control (PPC) scheme for uncertain strict-feedback system. Firstly, a prescribed performance boundary (PPB) condition is developed for the tracking error, and the original system is transformed into an equivalent one by using a transformation function. In order to ensure that the tracking error satisfies the PPB, a sufficient condition is given. Then, a control scheme of PPC combined with neural network (NN) and backstepping technique is proposed. However, the unknown functions cannot be guaranteed to estimate accurately by this method. To solve this problem, predictive errors are defined by applying online recorded date and instantaneous date. Furthermore, novel composite learning laws are proposed to update NN weights based on a partial persistent excitation (PE) condition. Subsequently, the stability of the closed-loop system is guaranteed and all signals are kept bounded by using composite learning PPC method. Finally, simulation results verify the effectiveness of the proposed methods.

scholarly journals Neural Prescribed Performance Control for Uncertain Marine Surface Vessels without Accurate Initial Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Wenjie Si ◽  
Xunde Dong
Keyword(s):  
Tracking Control ◽  
Neural Control ◽  
Dead Zone ◽  
Control Performance ◽  
Trajectory Tracking Control ◽  
Performance Control ◽  
Prescribed Performance ◽  
Prescribed Performance Control ◽  
Surface Vessels ◽  
Marine Surface

This paper deals with the problems concerned with the trajectory tracking control with prescribed performance for marine surface vessels without velocity measurements in uncertain dynamical environments, in the presence of parametric uncertainties, unknown disturbances, and unknown dead-zone. First, only the ship position and heading measurements are available and a high-gain observer is used to estimate the unmeasurable velocities. Second, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured, while the requirement for the initial error is removed via the preprocessing. At last, based on neural network approximation in combination with backstepping and Lyapunov synthesis, a robust adaptive neural control scheme is developed to handle the uncertainties and input dead-zone characteristics. Under the designed adaptive controller for marine surface vessels, all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB), and the prescribed transient and steady tracking control performance is guaranteed. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

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