scholarly journals Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiangwei Bu ◽  
Qiong Wang ◽  
Yan Zhao ◽  
Guangjun He
Keyword(s):  
Neural Control ◽  
Computational Cost ◽  
Lyapunov Stability Theory ◽  
Parametric Uncertainties ◽  
Air Breathing ◽  
Control Approach ◽  
Adaptive Neural Controller ◽  
Learning Parameter ◽  
Uniformly Ultimate Boundedness ◽  
Control Methodology

In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV) directly using nonaffine models instead of affine ones. For the velocity dynamics, an adaptive neural controller is devised based on a minimal-learning parameter (MLP) technique for the sake of decreasing computational loads. The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping. The special contributions are that the control architecture is concise and the computational cost is low. Moreover, the exploited controller possesses good practicability since there is no need for affine models. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory. Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.

scholarly journals Adaptive Neural Back-Stepping Control with Constrains for a Flexible Air-Breathing Hypersonic Vehicle

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Pengfei Wang ◽  
Jie Wang ◽  
Jianming Shi ◽  
Chang Luo ◽  
Shili Tan ◽  
...  
Keyword(s):  
Hypersonic Vehicle ◽  
Parametric Uncertainties ◽  
Simulation Studies ◽  
Input Constraint ◽  
Tracking Errors ◽  
Control Laws ◽  
Air Breathing ◽  
Control Approach ◽  
System Uncertainties ◽  
Back Stepping Control

The design of an adaptive neural back-stepping control for a flexible air-breathing hypersonic vehicle (AHV) in the presence of input constraint and aerodynamic uncertainty is discussed. Based on functional decomposition, the dynamics can be decomposed into the velocity subsystem and the altitude subsystem. To guarantee the exploited controller’s robustness with respect to parametric uncertainties, neural network (NN) is applied to approximate the lumped uncertainty of each subsystem of AHV model. The exceptional contribution is that novel auxiliary systems are introduced to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the actuators are saturated. Finally, simulation studies are made to illustrate the effectiveness of the proposed control approach in spite of the flexible effects, system uncertainties, and varying disturbances.

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 Modified LQG/LTR Control Methodology in Active Structural Control

2003 ◽  
Vol 22 (2) ◽  
pp. 97-108 ◽  
Author(s):  
Yan Sheng ◽  
Chao Wang ◽  
Ying Pan ◽  
Xinhua Zhang
Keyword(s):  
Structural Control ◽  
Signal To Noise Ratio ◽  
Open Loop ◽  
Control Force ◽  
Linear Quadratic ◽  
Linear Quadratic Gaussian ◽  
Control Approach ◽  
Active Structural Control ◽  
Loop Transfer Recovery ◽  
Control Methodology

This paper presents a new active structural control design methodology comparing the conventional linear-quadratic-Gaussian synthesis with a loop-transfer-recovery (LQG/LTR) control approach for structures subjected to ground excitations. It results in an open-loop stable controller. Also the closed-loop stability can be guaranteed. More importantly, the value of the controller's gain required for a given degree of LTR is orders of magnitude less than what is required in the conventional LQG/LTR approach. Additionally, for the same value of gain, the proposed controller achieves a much better degree of recovery than the LQG/LTR-based controller. Once this controller is obtained, the problems of control force saturation are either eliminated or at least dampened, and the controller band-width is reduced and consequently the control signal to noise ratio at the input point of the dynamic system is increased. Finally, numerical examples illustrate the above advantages.

scholarly journals Optimal Pressure Boundary Control of Steady Multiscale Fluid-Structure Interaction Shell Model Derived from Koiter Equations

Fluids ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 149
Author(s):  
Andrea Chierici ◽  
Leonardo Chirco ◽  
Sandro Manservisi
Keyword(s):  
Optimal Control ◽  
Solid Wall ◽  
Fluid Structure Interaction ◽  
Computational Cost ◽  
Robin Boundary Condition ◽  
Design Parameters ◽  
Fluid Structure ◽  
Control Approach ◽  
Structure Interaction ◽  
Fluid Boundary

Fluid-structure interaction (FSI) problems are of great interest, due to their applicability in science and engineering. However, the coupling between large fluid domains and small moving solid walls presents numerous numerical difficulties and, in some configurations, where the thickness of the solid wall can be neglected, one can consider membrane models, which are derived from the Koiter shell equations with a reduction of the computational cost of the algorithm. With this assumption, the FSI simulation is reduced to the fluid equations on a moving mesh together with a Robin boundary condition that is imposed on the moving solid surface. In this manuscript, we are interested in the study of inverse FSI problems that aim to achieve an objective by changing some design parameters, such as forces, boundary conditions, or geometrical domain shapes. We study the inverse FSI membrane model by using an optimal control approach that is based on Lagrange multipliers and adjoint variables. In particular, we propose a pressure boundary optimal control with the purpose to control the solid deformation by changing the pressure on a fluid boundary. We report the results of some numerical tests for two-dimensional domains to demonstrate the feasibility and robustness of our method.

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

2021 ◽  
pp. 095965182110131
Author(s):  
Guoqing Zhang ◽  
Shen Gao ◽  
Jiqiang Li ◽  
Weidong Zhang
Keyword(s):  
Neural Control ◽  
Fault Tolerant ◽  
Lyapunov Stability Theory ◽  
Actuator Faults ◽  
Closed Loop System ◽  
External Disturbances ◽  
Unmanned Surface Vehicle ◽  
Adaptive Parameters ◽  
Control Gain ◽  
Event Triggered

This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

Automatic Flight Control of Unmanned Helicopter Based on Nonlinear Model

2012 ◽  
Vol 472-475 ◽  
pp. 1492-1499
Author(s):  
Run Xia Guo
Keyword(s):  
Nonlinear Model ◽  
Flight Control ◽  
Attitude Control ◽  
Lyapunov Stability Theory ◽  
Test Results ◽  
Unmanned Helicopter ◽  
Control Laws ◽  
Control Approach ◽  
State Dependent ◽  
Compensation Technique

The Unmanned helicopter (UMH) movement was divided into two parts, namely, attitude and trajectory motion. And then a two-timescale nonlinear model was established. The paper improved and expanded state dependent riccati equation (SDRE) control approach, deriving analytical conditions for achieving global asymptotic stability with lyapunov stability theory. Proof was given. By combining improved SDRE control with nonlinear feed-forward compensation technique, the full envelop flight attitude control laws could be designed. On the basis of attitude control, trajectory controller was developed. Actual flight tests were carried out. Test results show that the control strategy is highly effective.

A Control Approach to Guide Nonpharmaceutical Interventions in the Treatment of COVID-19 Disease Using a SEIHRD Dynamical Model

2021 ◽  
Vol 30 (3) ◽  
pp. 323-346
Author(s):  
Fernando Pazos ◽  
◽  
Flavia E. Felicioni ◽  
Keyword(s):  
Public Health ◽  
Ad Hoc ◽  
Early Stage ◽  
Control Law ◽  
Parametric Uncertainties ◽  
Social Unrest ◽  
Control Approach ◽  
Health Measures ◽  
Optimal Intensity ◽  
Nonpharmaceutical Interventions

The recent worldwide epidemic of COVID-19 disease, for which there are no medications to cure it and the vaccination is still at an early stage, led to the adoption of public health measures by governments and populations in most of the affected countries to avoid the contagion and its spread. These measures are known as nonpharmaceutical interventions (NPIs), and their implementation clearly produces social unrest as well as greatly affects the economy. Frequently, NPIs are implemented with an intensity quantified in an ad hoc manner. Control theory offers a worthwhile tool for determining the optimal intensity of the NPIs in order to avoid the collapse of the healthcare system while keeping them as low as possible, yielding concrete guidance to policymakers. A simple controller, which generates a control law that is easy to calculate and to implement is proposed. This controller is robust to large parametric uncertainties in the model used and to some level of noncompliance with the NPIs.

scholarly journals Neural Network Self-Tuning Control for a Piezoelectric Actuator

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3342 ◽  
Author(s):  
Wenjun Li ◽  
Chen Zhang ◽  
Wei Gao ◽  
Miaolei Zhou
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Input Voltage ◽  
Rate Dependency ◽  
Output Displacement ◽  
Control Approach ◽  
Adaptive Parameter ◽  
Hysteresis Nonlinearity ◽  
Self Tuning ◽  
Control Methodology

Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.

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