scholarly journals Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
Hao Zhou ◽  
Tawfiqur Rahman ◽  
Wanchun Chen
Keyword(s):  
Neural Network ◽  
Terminal Velocity ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Reference Trajectory ◽  
Inverse Dynamic ◽  
Guidance Law ◽  
Terminal Constraints ◽  
Entry Guidance ◽  
Improved Performance

This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal constraints for position, flight path, and azimuth angle. In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. The method enables faster implementation in achieving fully constrained entry flight. Results from simulations indicate improved performance of the neural network assisted method. The scheme is expected to have prospect for further research on automated onboard control of terminal velocity for both reentry and terminal guidance laws.

scholarly journals Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8178
Author(s):  
Leiyan Yu ◽  
Xianyu Wang ◽  
Zeyu Hou ◽  
Zaiyou Du ◽  
Yufeng Zeng ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Control Points ◽  
Parallel Parking ◽  
Radial Basis ◽  
Initial Path ◽  
Continuous Curvature

To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bézier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bézier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bézier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces.

scholarly journals CMAC Trained Optimum Mid course Guidance for Tactical Flight Vehicle

2021 ◽  
Vol 71 (6) ◽  
pp. 826-835
Author(s):  
G. N. Kumar ◽  
A. K. Sarkar
Keyword(s):  
Neural Network ◽  
Direct Method ◽  
Terminal Velocity ◽  
Lateral Acceleration ◽  
Terminal Phase ◽  
Optimal Trajectories ◽  
Flight Vehicle ◽  
Cmac Neural Network ◽  
Guidance Law ◽  
Network Training

This paper discusses design and validation of neural network based mid-course guidance law of a surface to air flight vehicle. In present study, initially different optimal trajectories have been generated off-line of different pursuer-evader engagements by ensuring minimum flight time, maximum terminal velocity and favorable handing over conditions for seeker based terminal guidance. These optimal trajectories have been evolved by nonlinear programming based direct method of optimisation. The kinematic information of both pursuer and evader, generated based on these trajectories have been used to train cerebellar model articulate controller (CMAC) neural network. Later for a given engagement scenario an on-line near optimal mid-course guidance law has been evolved based on output of trained network. Training has been carried out by CMAC type supervisory neural network. The tested engagement condition is within input/output training space of neural network. Seeker based homing guidance has been used for terminal phase. Complete methodology has been validated along pitch plane of pursuer-evader engagement. During mid-course phase, the guidance demand has been tracked by attitude hold autopilot and during terminal phase, the guidance demanded lateral acceleration has been tracked by acceleration autopilot. System robustness has been studied in presence of plant parameter variations and sensor noise under Monte Carlo Platform.

Dynamic pressure based mid-course guidance scheme for hypersonic boost-glide vehicle

2018 ◽  
Vol 233 (9) ◽  
pp. 3211-3222
Author(s):  
G Naresh Kumar ◽  
AK Sarkar ◽  
SE Talole
Keyword(s):  
Search Algorithm ◽  
Dynamic Pressure ◽  
Robustness Analysis ◽  
Pattern Search ◽  
Reference Trajectory ◽  
Flight Trajectory ◽  
Guidance Law ◽  
Terminal Constraints ◽  
Pressure Maximum ◽  
Guidance Algorithm

In this study, a guidance scheme for an aerodynamically controlled hypersonic boost-glide class of flight vehicle is proposed. In this work, optimum glide dynamic pressure corresponding to maximum L/ D throughout the flight is calculated and a mid-course guidance law formulation to track the dynamic pressure while suppressing phugoid oscillations is proposed for real-time flight trajectory shaping. Efficacy of the proposed guidance scheme has been demonstrated through simulation studies. Robustness analysis on the proposed guidance algorithm is carried out using Monte Carlo technique. Lastly, a pattern search algorithm-based offline generated maximum L/ D optimal trajectory existing in literature, which meets minimum dynamic pressure, maximum airframe skin temperature, as well as other in-flight and terminal constraints is used as reference trajectory to evaluate the performance of the proposed guidance scheme.

Neural-network-based real-time trajectory replanning for Mars entry guidance

2016 ◽  
Vol 231 (14) ◽  
pp. 2634-2645
Author(s):  
X Zheng ◽  
H Huang ◽  
W Li
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Trajectory Optimization ◽  
Reference Trajectory ◽  
The Real ◽  
The Neural Network ◽  
Entry Guidance ◽  
Computational Speed ◽  
Uncertain Factor ◽  
Predictor Corrector

The real-time trajectory replanning method which is used for the guidance of Mars entry is investigated in this paper. Comparing with the traditional Mars entry guidance methods, such as the reference-trajectory tracking guidance and predictor–corrector guidance, the real-time trajectory replanning method can increase the reliability of the mission remarkably. When faults occur during the Mars entry phase, a replacement trajectory will be planned quickly. Due to the limited onboard computing capacity, replanning the trajectory onboard is a challenging task. Corresponding to this problem, the neural network is trained to approximate the dynamics of the atmospheric entry. The uncertain factor of the atmospheric density is also included in the neural network. Then, by using the characters of the neural network, the analytical expressions of the Jacobian which are needed in trajectory optimization are derived. Finally, an estimation-replanning guidance procedure is introduced. The numerical simulation shows that the proposed guidance strategy can decrease the error of final states effectively, and the neural network approximation improves the computational speed of the nonlinear programming solver remarkably, which makes the method more suitable for use onboard.

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