Autonomous obstacle avoidance maneuvering of thrust-vectored airship with neural network control

2019 ◽  
Vol 234 (3) ◽  
pp. 689-708
Author(s):  
Amardeep Mishra
Keyword(s):  
Neural Network ◽  
Obstacle Avoidance ◽  
Sliding Mode ◽  
Network Control ◽  
Reference Trajectory ◽  
Scientific Exploration ◽  
Operational Profile ◽  
Control Framework ◽  
Equivalent Control ◽  
And Control

There has been a renewed interest in recent times in airship technology owing to its potential usage for applications ranging from defense, scientific exploration, advertising to even remote monitoring. For airships to expand operational profile, further enhancement of configurational features and control development for full autonomy are key technologies gaining attention. In this paper, beginning with the mathematical modeling of a thrust-vectored airship, the integrated motion planning and controller development for vehicle autonomy, taking into account various uncertainties, are dealt with. A rapidly exploring random tree-based obstacle avoidance path planning exercise is carried out to chart out a trajectory in the presence of obstacles. Then, a neural network-based sliding mode controller is subsequently designed that learns the unknown equivalent control in sliding mode control framework to track the reference trajectory. Simulation results presented at the end demonstrate the effectiveness of the approach.

Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

Backstepping sliding mode neural network control system for hypersonic vehicle

2019 ◽  
Vol 27 (11) ◽  
pp. 2392-2401
Author(s):  
刘 蓉 LIU Rong ◽  
黄大庆 HUANG Da-qing ◽  
姜定国 JIANG Ding-guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Sliding Mode ◽  
Hypersonic Vehicle ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System

scholarly journals A New Method of Applying Data Engine Technology to Realize Neural Network Control

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 97
Author(s):  
Song Zheng ◽  
Chao Bi ◽  
Yilin Song
Keyword(s):  
Neural Network ◽  
Control System ◽  
Real Time ◽  
Control Algorithm ◽  
Network Control ◽  
Neural Network Control ◽  
Hybrid Controller ◽  
Advanced Control ◽  
Real Time Database ◽  
And Control

This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.

scholarly journals Neural Network Control Design for an Unmanned Aerial Vehicle with a Suspended Payload

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 931 ◽  
Author(s):  
Cai Luo ◽  
Zhenpeng Du ◽  
Leijian Yu
Keyword(s):  
Neural Network ◽  
Position Control ◽  
Control Method ◽  
Sliding Mode ◽  
Network Control ◽  
Outdoor Environment ◽  
Test Results ◽  
Cluttered Environments ◽  
Aerial Vehicle ◽  
Backstepping Sliding Mode Control

Unmanned aerial vehicles (UAVs) demonstrate excellent manoeuvrability in cluttered environments, which makes them a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenges for a drone with a package connected by a wire is analysed. During the delivering task, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network-based backstepping sliding mode control method is designed, which is capable of monitoring the drone’s flight path and desired attitude with a suspended cable attached. The convergence of the position and attitude errors together with the Lyapunov function are employed to attest to the robustness of the nonlinear transportation platform. The proposed control system is tested with a simulation and in an outdoor environment. The simulation and open field test results for the UAV transportation platform verify the controllers’ reliability.

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