scholarly journals AUTONOMOUS NAVIGATION OF SMALL UAVS BASED ON VEHICLE DYNAMIC MODEL

2016 ◽  
Vol XL-3/W4 ◽  
pp. 117-122 ◽  
Author(s):  
M. Khaghani ◽  
J. Skaloud
Keyword(s):  
Dynamic Model ◽  
Electromagnetic Interference ◽  
Autonomous Navigation ◽  
Process Model ◽  
Low Cost ◽  
Model Parameters ◽  
Main Process ◽  
Control Input ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

scholarly journals AUTONOMOUS NAVIGATION OF SMALL UAVS BASED ON VEHICLE DYNAMIC MODEL

2016 ◽  
Vol XL-3/W4 ◽  
pp. 117-122 ◽  
Author(s):  
M. Khaghani ◽  
J. Skaloud
Keyword(s):  
Dynamic Model ◽  
Electromagnetic Interference ◽  
Autonomous Navigation ◽  
Process Model ◽  
Low Cost ◽  
Model Parameters ◽  
Main Process ◽  
Control Input ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

This paper presents a novel approach to autonomous navigation for small UAVs, in which the vehicle dynamic model (VDM) serves as the main process model within the navigation filter. The proposed method significantly increases the accuracy and reliability of autonomous navigation, especially for small UAVs with low-cost IMUs on-board. This is achieved with no extra sensor added to the conventional INS/GNSS setup. This improvement is of special interest in case of GNSS outages, where inertial coasting drifts very quickly. In the proposed architecture, the solution to VDM equations provides the estimate of position, velocity, and attitude, which is updated within the navigation filter based on available observations, such as IMU data or GNSS measurements. The VDM is also fed with the control input to the UAV, which is available within the control/autopilot system. The filter is capable of estimating wind velocity and dynamic model parameters, in addition to navigation states and IMU sensor errors. Monte Carlo simulations reveal major improvements in navigation accuracy compared to conventional INS/GNSS navigation system during the autonomous phase, when satellite signals are not available due to physical obstruction or electromagnetic interference for example. In case of GNSS outages of a few minutes, position and attitude accuracy experiences improvements of orders of magnitude compared to inertial coasting. It means that during such scenario, the position-velocity-attitude (PVA) determination is sufficiently accurate to navigate the UAV to a home position without any signal that depends on vehicle environment.

scholarly journals Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2467 ◽  
Author(s):  
Hery Mwenegoha ◽  
Terry Moore ◽  
James Pinchin ◽  
Mark Jabbal
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Moment Of Inertia ◽  
Model Parameters ◽  
Main Process ◽  
Model Based ◽  
Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

Co-Simulation of Power Steering Control and Vehicle Dynamic Responses

2001 ◽  
Author(s):  
Gene Y. Liao
Keyword(s):  
Dynamic Model ◽  
General Purpose ◽  
Dynamic Responses ◽  
Steering Wheel ◽  
Vehicle Dynamic ◽  
Steering Control ◽  
Integrated Simulation ◽  
Full Vehicle ◽  
Power Steering ◽  
Vehicle Dynamic Model

Abstract Many general-purpose and specialized simulation codes are becoming more flexible which allows analyses to be carried out simultaneously in a coupled manner called co-simulation. Using co-simulation technique, this paper develops an integrated simulation of an Electric Power Steering (EPS) control system with a full vehicle dynamic model. A full vehicle dynamic model interacting with EPS control algorithm is concurrently simulated on a single bump road condition. The effects of EPS on the vehicle dynamic behavior and handling responses resulting from steer and road input are analyzed and compared with proving ground experimental data. The comparisons show reasonable agreement on tie-rod load, rack displacement, steering wheel torque and tire center acceleration. This developed co-simulation capability may be useful for EPS performance evaluation and calibration as well as for vehicle handling performance integration.

Investigation on a semi-active hydraulic damping strut to reduce vehicle in situ shift vibration

2019 ◽  
Vol 26 (1-2) ◽  
pp. 3-18
Author(s):  
Dao-Yong Wang ◽  
Wen-Can Zhang ◽  
Xia-Guang Zeng
Keyword(s):  
Dynamic Response ◽  
Dynamic Model ◽  
Automatic Transmission ◽  
Degree Of Freedom ◽  
Vehicle Dynamic ◽  
Response Evaluation ◽  
Evaluation Indexes ◽  
Engine Mount ◽  
Vehicle Dynamic Model

In order to reduce the shock and vibration caused by torque disturbance of the gearbox in vehicles equipped with automatic transmission in the process of in situ shift, a novel semi-active hydraulic damping strut is introduced in the powertrain mounting system. The dynamic response evaluation indexes of vehicle in situ shift are put forward, and a 13-degree of freedom vehicle dynamic model including the semi-active hydraulic damping strut is established. The optimized dynamic characteristic parameters are acquired according to the principle of sharing force and the 13-degree of freedom vehicle dynamic model. The dynamic response evaluation indexes with and without the semi-active hydraulic damping strut are calculated using the 13-degree of freedom vehicle dynamic model in the process of in situ shift, and the calculation results show that the vibration of a vehicle can be reduced by the introduction of a semi-active hydraulic damping strut. Experiments are carried out to analyze the vibration response of the vehicle with and without a semi-active hydraulic damping strut, and the results show that the shock and vibration of the vehicle are reduced by introducing the semi-active hydraulic damping strut. The theoretical calculation values of active-side acceleration of the engine mount and torque strut are consistent with the experimental values, which show that the 13-degree of freedom vehicle dynamic model is reasonable.

Lateral robust iterative learning control for unmanned driving robot vehicle

2020 ◽  
Vol 234 (7) ◽  
pp. 792-808
Author(s):  
Shuhua Su ◽  
Gang Chen
Keyword(s):  
Dynamic Model ◽  
Iterative Learning Control ◽  
Tracking Error ◽  
Learning Control ◽  
Steering System ◽  
Path Tracking ◽  
Iterative Learning ◽  
Control Law ◽  
Vehicle Dynamic ◽  
Vehicle Dynamic Model

In order to achieve stable steering and path tracking, a lateral robust iterative learning control method for unmanned driving robot vehicle is proposed. Combining the nonlinear tire dynamic model with the vehicle dynamic model, the nonlinear vehicle dynamic model is constructed. The structure of steering manipulator of unmanned driving robot vehicle is analyzed, and the kinematics model and dynamics model of steering manipulator of unmanned driving robot vehicle are established. The structure of vehicle steering system is analyzed, and the dynamic model of vehicle steering system is established. Vehicle steering angle model is established by taking vehicle path tracking error and vehicle yaw angle error as input. Combining with the typical iterative learning control law, the robust term is added to the control law, and a robust iterative learning controller for steering manipulator system of unmanned driving robot vehicle is designed. The proposed controller’s stability and astringency are proved. The effectiveness of the proposed method is verified by comparing it with other control methods and human driver simulation tests.

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