653 research outputs found
Dynamic Modelling and Adaptive Traction Control for Mobile Robots
Mobile robots have received a great deal of research in recent years. A
significant amount of research has been published in many aspects related to
mobile robots. Most of the research is devoted to design and develop some
control techniques for robot motion and path planning. A large number of
researchers have used kinematic models to develop motion control strategy for
mobile robots. Their argument and assumption that these models are valid if the
robot has low speed, low acceleration and light load. However, dynamic
modelling of mobile robots is very important as they are designed to travel at
higher speed and perform heavy duty work. This paper presents and discusses a
new approach to develop a dynamic model and control strategy for wheeled mobile
robot which I modelled as a rigid body that roles on two wheels and a castor.
The motion control strategy consists of two levels. The first level is dealing
with the dynamic of the system and denoted as Low level controller. The second
level is developed to take care of path planning and trajectory generation
Kinematic control design for wheeled mobile robots with longitudinal and lateral slip
The motion control of wheeled mobile robots at high speeds under adverse
ground conditions is a difficult task, since the robots' wheels may be subject
to different kinds of slip. This work introduces an adaptive kinematic
controller that is capable of solving the trajectory tracking problem of a
nonholonomic mobile robot under longitudinal and lateral slip. While the
controller can effectively compensate for the longitudinal slip, the lateral
slip is a more involved problem to deal with, since nonholonomic robots cannot
directly produce movement in the lateral direction. To show that the proposed
controller is still able to make the mobile robot follow a reference trajectory
under lateral and longitudinal time-varying slip, the solutions of the robot's
position and orientation error dynamics are shown to be uniformly ultimately
bounded. Numerical simulations are presented to illustrate the robot's
performance using the proposed adaptive control law
Intelligent Adaptive Motion Control for Ground Wheeled Vehicles
In this paper a new intelligent adaptive control is applied to solve a problem of motion control of ground vehicles with two independent wheels actuated by a differential drive. The major objective of this work is to obtain a motion control system by using a new fuzzy inference mechanism where the Lyapunovโs stability can be assured. In particular the parameters of the kinematical control law are obtained using an intelligent Fuzzy mechanism, where the properties of the Fuzzy maps have been established to have the stability above. Due to the nonlinear map of the intelligent fuzzy inference mechanism (i.e. fuzzy rules and value of the rule), the parameters above are not constant, but, time after time, based on empirical fuzzy rules, they are updated in function of the values of the tracking errors. Since the fuzzy maps are adjusted based on the control performances, the parameters updating assures a robustness and fast convergence of the tracking errors. Also, since the vehicle dynamics and kinematics can be completely unknown, a dynamical and kinematical adaptive control is added. The proposed fuzzy controller has been implemented for a real nonholonomic electrical vehicle. Therefore system robustness and stability performance are verified through simulations and experimental studies
ํ์ด์ด ๋ชจ๋ธ์ ์ฌ์ฉํ ์์จ ๋๋ฆฌํํธ ์ฃผํ ์ ์ด ์ค๊ณ ๋ฐ ๋ถ์
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2019. 2. ์ด๋์ค.๋ณธ ๋
ผ๋ฌธ์์๋ Wheeled Mobile Robot(WMR)์์์จ๋๋ฆฌํํธ ๋๋ผ์ด๋น ์ปจํธ๋กค๋ฌ๋ฅผ ๋์์ธ ํ๊ณ ๋ถ์ํ๋ฉฐ, ์ด๋ฅผ ์์ฉ ํ๋ก๊ทธ๋จ์ธ CarSim์ ์ฌ์ฉํ ์๋ฎฌ๋ ์ด์
์ ํตํ์ฌ ์๊ณ ๋ฆฌ์ฆ์ ๊ฒ์ฆ ํ๋ค. ์ฒซ์งธ๋ก, WMR์ ๋ค์ด๋๋ฏน์ค์ ํ์ด์ด ๋ชจ๋ธ์ ์ ์ ํ๊ณ , ์ด๋ฌํ ๋ชจ๋ธ๋ก ์ธํ ์ ์ฝ ์ฌํญ์ ๋ํ์ฌ ๋
ผ์ํ๋ค. ๋ค์์ผ๋ก, ์ฌ๋์ ๊ด์ ์์ ๋๋ฆฌํํธ ๋๋ผ์ด๋น์ ๋ถ์ํ๊ณ , ๋๋ฆฌํํธ ๋๋ผ์ด๋น ์ ์ด๊ธฐ์ ์ ์ด ๋ชฉ์ ์ ์ ์ํ๋ค. (์ฐจ๋์ ๋ฐฉํฅ๊ณผ ์ ๊ฐ์๋๋ฅผ ์ ์ดํ๋ค.) ๋๋ฆฌํํธ ๋๋ผ์ด๋น ์ ์ด๊ธฐ๋ ๊ณ -๋ ๋ฒจ ์ ์ด, ๋ชฉํ ๊ฐ์ ์ฐพ๊ธฐ ์ํ ์ต์ ํ ๊ทธ๋ฆฌ๊ณ ๊ณ -๊ฒ์ธ ์ ์ด๋ก ๊ตฌ์ฑ๋๋ค. ๋ค์์ผ๋ก, ์ ์ดํ์ง ์๋ ์๋์ ๋ํ ๋ถ์์ ์งํํ์๋ค. ๋ง์ง๋ง์ผ๋ก ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ CarSim ์๋ฎฌ ๋ ์ดํฐ๋ฅผ ์ด์ฉํ์ฌ ๊ฒ์ฆํ์๋ค. ์ ์ ์ํ์ ๋๋ฆฌํํธ ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ์, ํค์ดํ ๊ฒฝ๋ก์ ๋ํ ๋๋ฆฌํํธ ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ๋ฅผ ์ ์ ํ๋ค.Control design and analysis of Wheeled Mobile Robot(WMR) autonomous drift-driving and the simulation experiment using the CarSim simulator are presented and the analysis of the controller proceeds. We first introduce WMR dynamics, tire model and problem formulation of the WMR. We then design drift-driving control using human strategy (control side slip angle and yaw rate). The drift-driving control consists of high-level control, optimization to find desired control input and high-gain control. We analyze the uncontrolled velocity dynamics and stability of the controller. The CarSim simulation results of drift-driving on steady-state equilibriums and the hairpin path with the desired yaw rate are provided.List of Figures - v
List of Tables - vi
Abbreviations - vii
1 Introduction - 1
1.1 Motivation and related works . . . . . . . . . . . . . . . . . . . . 1
1.2 Contribution of this work . . . . . . . . . . . . . . . . . . . . . . 3
2 System Modeling - 5
2.1 Model dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Tire model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Problemformulation . . . . . . . . . . . . . . . . . . . . . . . . . 9
3 Drift-Driving Control Design - 10
3.1 High-level control . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 High-gain control . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4 Analysis of Control - 17
4.1 Internal dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2 Stability analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5 Simulation Results - 25
5.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.2 Steady-state drift-driving . . . . . . . . . . . . . . . . . . . . . . 27
5.3 Hairpin turn drift-driving . . . . . . . . . . . . . . . . . . . . . . 33
6 Conclusion and Future Work - 40
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
6.2 Future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Maste
Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning
Slip and skid compensation is crucial for mobile robots' navigation in
outdoor environments and uneven terrains. In addition to the general slipping
and skidding hazards for mobile robots in outdoor environments, slip and skid
cause uncertainty for the trajectory tracking system and put the validity of
stability analysis at risk. Despite research in this field, having a real-world
feasible online slip and skid compensation is still challenging due to the
complexity of wheel-terrain interaction in outdoor environments. This paper
presents a novel trajectory tracking technique with real-world feasible online
slip and skid compensation at the vehicle-level for skid-steering mobile robots
in outdoor environments. The sliding mode control technique is utilized to
design a robust trajectory tracking system to be able to consider the parameter
uncertainty of this type of robot. Two previously developed deep learning
models [1], [2] are integrated into the control feedback loop to estimate the
robot's slipping and undesired skidding and feed the compensator in a real-time
manner. The main advantages of the proposed technique are (1) considering two
slip-related parameters rather than the conventional three slip parameters at
the wheel-level, and (2) having an online real-world feasible slip and skid
compensator to be able to reduce the tracking errors in unforeseen
environments. The experimental results show that the proposed controller with
the slip and skid compensator improves the performance of the trajectory
tracking system by more than 27%
Modeling and Design of Longitudinal and Lateral Control System with a FeedForward Controller for a 4 Wheeled Robot
The work show in this paper progresses through a sequence of physics-based
increasing fidelity models that are used to design the robot controllers that
respect the limits of the robot capabilities, develop a reference simple
controller applicable to a large subset of tracking conditions, which include
mostly non-invasive or highly dynamic movements and define path geometry
following the control problem and develop both a simple geometric control and a
dynamic model predictive control approach. In this paper, we propose for a
nonlinear model with disturbance effect, the mathematical modeling of the
longitudinal and lateral movements using PID with a feed-forward controller.
This study proposes a feedforward controller to eliminate the disturbance
effect
Trajectory tracking and traction coordinating controller design for lunar rover based on dynamics and kinematics analysis
Trajectory tracking control is a necessary part for autonomous navigation of planetary rover and traction coordinating control can reduce the forces consumption during navigation. As a result, a trajectory tracking and traction coordinating controller for wheeled lunar rover with Rocker Bogie is proposed in the paper. Firstly, the longitudinal dynamics model and the kinematics model of six-wheeled rover are established. Secondly, the traction coordinating control algorithm is studied based on sliding mode theory with improved exponential approach law. Thirdly, based on kinematics analysis and traction system identification, the trajectory tracking controller is designed using optimal theory. Then, co-simulations between ADAMS and MATLAB/Simulink are carried out to validate the proposed algorithm, and the simulation results have confirmed the effectiveness of path tracking and traction mobility improving
- โฆ