• Journal of Positioning, Navigation, and Timing
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• v.2 no.2
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• pp.123-130
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• 2013

Autonomous agricultural machines that are operated in small-scale farmland frequently experience turning and changes in direction. Thus, unlike when they are operated in large-scale farmland, the steering control systems need to be controlled precisely so that travel errors can be minimized. This study aims to develop a control algorithm for improving the path tracking performance of a steering system by analyzing the effect of the setting of the waypoint, which serves as the reference point for steering when an autonomous agricultural machine moves along a path or a coordinate, on control errors. A simulation was performed by modeling a 26-hp tractor steering system and by applying the equations of motion of a tractor, with the use of a computer. Path tracking errors could be reduced using an algorithm which sets the waypoint for steering on a travel path depending on the radius of curvature of the path and which then controls the speed and steering angle of the vehicle, rather than by changing the steering speed or steering ratio which are dependent on mechanical performance.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.552-563
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• 2005

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.4
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• pp.457-466
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• 2005

In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.556-559
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• 1997

In this paper, we propose a cooperative multi-robot control algorithm. Specifically, the cooperative task is that two mobile robots should transfer a long rigid object along a predefined path. To resolve the problem, we introduce the master-slave concept for two mobile robots, which have the same structure. According to the velocity of the master robot and the positions of two robots on the path, the velocity of the slave robot is determined. In case that the robots can't move further, the role of the robot is interchanged. The effectiveness of this decentralized algorithm is proved by computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.723-732
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• 2001

In this paper, the neurofuzzy controller of optically guided AGV is proposed to improve the path-tracking performance A differential steered AGV has front-side and rear-side optical sensors, which can identify the guiding path. Due to the discontinuity of measured data in optical sensors, optically guided AGVs break away easily from the guiding path and path-tracking performance is being degraded. Whenever the On/Off signals in the optical sensors are generated discontinuously, the motion errors can be measured and updated. After sensing, the variation of motion errors can be estimated continuously by the dead reckoning method according to left/right wheel angular velocity. We define the estimated contour error as the sum of the measured contour in the sensing error and the estimated variation of contour error after sensing. The neurofuzzy system consists of incorporating fuzzy controller and neural network. The center and width of fuzzy membership functions are adaptively adjusted by back-propagation learning to minimize th estimated contour error. The proposed control system can be compared with the traditional fuzzy control and decision system in their network structure and learning ability. The proposed control strategy is experience through simulated model to check the performance.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.19 no.4
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• pp.37-43
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• 2011

Lunar exploration program has been prepared with the aim of launch in the 2020's. As part of it, a lunar lander demonstrator has been developed which is the model for verifying all the system, such as structure, propulsion and control system before launch to deep space. After verifying all the system, the demonstrator will be evaluated by flight test. This paper deals with path tracking controller based on thrusters for the demonstrator. For this, first we derive equations of motion according to the allocation of thrusters and design the path tracking controller. The signal generated from the controller is continuous so PWPF(Pulse-Width Pulse-Frequency) modulator is adopted for generating on/off signal. Finally MATLAB simulation is performed for evaluating the path tracking ability and the final landing velocity.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.7
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• pp.599-605
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• 2017

For an unmanned vehicle to be driven on the off-road terrain, it is necessary to consider the vehicle's stability. This paper suggests a path tracking controller for simulation of real-time vehicle stability analysis. The path tracking controller uses the preview distance to track the given trajectory. The disturbance moment is estimated using the yaw moment observer, and this information is used for compensation in the yaw moment control. On a curved path, the vehicle's desired velocity is determined from the curvature of the path. Because the vehicle is equipped with six independent motor driven wheels, the driving torques are distributed on all the wheels. The effectiveness of the path tracking controller is verified using ADAMS/MATLAB co-simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1047-1051
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• 1993

A fuzzy controller of a manipulator with artificial rubber muscles is proposed. The fuzzy logic controller as a compensator is described to control the trajectory tracking of a -two link manipulator, where computed torque control method has already assumed to be applied. We shows that the fuzzy compensator with a simple adaptive scaling technique is effective for the robust control when there exist model uncertainties and/or untuned feedback gains. The effectiveness of the proposed control method is illustrated by some experimental results for a circular path tracking.

• Journal of Navigation and Port Research
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• v.42 no.2
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• pp.87-96
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• 2018

A trajectory control system plays an important role in controlling motions of marine vehicle when a series of way points or a path is given. In this paper, a sliding mode control (SMC)-based trajectory tracking controller for marine vehicles is presented. A small-sized unmanned ship is considered as a control object. Both speed and heading angle of a ship should be controlled for tracking control. The common point of related researches was to separate ship's speed and heading angle in control methods. In this research, a new control law from a general sliding mode theory that can be applied to MIMO (multi input multi output) system is derived and both speed and heading angle of a ship can be controlled simultaneously. The propulsion force and rudder force are also applied in modeling stage to achieve accurate simulation. Disturbance induced by wind is also tackled in the dynamics considering robustness of the proposed control scheme. In the simulation, we employed a way-point method to generate ship's trajectory and applied the proposed control scheme to ship's trajectory tracking control. Our results confirmed that the tracking error was converged to zero, thus demonstrating the effectiveness of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1418-1423
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• 2003

In this paper, a nonlinear controller based on sliding mode control is applied to a two -wheeled Welding Mobile Robot (WMR) to track a smooth-curved welding path at a constant velocity of the welding point. T he mobile robot is considered in terms of dynamics model in Cartesian coordinates and its parameters are exactly known . To obtain the controller, the tracking errors are defined, and the two sliding surfaces are chosen to guarantee that the errors converge to zero asymptotically. Two cases are to be considered: fixed torch and controllable torch. In addition, a simple way of measuring the errors is introduced using two potentiometers. The simulation results are included to illustrate the performance of the control law.