미지환경에서의 캐터필러차량용 위치결정과

Abstract

Applications of Caterpillar Vehicle (CV) systems have been increasing in various fields during recent decades. These CV systems are able to accomplish various tasks in unsafe and dangerous places where workers cannot enter. Therefore, the researching of the CV for real applications is deeply needed. Especially, making the CV move automatically in unknown environments is one of the requisites and important tasks. To solve this problem, this dissertation presents the development results of positioning and trajectory tracking controllers for the CV system with an unknown environment. To do these tasks, the followings are done in this dissertation. Firstly, system description and mathematical modeling for the CV system are presented. The configuration of the CV system consists of mechanical design and electrical design. The system modeling of the CV system consists of a kinematic modeling and a dynamic modeling. Secondly, to make the CV track desired trajectory, a positioning system design is needed. In this dissertation, the positioning system for CV based on a simultaneous localization and mapping (SLAM) method using a Lidar sensor is suggested. The encoders are used for detecting the motion state of CV. In a slippery and unknown environment, the positioning method using encoder generates big errors. Therefore, an Extended Kalman Filter (EKF) is used to estimate the best position of the CV by combining the positioning result of the encoders and landmark positions obtained from the Lidar sensor. The EKF consists of two steps such as prediction and update. Thirdly, to track a desired sharp trajectory, a trajectory tracking controller using a backstepping control method is introduced. This trajectory tracking controller based on the kinematic modeling is designed such that the estimation global and local tracking error vectors go to zero. And then, by choosing a suitable Lyapunov function candidate and using a backstepping method, system stability is guaranteed and a control law is obtained. To implement this tracking controller, the hardware consists of a computer as the main controller, encoder sensors, compass sensor, and lidar sensor, etc. Finally, to evaluate the trajectory tracking performance of the proposed controller, the simulation and experimental results are shown. Fourthly, a backstepping-based model reference adaptive controller (MRAC) for trajectory tracking of the CV by combining a kinematic controller using a backstepping method and a dynamic controller using an MRAC is proposed. In this case, a dynamic modeling of the CV with some uncertain parameters is presented. The trajectory tracking controller based on MRAC is utilized to estimate these uncertain parameters. To design this controller, the followings are done. Firstly, a kinematic controller is designed such that global and local tracking errors converge to zero. Secondly, the dynamic controller based on the MRAC method is designed such that CV’s output velocities converge to velocity control inputs. Thirdly, by choosing a suitable Lyapunov function candidate and using a backstepping method, system stability is proven, control laws and update laws are obtained. Finally, to verify the effectiveness of the proposed MRAC method, the simulation and experimental results are shown. Finally, to guarantee the CV system to be strong robustness against external disturbances, a MIMO robust servo controller using a linear shift-invariant differential (LSID) operator is proposed. To do this task, the followings are done. Firstly, by using an estimation posture vector obtained from EKF and a reference input signal, an estimation output tracking error vector is obtained. Secondly, by operating the LSID operator to a system model and the estimation output tracking error vector, a new extended system and a new estimation control law are obtained. The controllability of the new extended system is checked. Thirdly, a MIMO robust servo controller is designed by using the pole assignment approach. Fourthly, by applying the inverse LSID operator, a servo compensator and control law for the given MIMO system are obtained. Finally, the simulation and experimental results are shown to verify the tracking performance of the proposed MIMO robust servo controller against the external disturbance. These simulation and experimental results of the proposed MIMO robust servo controller are compared to those of the backstepping controller and the backstepping-based MRAC. In addition, their standard deviations of global tracking errors are presented for evaluating the tracking performance of the proposed MIMO robust servo controller, the backstepping controller, and the backstepping-based MRAC.