scholarly journals On Limitations to the Achievable Path Tracking Performance for Linear Multivariable Plants

Author(s):  
Daniel E. Miller ◽  
Richard H. Middleton
2000 ◽  
Vol 9 (4) ◽  
pp. 337-349 ◽  
Author(s):  
Corinna Lathan ◽  
Kevin Cleary ◽  
Laura Traynor

Computed tomography (CT)-directed needle biopsies are routinely performed to gather tissue samples near the spine. As currently practiced, this procedure requires a great deal of spatial reasoning, skill, and training on the part of the interventional radiologist. Our goal was to evaluate the procedure through a task analysis and to make recommendations as to how the procedure could be improved through technological intervention. To this end, a spine biopsy surgical simulator was developed to mimic the current procedure and to serve as a development testbed for procedure innovation. Our methods for looking at the biopsy procedure itself included a task analysis (which produces a detailed list of tasks needed to complete a goal, their order, and time to completion) and an evaluation of human performance measures related to our simulator interface. Experiments were run to examine the effects of force and visual feedback on path-tracking performance and to determine the effects of time delay in the visual feedback on path-tracking performance. Force feedback improved performance in the conditions with visual feedback and in the conditions with visual feedback and time delay.


2021 ◽  
Vol 45 (12) ◽  
pp. 1167-1176
Author(s):  
Jun Ha Sohn ◽  
Chang-Ho Lee ◽  
Yong-Joo Kim ◽  
Sung-Soo Kim

2016 ◽  
pp. 49-54
Author(s):  
S. Inoue ◽  
H. Inoue ◽  
T. Ozawa ◽  
P. Raksincharoensak ◽  
M. Nagai

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 144
Author(s):  
Taewon Ahn ◽  
Yongki Lee ◽  
Kihong Park

This paper describes an integrated autonomous driving (AD) control system for an autonomous vehicle with four independent in-wheel motors (IWMs). The system consists of two parts: the AD controller and the chassis controller. These elements are functionally integrated to improve vehicle stability and path tracking performance. The vehicle is assumed to employ an IWM independently at each wheel. The AD controller implements longitudinal/lateral path tracking using proportional-integral(PI) control and adaptive model predictive control. The chassis controller is composed of two lateral control units: the active front steering (AFS) control and the torque vectoring (TV) control. Jointly, they find the yaw moment to maintain vehicle stability using sliding mode control; AFS is prioritized over TV to enhance safety margin and energy saving. Then, the command yaw moment is optimally distributed to each wheel by solving a constrained least-squares problem. Validation was performed using simulation in a double lane change scenario. The simulation results show that the integrated AD control system of this paper significantly improves the path tracking capability and vehicle stability in comparison with other control systems.


Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6845
Author(s):  
Yoonsuk Choi ◽  
Wonwoo Lee ◽  
Jeesu Kim ◽  
Jinwoo Yoo

This paper proposes a novel model predictive control (MPC) algorithm that increases the path tracking performance according to the control input. The proposed algorithm reduces the path tracking errors of MPC by updating the sampling time of the next step according to the control inputs (i.e., the lateral velocity and front steering angle) calculated in each step of the MPC algorithm. The scenarios of a mixture of straight and curved driving paths were constructed, and the optimal control input was calculated in each step. In the experiment, a scenario was created with the Automated Driving Toolbox of MATLAB, and the path-following performance characteristics and computation times of the existing and proposed MPC algorithms were verified and compared with simulations. The results prove that the proposed MPC algorithm has improved path-following performance compared to those of the existing MPC algorithm.


2020 ◽  
Vol 44 (2) ◽  
pp. 213-227
Author(s):  
Yadong Ding ◽  
Yaoyao Wang ◽  
Feng Ju ◽  
Bai Chen

Time-optimal trajectory planning algorithms have been widely adopted to minimize the motion time by exploiting the dynamics and joint allowable torques of a robotic manipulator. However, the actual joint torques may exceed the joint allowable torques because of modelling errors or disturbances in the control system. When the torque limit is added for actuator safety, the controller will have no margin to deal with modeling errors or disturbances, which may lead to large path tracking errors. An on-line trajectory time scaling method called path velocity controller can improve path tracking performance by modifying the path velocity when torque saturation occurs. However, the path velocity controller is based on a feedforward or computed torque controller, so the dynamic modelling errors will worsen the path tracking performance. In addition, the motion time may also be increased because the dynamic modelling errors could result in longer duration time of torque saturation. To further improve the path tracking performance of a path velocity controller, a path velocity controller with an on-line parameter estimate mechanism is proposed. The simulation results show that the proposed method can achieve a better path tracking performance and shorter motion time.


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