2019
DOI: 10.1007/s12555-018-0889-9
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Quintic Polynomial-based Obstacle Avoidance Trajectory Planning and Tracking Control Framework for Tractor-trailer System

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Cited by 34 publications
(12 citation statements)
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“…In [17], a PID‐based controller was designed for a single tractor–trailer. By integrating the polynomial function and a model predictive controller, a trajectory planning obstacle avoidance controller has been proposed in [18]. In [19], a non‐linear state feedback controller was proposed for TTWMRs by using the adaptive back‐stepping method.…”
Section: Introductionmentioning
confidence: 99%
“…In [17], a PID‐based controller was designed for a single tractor–trailer. By integrating the polynomial function and a model predictive controller, a trajectory planning obstacle avoidance controller has been proposed in [18]. In [19], a non‐linear state feedback controller was proposed for TTWMRs by using the adaptive back‐stepping method.…”
Section: Introductionmentioning
confidence: 99%
“…Model predictive control is an advanced method of process control, it has been adopted in the trajectory tracking control recently [16]. In [17], model predictive control was used to design a posture controller to track the path and avoid obstacle from forward and backward. In [18] a nonlinear model predictive controller was designed by introducing a cost function and trajectory for a tractor-trailer system, and the performance of the method was illustrated.…”
Section: Introductionmentioning
confidence: 99%
“…In the past years, numerous studies have been published concerning the path tracking control of articulated vehicles, especially tractor-trailer vehicles. Yue et al [10] designed a posture controller based on model predictive control (MPC) to track the forward and backward obstacle avoidance maneuvers for tractor-trailer system. Khalaji [11] proposed a novel PID-based kinematic controller as a non-model-based controller to navigate the tractor-trailer wheeled robot follows desired trajectories.…”
Section: Introductionmentioning
confidence: 99%