A Composite Model Predictive and Super Twisting Sliding Mode Controller for Stable and Robust Trajectory Tracking of Autonomous Ground Vehicles

Author(s):  
Hassan El Atwi ◽  
Naseem Daher
2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091698 ◽  
Author(s):  
Pengcheng Wang ◽  
Dengfeng Zhang ◽  
Baochun Lu

This article investigates a difficult problem which focuses on the external disturbance and dynamic uncertainty in the process of trajectory tracking. This article presents a robust adaptive fuzzy terminal sliding mode controller with low-pass filter. The low-pass filter can provide smooth position and speed signals. The fuzzy terminal sliding mode controller can achieve fast convergence and desirable tracking precision. Chattering is eliminated with continuous control law, due to high-frequency switching terms contained in the first derivative of actual control signals. Ignoring the prior knowledge upper bound, the controller can reduce the influence of the uncertain kinematics and dynamics in the actual situation. Finally, the experiment is carried out and the results show the performance of the proposed controller.


Author(s):  
Cheng Liu ◽  
Zaojian Zou ◽  
Jianchuan Yin

Trajectory tracking is an importance practice in ship motion control field. It attracts more attention recently due to its difficulties. Trajectory tracking requires the ship to arrive pinpoint location at exact time. It is a underactuated system because the degrees of freedom of control inputs are fewer than the degrees of freedom that needed to be controlled. In this paper, a hierarchical sliding mode controller and a common sliding mode controller are proposed to deal with the trajectory tracking problem of underactuated surface vessels. Simulation results validate the tracking performance of the proposed controllers. The closed-loop stability is testified by the Lyapunov stability theorem.


Author(s):  
Yaswanth Siramdasu ◽  
Farbod Fahimi

Sliding mode controller for trajectory tracking of a surface vessel is designed based on a 3DOF dynamic model. The model has six unknown parameters. For parameter identification, four special test scenarios are defined to isolate and identify one of the six parameters at a time. The identification tests are performed on a robotic boat which has an onboard PC104 computer and a navigation sensor providing vessel’s dynamic states in real-time. The data from experiments are used to determine the model parameters. A sliding mode controller is designed based on the identified model, and is implemented and tested on a real robotic boat. The experiments show the excellent performance of the controller.


Sign in / Sign up

Export Citation Format

Share Document