A nonlinear trajectory tracking controller for mobile robots with velocity limitation via parameters regulation

SUMMARYThis paper addresses the problem of trajectory tracking control in mobile robots under velocity limitations. Following the results reported in ref. [1], the problem of trajectory tracking considering control actions constraint is focused and the zero convergence of the tracking errors is demonstrated. In this work, the original methodology is expanded considering a controller that depends not only on the position but also on the velocity. A simple scheme is obtained, which can be easily implemented in others controllers of the literature. Experimental results are presented and discussed, demonstrating the good performance of the controller.

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A self-tuning trajectory tracking controller for wheeled mobile robots

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-02-2019-0032 ◽

2019 ◽

Vol 46 (6) ◽

pp. 828-838 ◽

Cited By ~ 3

Author(s):

Pouya Panahandeh ◽

Khalil Alipour ◽

Bahram Tarvirdizadeh ◽

Alireza Hadi

Keyword(s):

Neural Networks ◽

Convergence Rate ◽

Mobile Robots ◽

Trajectory Tracking ◽

Suggested Method ◽

Wheeled Mobile Robots ◽

Trajectory Tracking Control ◽

Content Type ◽

Tracking Controller ◽

Trajectory Tracking Controller

Purpose Trajectory tracking is a common problem in the field of mobile robots which has attracted a lot of attention in the past two decades. Therefore, besides the search for new controllers to achieve a better performance, improvement and optimization of existing control rules are necessary. Trajectory tracking control laws usually contain constant gains which affect greatly the robot’s performance. Design/methodology/approach In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller. Findings Simulations and experiments are performed to assess the ability of the suggested scheme. The obtained results show the effectiveness of the proposed method. Originality/value In this paper, a method based on neural networks is introduced to automatically upgrade the gains of a well-known trajectory tracking controller of wheeled mobile robots. The suggested method speeds up the convergence rate of the main controller.

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Trajectory Tracking and Re-planning with Model Predictive Control of Autonomous Underwater Vehicles

Journal of Navigation ◽

10.1017/s0373463318000668 ◽

2018 ◽

Vol 72 (2) ◽

pp. 321-341 ◽

Cited By ~ 4

Author(s):

Zhen Hu ◽

Daqi Zhu ◽

Caicha Cui ◽

Bing Sun

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Trajectory Tracking ◽

Tracking Control ◽

Autonomous Underwater Vehicles ◽

Underwater Vehicles ◽

Trajectory Tracking Control ◽

Tracking Controller ◽

Trajectory Tracking Controller ◽

Set Up

The trajectory tracking of Autonomous Underwater Vehicles (AUV) is an important research topic. However, in the traditional research into AUV trajectory tracking control, the AUV often follows human-set trajectories without obstacles, and trajectory planning and tracking are separated. Focusing on this separation, a trajectory re-planning controller based on Model Predictive Control (MPC) is designed and added into the trajectory tracking controller to form a new control system. Firstly, an obstacle avoidance function is set up for the design of an MPC trajectory re-planning controller, so that the re-planned trajectory produced by the re-planning controller can avoid obstacles. Then, the tracking controller in the MPC receives the re-planned trajectory and obtains the optimal tracking control law after calculating the object function with a Sequential Quadratic Programming (SQP) optimisation algorithm. Lastly, in a backstepping algorithm, the speed jump can be sharp while the MPC tracking controller can solve the speed jump problem. Simulation results of different obstacles and trajectories demonstrate the efficiency of the proposed MPC trajectory re-planning tracking control algorithm for AUVs.

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Robust finite-time trajectory tracking control for a space manipulator with parametric uncertainties and external disturbances

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering ◽

10.1177/09544100211014754 ◽

2021 ◽

pp. 095441002110147

Author(s):

Qijia Yao

Keyword(s):

Trajectory Tracking ◽

Finite Time ◽

Tracking Control ◽

Disturbance Observer ◽

Control Method ◽

Parametric Uncertainties ◽

External Disturbances ◽

Trajectory Tracking Control ◽

Space Manipulator ◽

Tracking Controller

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this study, a robust finite-time tracking control method is proposed for the rapid and accurate trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of parametric uncertainties and external disturbances. First, a baseline finite-time tracking controller is designed to track the desired position of the space manipulator based on the homogeneous method. Then, a finite-time disturbance observer is designed to accurately estimate the lumped uncertainties. Finally, a robust finite-time tracking controller is developed by integrating the baseline finite-time tracking controller with the finite-time disturbance observer. Rigorous theoretical analysis for the global finite-time stability of the whole closed-loop system is provided. The proposed robust finite-time tracking controller has a relatively simple structure and can guarantee the position and velocity tracking errors converge to zero in finite time even subject to lumped uncertainties. To the best of the authors’ knowledge, there are really limited existing controllers can achieve such excellent performance under the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control method.

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