scholarly journals MPC Based Trajectory Tracking for An Automonous Deep-Sea Tracked Mining Vehicle

2021 ◽  
Vol 1 (2) ◽  
pp. 1-13
Author(s):  
Hongyun Wu ◽  
Yuheng Chen ◽  
Hongmao Qin
Keyword(s):  
Obstacle Avoidance ◽  
Kalman Filtering ◽  
Trajectory Tracking ◽  
Deep Sea ◽  
Autonomous Vehicles ◽  
External Disturbance ◽  
Kinematic Model ◽  
Vehicle Body ◽  
Model Uncertainties ◽  
External Disturbances

Model predictive control (MPC) has been successfully used in trajectory tracking for autonomous vehicles based on certain kinematic model under low external disturbance conditions, but when there are model uncertainties and external disturbances, autonomous vehicles will fail to follow the pre-set trajectory. This paper studies trajectory tracking control based on MPC for an autonomous deep-sea tracked mining vehicle in polymetallic nodule mines with model uncertainty and external disturbances. A MPC algorithm is designed for trajectory tracking. To address model uncertainties caused by vehicle body subsidence and track slippage, a drive wheel speed correction controller is designed by experimental data fitting, and Kalman filtering (KF) and adaptive Kalman filtering (AKF) are introduced to improve tracking performance by rejecting external disturbances especially during curve tracking. To handle dead zones and obstacles during actual operation, an obstacle avoidance strategy is proposed that uses the tri-circular arc obstacle avoidance trajectory with an equal curvature for path re-planning. Finally, Simulink&Recurdyn co-simulations validate the performance of the proposed MPC controller through a comparison with nonlinear MPC(NMPC).

scholarly journals Fixed-Time Trajectory Tracking Control of Autonomous Surface Vehicle with Model Uncertainties and Disturbances

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jiawen Cui ◽  
Haibin Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
Fixed Time ◽  
Convergence Time ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Trajectory Tracking Control ◽  
Integral Sliding Mode ◽  
Control Scheme

The issue of fixed-time trajectory tracking control for the autonomous surface vehicles (ASVs) system with model uncertainties and external disturbances is investigated in this paper. Particularly, convergence time does not depend on initial conditions. The major contributions include the following: (1) An integral sliding mode controller (ISMC) via integral sliding mode surface is first proposed, which can ensure that the system states can follow the desired trajectory within a fixed time. (2) Unknown external disturbances are absolutely estimated by means of designing a fixed-time disturbance observer (FTDO). By combining the FTDO and ISMC techniques, a new control scheme (FTDO-ISMC) is developed, which can achieve both disturbance compensation and chattering-free condition. (3) Aiming at reconstructing the unknown nonlinear dynamics and external disturbances, a fixed-time unknown observer (FTUO) is proposed, thus providing the FTUO-ISMC scheme that finally achieves trajectory tracking of ASVs with unknown parameters. Finally, simulation tests and detailed comparisons indicate the effectiveness of the proposed control scheme.

scholarly journals A hybrid robust adaptive control for a quadrotor UAV via mass observer and robust controller

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Laihong Zhou ◽  
Shunjian Xu ◽  
Hong Jin ◽  
Huihua Jian
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Trajectory Tracking ◽  
External Disturbance ◽  
Robust Controller ◽  
External Disturbances ◽  
The Real ◽  
Quadrotor Uav ◽  
Robust Adaptive ◽  
Varying Mass

The flight stability and safety of the quadrotor unmanned aerial vehicle (UAV) with variable mass are the key problems that limit its application. In order to improve the stability and steady-state control precision of the quadrotor system against slow-varying mass and external disturbance, a new robust adaptive flight control algorithm is developed and analyzed in detail in this paper. Firstly, a mass observer based on adaptive control theory is designed to estimate the real-time mass and correct the mass parameter of the UAV. Then, a hyperbolic tangent function and a proportional integral (PI) controller is added into the attitude controller to eliminate the effect of the external disturbances. Finally, a hybrid robust adaptive controller (HRAC) developed with backstepping control method is used here for the trajectory tracking of quadrotor. The boundedness of the nonlinear system is verified by Lyapunov stability theory and uniformly ultimately bounded theorem. The trajectory tracking simulation experiments are presented in MATLAB/SIMULINK simulation environment. According to the simulation results, the real-time mass of quadrotor can be estimated by HRAC satisfactorily under the condition of external disturbances, while the estimate error of mass is only 6.4% of its own. In addition, HRAC can provide a higher trajectory tracking accuracy compared with robust optimal backstepping control (ROBC) and robust generalized dynamic inversion (RGDI). The results suggest a promising route based on the mass observer and hybrid robust controller toward slow-varying mass and the external disturbance as effective robustness control strategy for quadrotor UAV.

Robust sliding mode control of uncertain nonlinear systems with chattering alleviating scheme

2021 ◽  
pp. 2140042
Author(s):  
Bhausaheb B. Musmade ◽  
Balasaheb M. Patre
Keyword(s):  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Uncertain Systems ◽  
Sliding Mode ◽  
External Disturbance ◽  
Uncertain Nonlinear Systems ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Controlled System ◽  
Mode Control

In this paper, a class of uncertain nonlinear systems is investigated and a sliding mode control (SMC) design is presented. The method is proposed for uncertain systems with model uncertainties, nonlinear dynamics and external disturbances. Using nominal system and related bounds of uncertainties, a chattering alleviating scheme is also proposed, which can ensure the robust SMC law. The performance and the significance of the controlled system are investigated under variation in system parameters and also in presence of an external disturbance. The simulation results indicate that performance of the proposed controller is effective compared to the existing controllers.

scholarly journals Human-Like Obstacle Avoidance Trajectory Planning and Tracking Model for Autonomous Vehicles That Considers the Driver’s Operation Characteristics

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4821
Author(s):  
Qinyu Sun ◽  
Yingshi Guo ◽  
Rui Fu ◽  
Chang Wang ◽  
Wei Yuan
Keyword(s):  
Obstacle Avoidance ◽  
Trajectory Planning ◽  
Trajectory Tracking ◽  
Potential Field ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Planning Model ◽  
Repulsive Potential ◽  
Tracking Model ◽  
Operation Characteristics

Developing a human-like autonomous driving system has gained increasing amounts of attention from both technology companies and academic institutions, as it can improve the interpretability and acceptance of the autonomous system. Planning a safe and human-like obstacle avoidance trajectory is one of the critical issues for the development of autonomous vehicles (AVs). However, when designing automatic obstacle avoidance systems, few studies have focused on the obstacle avoidance characteristics of human drivers. This paper aims to develop an obstacle avoidance trajectory planning and trajectory tracking model for AVs that is consistent with the characteristics of human drivers’ obstacle avoidance trajectory. Therefore, a modified artificial potential field (APF) model was established by adding a road boundary repulsive potential field and ameliorating the obstacle repulsive potential field based on the traditional APF model. The model predictive control (MPC) algorithm was combined with the APF model to make the planning model satisfy the kinematic constraints of the vehicle. In addition, a human driver’s obstacle avoidance experiment was implemented based on a six-degree-of-freedom driving simulator equipped with multiple sensors to obtain the drivers’ operation characteristics and provide a basis for parameter confirmation of the planning model. Then, a linear time-varying MPC algorithm was employed to construct the trajectory tracking model. Finally, a co-simulation model based on CarSim/Simulink was established for off-line simulation testing, and the results indicated that the proposed trajectory planning controller and the trajectory tracking controller were more human-like under the premise of ensuring the safety and comfort of the obstacle avoidance operation, providing a foundation for the development of AVs.

A practical trajectory tracking control of autonomous vehicles using linear time-varying MPC method

2021 ◽  
pp. 095440702110229
Author(s):  
Hui Pang ◽  
Nan Liu ◽  
Chuan Hu ◽  
Zijun Xu
Keyword(s):  
Trajectory Tracking ◽  
Autonomous Vehicles ◽  
Linear Time ◽  
Tracking System ◽  
Rapid Development ◽  
Tracking Error ◽  
Kinematic Model ◽  
Computational Cost ◽  
Error Model ◽  
Time Varying

With the rapid development and implementation of autonomous vehicles (AVs), the simultaneous and accurate trajectory tracking problem for such AVs has become a popular research topic. This paper proposes a comprehensive linear time-varying model predictive controller (LTV-MPC) design for a type of AV, aiming to achieve good trajectory tracking in a practical driving scenario. First, a two-degree-of-freedom kinematic model of an AV is established. Next, an error model of the AV’s trajectory tracking system is constructed using linear time-varying theory. A successive linearization is introduced to linearize the nonlinear tracking error model, and a quadratic programming optimization problem is then formulated. Thus, the control sequence for this AV is incorporated into the predictive control framework, and the desired controller can be solved with a relatively higher computational efficiency and lower computational cost. Finally, the effectiveness and performance of the proposed controller are validated via a comparison of simulations conducted using MATLAB software and experiments conducted using a self-established test platform. The results demonstrate that the proposed LTV-MPC method can track the prescribed reference road trajectories with high precision and stability for an AV under various driving conditions.

scholarly journals Robust Backstepping Trajectory Tracking Control of a Quadrotor with Input Saturation via Extended State Observer

2019 ◽  
Vol 9 (23) ◽  
pp. 5184 ◽  
Author(s):  
Nguyen Xuan-Mung ◽  
Sung Kyung Hong
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Input Saturation ◽  
Extended State Observer ◽  
Control Input ◽  
Extended State ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Trajectory Tracking Control ◽  
Aerial Vehicles

Quadrotor unmanned aerial vehicles have become increasingly popular in several applications, and the improvement of their control performance has been documented in several studies. Nevertheless, the design of a high-performance tracking controller for aerial vehicles that reliably functions in the simultaneous presence of model uncertainties, external disturbances, and control input saturation still remains a challenge. In this paper, we present a robust backstepping trajectory tracking control of a quadrotor with input saturation. The controller design accounts for both parameterized uncertainties and external disturbances, whereas a new auxiliary system is proposed to cope with control input saturation. Taking into account that only the position and attitude of the quadrotor are measurable, we devise an extended state observer to supply the estimations of unmeasured states, model uncertainties, and external disturbances. We strictly prove the stability of the closed-loop system by using the Lyapunov theory and demonstrate the effectiveness of the proposed algorithm through numerical simulations.

Finite-time model-free trajectory tracking control for overhead cranes subject to model uncertainties, parameter variations and external disturbances

2019 ◽  
Vol 41 (12) ◽  
pp. 3516-3525 ◽  
Author(s):  
Menghua Zhang
Keyword(s):  
Trajectory Tracking ◽  
Finite Time ◽  
Sliding Mode ◽  
Overhead Crane ◽  
Model Uncertainties ◽  
Asymptotic Results ◽  
External Disturbances ◽  
Trajectory Tracking Control ◽  
Model Free ◽  
Parameter Variations

The payload mass and the cable length are always different/uncertain for various transportation tasks and external disturbances that accompany industrial overhead crane systems. In addition, existing control methods can obtain merely asymptotic results. To solve the aforementioned problems, an accurate model-free trajectory tracking controller subject to finite time convergence for overhead crane systems is proposed based on the suitably defined non-singular terminal sliding vector. Moreover, the proposed controller is absolutely continuous, addressing the limitations and shortcomings of the traditional sliding mode control. Lyapunov techniques are used to prove that the proposed controller guarantees finite-time tracking result and the finite time T is calculated. Simulation and experimental results are included to demonstrate the robustness of the proposed controller with respect to model uncertainties, parameter variations and external disturbances.

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