Receding Horizon Optimal controller for reference trajectory tracking in Mars entry guidance

This article establishes an innovative and general approach for the dynamic modeling and trajectory tracking control of a serial robotic manipulator with n-rigid links connected by revolute joints and mounted on an autonomous wheeled mobile platform. To this end, first the Gibbs–Appell formulation is applied to derive the motion equations of the mentioned robotic system in closed form. In fact, by using this dynamic method, one can eliminate the disadvantage of dealing with the Lagrange Multipliers that arise from nonholonomic system constraints. Then, based on a predictive control approach, a general recursive formulation is used to analytically obtain the kinematic control laws. This multivariable kinematic controller determines the desired values of linear and angular velocities for the mobile base and manipulator arms by minimizing a point-wise quadratic cost function for the predicted tracking errors between the current position and the reference trajectory of the system. Again, by relying on predictive control, the dynamic model of the system in state space form and the desired velocities obtained from the kinematic controller are exploited to find proper input control torques for the robotic mechanism in the presence of model uncertainties. Finally, a computer simulation is performed to demonstrate that the proposed algorithm can dynamically model and simultaneously control the trajectories of the mobile base and the end-effector of such a complicated and high-degree-of-freedom robotic system.

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Event-triggered integral sliding mode fixed time control for trajectory tracking of autonomous underwater vehicle

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331221994380 ◽

2021 ◽

pp. 014233122199438

Author(s):

Bo Su ◽

Hongbin Wang ◽

Ning Li

Keyword(s):

Trajectory Tracking ◽

Control Strategy ◽

Autonomous Underwater Vehicle ◽

Sliding Mode ◽

Fixed Time ◽

Underwater Vehicle ◽

Reference Trajectory ◽

Integral Sliding Mode ◽

Time Control ◽

Event Triggered

In this paper, an event-triggered integral sliding mode fixed-time control method for trajectory tracking problem of autonomous underwater vehicle (AUV) with disturbance is investigated. Initially, the global fixed time stability is ensured with conventional periodic sampling method for reference trajectory tracking. By introducing fixed time integral sliding mode manifold, fixed time control strategy is expressed for the AUV, which can effectively eliminate the singularity. Correspondingly, in order to reduce the damage caused by chattering phenomenon, an adaptive fixed-time method is proposed based on the designed continuous integral terminal sliding mode (ITSM) to ensure that the trajectory tracking for AUV is achieved in fixed-time with external disturbance. In order to reduce resource consumption in the process of transmission network, the event-triggered sliding mode control strategy is designed which condition is triggered by an event. Also, Zeno behavior is avoided by proof of theoretical. It is shown that the upper bounds of settling time are only dependent on the parameters of controller. Theoretical analysis and simulation experiment results show that the presented methods can realize the control object.

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Trajectory Tracking For Quadrotors: An Optimization-Based Planning Followed By Controlling Approach

10.21203/rs.3.rs-963714/v2 ◽

2021 ◽

Author(s):

Geesara Kulathunga ◽

Dmitry Devitt ◽

Alexandr Klimchik

Keyword(s):

Trajectory Tracking ◽

Euclidean Distance ◽

Control Policy ◽

Multiple Shooting ◽

Reference Trajectory ◽

Linear Quadratic ◽

Linear Quadratic Gaussian ◽

Distance Transformation ◽

Direct Collocation ◽

Better Than

Abstract We present an optimization-based reference trajectory tracking method for quadrotor robots for slow-speed maneuvers. The proposed method uses planning followed by the controlling paradigm. The basic concept of the proposed method is an analogy to Linear Quadratic Gaussian (LQG) in which Nonlinear Model Predictive Control (NMPC) is employed for predicting optimal control policy in each iteration. Multiple-shooting (MS) is suggested over Direct-collocation (DC) for imposing constraints when modelling the NMPC. Incremental Euclidean Distance Transformation Map (EDTM) is constructed for obtaining the closest free distances relative to the predicted trajectory; these distances are considered obstacle constraints. The reference trajectory is generated, ensuring dynamic feasibility. The objective is to minimize the error between the quadrotor’s current pose and the desired reference trajectory pose in each iteration. Finally, we evaluated the proposed method with two other approaches and showed that our proposal is better than those two in terms of reaching the goal without any collision. Additionally, we published a new dataset, which can be used for evaluating the performance of trajectory tracking algorithms.

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