A Fuzzy Logic-based Cascade Control without Actuator Saturation for the Unmanned Underwater Vehicle Trajectory Tracking

• URL: http://arxiv.org/abs/2210.01706v1
• Date: Tue, 4 Oct 2022 16:01:12 GMT
• Title: A Fuzzy Logic-based Cascade Control without Actuator Saturation for the Unmanned Underwater Vehicle Trajectory Tracking
• Authors: Danjie Zhu, Simon X. Yang, Mohammad Biglarbegian
• Abstract summary: An intelligent control strategy is proposed to eliminate the actuator saturation problem in the trajectory tracking process of unmanned underwater vehicles (UUV) The strategy consists of two parts: for the kinematic modeling part, a fuzzy logic-refined backstepping control is developed to achieve control velocities within acceptable ranges and errors of small fluctuations. With the control velocities computed by the kinematic model and applied forces derived by the dynamic model, the robustness and accuracy of the UUV trajectory without actuator saturation can be achieved.
• Score: 4.828832506496124
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An intelligent control strategy is proposed to eliminate the actuator saturation problem that exists in the trajectory tracking process of unmanned underwater vehicles (UUV). The control strategy consists of two parts: for the kinematic modeling part, a fuzzy logic-refined backstepping control is developed to achieve control velocities within acceptable ranges and errors of small fluctuations; on the basis of the velocities deducted by the improved kinematic control, the sliding mode control (SMC) is introduced in the dynamic modeling to obtain corresponding torques and forces that should be applied to the vehicle body. With the control velocities computed by the kinematic model and applied forces derived by the dynamic model, the robustness and accuracy of the UUV trajectory without actuator saturation can be achieved.

Related papers

• Iterative Learning Control of Fast, Nonlinear, Oscillatory Dynamics (Preprint) [0.0]
  nonlinear, chaotic, and are often too fast for active control schemes. We develop an alternative active controls system using an iterative, trajectory-optimization and parameter-tuning approach. We demonstrate that the controller is robust to missing information and uncontrollable parameters as long as certain requirements are met.
  arXiv  Detail & Related papers  (2024-05-30T13:27:17Z)
• Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks [0.24578723416255746]
  In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability. We propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level control strategy.
  arXiv  Detail & Related papers  (2024-02-04T15:54:03Z)
• A Tricycle Model to Accurately Control an Autonomous Racecar with Locked Differential [71.53284767149685]
  We present a novel formulation to model the effects of a locked differential on the lateral dynamics of an autonomous open-wheel racecar. We include a micro-steps discretization approach to accurately linearize the dynamics and produce a prediction suitable for real-time implementation.
  arXiv  Detail & Related papers  (2023-12-22T16:29:55Z)
• TLControl: Trajectory and Language Control for Human Motion Synthesis [68.09806223962323]
  We present TLControl, a novel method for realistic human motion synthesis. It incorporates both low-level Trajectory and high-level Language semantics controls. It is practical for interactive and high-quality animation generation.
  arXiv  Detail & Related papers  (2023-11-28T18:54:16Z)
• DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control [62.24301794794304]
  Deep Adaptive Trajectory Tracking (DATT) is a learning-based approach that can precisely track arbitrary, potentially infeasible trajectories in the presence of large disturbances in the real world. DATT significantly outperforms competitive adaptive nonlinear and model predictive controllers for both feasible smooth and infeasible trajectories in unsteady wind fields. It can efficiently run online with an inference time less than 3.2 ms, less than 1/4 of the adaptive nonlinear model predictive control baseline.
  arXiv  Detail & Related papers  (2023-10-13T12:22:31Z)
• Distributed Leader Follower Formation Control of Mobile Robots based on Bioinspired Neural Dynamics and Adaptive Sliding Innovation Filter [14.66072990853587]
  We propose a bioinspired neural dynamic based backstepping and sliding mode control hybrid formation control method. The proposed control strategy resolves the impractical speed jump issue that exists in the conventional backstepping design. We performed multiple simulations to demonstrate the efficiency and effectiveness of the proposed formation control strategy.
  arXiv  Detail & Related papers  (2023-05-03T17:29:46Z)
• A GOA-Based Fault-Tolerant Trajectory Tracking Control for an Underwater Vehicle of Multi-Thruster System without Actuator Saturation [9.371458775465825]
  This paper proposes an intelligent fault-tolerant control (FTC) strategy to tackle the trajectory tracking problem of an underwater vehicle (UV) under thruster damage (power loss) cases. In the proposed control strategy, the trajectory tracking component is formed by a refined backstepping algorithm that controls the velocity variation and a sliding mode control deducts the torque/force outputs.
  arXiv  Detail & Related papers  (2023-01-04T21:30:16Z)
• A Hybrid Tracking Control Strategy for an Unmanned Underwater Vehicle Aided with Bioinspired Neural Dynamics [14.66072990853587]
  This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model. An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands. Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands.
  arXiv  Detail & Related papers  (2022-09-03T19:18:54Z)
• Motion Planning and Control for Multi Vehicle Autonomous Racing at High Speeds [100.61456258283245]
  This paper presents a multi-layer motion planning and control architecture for autonomous racing. The proposed solution has been applied on a Dallara AV-21 racecar and tested at oval race tracks achieving lateral accelerations up to 25 $m/s2$.
  arXiv  Detail & Related papers  (2022-07-22T15:16:54Z)
• MotionHint: Self-Supervised Monocular Visual Odometry with Motion Constraints [70.76761166614511]
  We present a novel self-supervised algorithm named MotionHint for monocular visual odometry (VO) Our MotionHint algorithm can be easily applied to existing open-sourced state-of-the-art SSM-VO systems.
  arXiv  Detail & Related papers  (2021-09-14T15:35:08Z)
• Power Control for a URLLC-enabled UAV system incorporated with DNN-Based Channel Estimation [82.16169603954663]
  This letter is concerned with power control for ultra-reliable low-latency communications (URLLC) enabled unmanned aerial vehicle (UAV) system incorporated with deep neural network (DNN) based channel estimation.
  arXiv  Detail & Related papers  (2020-11-14T02:31:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.