4.7 Article

Data-driven distributed formation control of under-actuated unmanned surface vehicles with collision avoidance via model-based deep reinforcement learning

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Safety-Critical Containment Maneuvering of Underactuated Autonomous Surface Vehicles Based on Neurodynamic Optimization With Control Barrier Functions

Nan Gu et al.

Summary: This article discusses the safety-critical containment maneuvering problem for multiple underactuated autonomous surface vehicles (ASVs) in complex marine environments. A fixed-time extended state observer is employed to estimate model uncertainties and external disturbances, and input-to-state safe control barrier functions are constructed to achieve collision-free containment formation. A neural network-based neurodynamic optimization approach is used to solve the quadratic optimization problem, ensuring the safety and effectiveness of the multi-ASVs system.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023)

Article Engineering, Marine

Safe deep reinforcement learning-based adaptive control for USV interception mission

Bin Du et al.

Summary: This paper presents a safe learning scheme for the USV interception mission using a safe Lyapunov boundary deep deterministic policy gradient algorithm. By applying a single neuron proportional adaptive control for pre-training, the proposed method quickly converges to feasible solutions subject to safety constraints and demonstrates high performance in stability and safety.

OCEAN ENGINEERING (2022)

Letter Computer Science, Information Systems

Adaptive synchronization control of uncertain multiple USVs with prescribed performance and preserved connectivity

Shude He et al.

SCIENCE CHINA-INFORMATION SCIENCES (2022)

Review Engineering, Marine

Safe-critical formation reconfiguration of multiple unmanned surface vehicles subject to static and dynamic obstacles based on guiding vector fields and fixed-time control barrier functions

Xiaoxuan Gong et al.

Summary: This paper addresses the problem of formation reconfiguration of multiple unmanned surface vehicles in the presence of static and dynamic obstacles. The proposed method utilizes guiding vector fields and fixed-time control barrier functions to ensure safety while reaching the goals.

OCEAN ENGINEERING (2022)

Article Engineering, Civil

Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation

Guibing Zhu et al.

Summary: This study investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances, considering undesirable faults and input saturation of actuators. A novel control scheme is proposed using a saturation function, event-triggered mechanism, and neural network technique, which is robust, adaptive, tolerant, and guarantees stable tracking of MSVs without prior knowledge of dynamics or faults. Simulation results demonstrate the effectiveness of the proposed scheme.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Observer-Based Neuro-Adaptive Optimized Control of Strict-Feedback Nonlinear Systems With State Constraints

Yongming Li et al.

Summary: This article presents an adaptive neural network output feedback optimized control design for strict-feedback nonlinear systems with unknown internal dynamics. By constructing optimal cost functions for subsystems and using the actor-critic architecture, virtual and actual optimal controllers are developed to ensure the boundedness of all closed-loop signals. The proposed strategy also guarantees that system states are always confined within some preselected compact sets.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

Article Automation & Control Systems

Event-Triggered Cooperative Path Following of Autonomous Surface Vehicles Over Wireless Network With Experiment Results

Mingao Lv et al.

Summary: This article presents a solution for cooperative path-following problem of autonomous surface vehicles (ASVs) over a wireless network with constrained resources. A network-based resource-aware control architecture is proposed to achieve cooperative path following with low network traffic and reduced computation. Experimental results verify the effectiveness of the proposed network-based resource-aware control architecture for multiple ASVs.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

Article Automation & Control Systems

Fuzzy Adaptive Optimized Leader-Following Formation Control for Second-Order Stochastic Multiagent Systems

Yongming Li et al.

Summary: This article studies the adaptive optimized formation control problem for second-order stochastic multiagent systems with unknown nonlinear dynamics. Fuzzy logic systems are used to approximate the nonlinear functions in the control design. By employing the actor-critic architecture and Lyapunov stability theory, the proposed optimal formation control strategy ensures that all the error signals are bounded in probability. Simulation examples verify the effectiveness of the proposed formation control approach.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

Article Engineering, Civil

Path Following Optimization for an Underactuated USV Using Smoothly-Convergent Deep Reinforcement Learning

Yujiao Zhao et al.

Summary: This paper introduces a deep reinforcement learning method to solve the path following problem for an underactuated unmanned-surface-vessel, showing promising performance in numerical simulations by reducing the complexity of control laws while achieving path following effectively.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2021)

Article Automation & Control Systems

An Overview of Recent Advances in Coordinated Control of Multiple Autonomous Surface Vehicles

Zhouhua Peng et al.

Summary: Autonomous surface vehicles (ASVs) are marine vessels capable of operating without a crew in various water/ocean environments, and coordinating multiple ASVs for complex missions offers enhanced capability and efficacy. Challenges in coordinated control of ASVs include their diversity, intravehicle interactions, collision avoidance requirements, and limited communication bandwidth in sea environments.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Engineering, Marine

Extended-state-observer-based distributed model predictive formation control of under-actuated unmanned surface vehicles with collision avoidance

Guanghao Lv et al.

Summary: A distributed formation tracking control method for under-actuated unmanned surface vehicles is proposed in this paper, utilizing an extended-state-observer-based model predictive control approach. The vehicles' dynamics are transformed and uncertainties are estimated to design position and angular motion controllers, solving the problem as a constrained quadratic programming. Simulation results validate the effectiveness of the proposed method for multiple vehicles.

OCEAN ENGINEERING (2021)

Article Engineering, Marine

A collision avoidance approach via negotiation protocol for a swarm of USVs

Yong Ma et al.

Summary: The paper proposes a collision avoidance approach using a negotiation protocol for USVs under complex situations, which is proven to be effective and reliable through simulations in various scenarios.

OCEAN ENGINEERING (2021)

Article Engineering, Marine

Formation Control and Obstacle Avoidance Algorithm of a Multi-USV System Based on Virtual Structure and Artificial Potential Field

Xun Yan et al.

Summary: This paper presents a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs) that combines a virtual structure and artificial potential field to achieve high accuracy in formation shape maintenance and flexibility in formation shape change. The improved dynamic window approach is utilized to address obstacle avoidance for the multi-USV system, ensuring that the USV formation can navigate around obstacles while maintaining its shape. The combination of virtual structure and artificial potential field results in fewer calculations, allowing for real-time performance and ease of deployment on actual USVs.

JOURNAL OF MARINE SCIENCE AND ENGINEERING (2021)

Article Computer Science, Artificial Intelligence

Data-Driven Adaptive Disturbance Observers for Model-Free Trajectory Tracking Control of Maritime Autonomous Surface Ships

Zhouhua Peng et al.

Summary: This article introduces reduced- and full-order data-driven adaptive disturbance observers (DADOs) for estimating unknown input gains and total disturbance of maritime autonomous surface ships. The proposed DADOs offer simultaneous estimation of total disturbance and input gains with guaranteed convergence through data-driven adaption. Simulation results validate the efficacy of the proposed DADO approach for model-free trajectory tracking control of autonomous surface ships without prior knowledge of their dynamics.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

USV Formation and Path-Following Control via Deep Reinforcement Learning With Random Braking

Yujiao Zhao et al.

Summary: This study addresses the problem of path following for underactuated unmanned surface vessels formation using a modified deep reinforcement learning with random braking approach. A formation control model based on deep reinforcement learning is developed, along with a novel random braking mechanism to prevent training from getting stuck in local optima. A virtual leader-based path-following system is proposed to automatically adjust formation and maintain flexibility even when some vessels deviate, with simulation results verifying the effectiveness and superiority of the control strategy.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Engineering, Ocean

Adaptive and extendable control of unmanned surface vehicle formations using distributed deep reinforcement learning

Shuwu Wang et al.

Summary: Future ocean exploration will be dominated by a large-scale deployment of marine robots such as unmanned surface vehicles (USVs), which can exploit oceans in an unprecedented way with increased mission efficiency. AI technologies like reinforcement learning can equip USVs with high-level intelligence for fully autonomous operation. The trend of USV operations in the future is to use them as a formation fleet, with potential impact from adopting advanced AI technologies in formation control.

APPLIED OCEAN RESEARCH (2021)

Article Engineering, Marine

Distributed Consensus of USVs under Heterogeneous UAV-USV Multi-Agent Systems Cooperative Control Scheme

Kai Xue et al.

Summary: This paper discusses the formation motion control of heterogeneous multi-agent unmanned systems using a distributed consensus approach, considering unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A fuzzy-based sliding mode control approach is proposed to ensure finite-time formation assembly, with finite-time stability proven by Lyapunov stability theorem. A novel vision-based path re-planning approach is introduced to highlight cooperation within heterogeneous systems like UAVs and USVs.

JOURNAL OF MARINE SCIENCE AND ENGINEERING (2021)

Article Engineering, Multidisciplinary

Reinforcement Learning-Based Autonomous Navigation and Obstacle Avoidance for USVs under Partially Observable Conditions

Nan Yan et al.

Summary: This paper proposes a UANOA method based on deep reinforcement learning, which achieves the autonomous navigation and obstacle avoidance tasks of USVs, and shows good performance in complex ocean environments.

MATHEMATICAL PROBLEMS IN ENGINEERING (2021)

Article Computer Science, Artificial Intelligence

Neural-Network-Based Event-Triggered Adaptive Control of Nonaffine Nonlinear Multiagent Systems With Dynamic Uncertainties

Hongjing Liang et al.

Summary: This article addresses the adaptive event-triggered neural control problem for nonaffine pure-feedback nonlinear multiagent systems with dynamic disturbance, unmodeled dynamics, and dead-zone input. The use of radial basis function neural networks to approximate unknown nonlinear functions and a dynamic signal to handle design difficulties in unmodeled dynamics were highlighted. A novel event-triggered control protocol was proposed to reduce communication burden and achieve convergence of follower outputs to a neighborhood of the leader's output, while ensuring bounded signals in the closed-loop system. An illustrative simulation example was provided to verify the efficacy of the proposed algorithms.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Computer Science, Artificial Intelligence

Event-Triggered Fuzzy Bipartite Tracking Control for Network Systems Based on Distributed Reduced-Order Observers

Hongjing Liang et al.

Summary: This article addresses the issue of distributed observer-based event-triggered bipartite tracking control for stochastic nonlinear multiagent systems, proposing a novel approach with reduced-order observer and event-triggered mechanism. The designed bipartite tracking controller utilizes fuzzy logic systems and the backstepping approach, with theoretical proofs for its effectiveness. A simulation example is provided to demonstrate the effectiveness of the proposed scheme.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2021)

Article Automation & Control Systems

Observer-Based Finite-Time Control for Distributed Path Maneuvering of Underactuated Unmanned Surface Vehicles With Collision Avoidance and Connectivity Preservation

Nan Gu et al.

Summary: This article addresses the distributed path maneuvering of underactuated unmanned surface vehicles (USVs) with collision avoidance and connectivity preservation. It proposes an observer-based finite-time control method incorporating artificial potential field for collision avoidance and connectivity preservation, along with antidisturbance kinetic control laws. The effectiveness of the proposed method is verified through simulation results for multiple USVs with position-yaw measurements.

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS (2021)

Article Computer Science, Information Systems

A Survey of Autonomous Underwater Vehicle Formation: Performance, Formation Control, and Communication Capability

Yue Yang et al.

Summary: Autonomous underwater vehicles (AUVs) are submersible underwater vehicles controlled by onboard computers. AUV formation offers higher efficiency and stability for various applications. Key factors include AUV performance, formation control, and communication capability. Current research focuses on formation control methods, while communication capability and AUV performance research is in its early stages.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2021)

Article Automation & Control Systems

Distributed Formation Control Using Artificial Potentials and Neural Network for Constrained Multiagent Systems

Ya Liu et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2020)

Article Automation & Control Systems

Line-of-Sight Target Enclosing of an Underactuated Autonomous Surface Vehicle With Experiment Results

Yue Jiang et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Engineering, Marine

Collision avoidance for an unmanned surface vehicle using deep reinforcement learning

Joohyun Woo et al.

OCEAN ENGINEERING (2020)

Article Computer Science, Information Systems

Data driven hybrid edge computing-based hierarchical task guidance for efficient maritime escorting with multiple unmanned surface vehicles

Jiajia Xie et al.

PEER-TO-PEER NETWORKING AND APPLICATIONS (2020)

Article Engineering, Civil

Redefined Output Model-Free Adaptive Control Method and Unmanned Surface Vehicle Heading Control

Yulei Liao et al.

IEEE JOURNAL OF OCEANIC ENGINEERING (2020)

Article Automation & Control Systems

Adaptive Leader-Follower Formation Control of Nonholonomic Mobile Robots With Prescribed Transient and Steady-State Performance

Shi-Lu Dai et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Computer Science, Artificial Intelligence

Finite-Time Adaptive Fuzzy Control for Nonstrict-Feedback Nonlinear Systems Via an Event-Triggered Strategy

Anqing Wang et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2020)

Article Automation & Control Systems

Formation control of mobile robot systems incorporating primal-dual neural network and distributed predictive approach

Dongdong Qin et al.

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS (2020)

Article Computer Science, Artificial Intelligence

Event-Triggered Robust Adaptive Fuzzy Control for a Class of Nonlinear Systems

Anqing Wang et al.

IEEE TRANSACTIONS ON FUZZY SYSTEMS (2019)

Article Automation & Control Systems

A Novel Cooperative Platform Design for Coupled USV-UAV Systems

Guangming Shao et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)

Article Automation & Control Systems

Path-Following Control of Autonomous Underwater Vehicles Subject to Velocity and Input Constraints via Neurodynamic Optimization

Zhouhua Peng et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Automation & Control Systems

Fuzzy Categorical Deep Reinforcement Learning of a Defensive Game for an Unmanned Surface Vessel

Yin Cheng et al.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2019)

Article Engineering, Electrical & Electronic

Error-Driven-Based Nonlinear Feedback Recursive Design for Adaptive NN Trajectory Tracking Control of Surface Ships With Input Saturation

Yong Ma et al.

IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE (2019)

Article Computer Science, Information Systems

Learn to Navigate: Cooperative Path Planning for Unmanned Surface Vehicles Using Deep Reinforcement Learning

Xinyuan Zhou et al.

IEEE ACCESS (2019)

Article Automation & Control Systems

The Distributed Adaptive Finite-Time Chattering Reduction Containment Control for Multiple Ocean Bottom Flying Nodes

Hongde Qin et al.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2019)

Article Automation & Control Systems

Adaptive Trajectory Tracking Control of a Fully Actuated Surface Vessel With Asymmetrically Constrained Input and Output

Zewei Zheng et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2018)

Article Automation & Control Systems

Survey on Fuzzy-Logic-Based Guidance and Control of Marine Surface Vehicles and Underwater Vehicles

Xianbo Xiang et al.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2018)

Article Automation & Control Systems

Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation

Zhouhua Peng et al.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2018)

Article Automation & Control Systems

Multi-objective path planning for unmanned surface vehicle with currents effects

Yong Ma et al.

ISA TRANSACTIONS (2018)

Article Automation & Control Systems

Finite-Time Formation Control of Under-Actuated Ships Using Nonlinear Sliding Mode Control

Tieshan Li et al.

IEEE TRANSACTIONS ON CYBERNETICS (2018)

Article Automation & Control Systems

Network-Based T-S Fuzzy Dynamic Positioning Controller Design for Unmanned Marine Vehicles

Yu-Long Wang et al.

IEEE TRANSACTIONS ON CYBERNETICS (2018)

Article Chemistry, Multidisciplinary

Fast nonlinear model predictive control of a chemical reactor: a random shooting approach

Peter Bakarac et al.

ACTA CHIMICA SLOVACA (2018)

Article Automation & Control Systems

Advanced Control in Marine Mechatronic Systems: A Survey

Yang Shi et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2017)

Article Automation & Control Systems

Containment Maneuvering of Marine Surface Vehicles With Multiple Parameterized Paths via Spatial-Temporal Decoupling

Zhouhua Peng et al.

IEEE-ASME TRANSACTIONS ON MECHATRONICS (2017)

Article Computer Science, Artificial Intelligence

Predictor-Based Neural Dynamic Surface Control for Uncertain Nonlinear Systems in Strict-Feedback Form

Zhouhua Peng et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2017)

Article Computer Science, Artificial Intelligence

Path following control for marine surface vessel with uncertainties and input saturation

Zewei Zheng et al.

NEUROCOMPUTING (2016)

Article Engineering, Civil

Model Predictive Control for Tracking of Underactuated Vessels Based on Recurrent Neural Networks

Zheng Yan et al.

IEEE JOURNAL OF OCEANIC ENGINEERING (2012)