Multiple AUVs Research Articles

Distributed Adaptive Integral Sliding Mode Affine Formation Maneuver Control for Multiple AUVs With Event-Triggered Chattering- Reduction Mechanism

Distributed Adaptive Integral Sliding Mode Affine Formation Maneuver Control for Multiple AUVs With Event-Triggered Chattering- Reduction Mechanism

Finite-time error constraint control for multi-AUV systems with unknown uncertainties based on observer and tan-type barrier Lyapunov function

PurposeAutonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.Design/methodology/approachThe tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.FindingsThe containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.Originality/valueThe state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

Control of Multiple AUV Systems With Input Saturations Using Distributed Fixed-Time Consensus Fuzzy Control

Control of Multiple AUV Systems With Input Saturations Using Distributed Fixed-Time Consensus Fuzzy Control

Development of Formation Control System for Multiple AUVs with Sonar Interference Avoidance Function

Development of Formation Control System for Multiple AUVs with Sonar Interference Avoidance Function

3D Localization for Multiple AUVs in Anchor-Free Environments by Exploring the Use of Depth Information

3D Localization for Multiple AUVs in Anchor-Free Environments by Exploring the Use of Depth Information

Hybrid-Algorithm-Based Full Coverage Search Approach With Multiple AUVs to Unknown Environments in Internet of Underwater Things

Hybrid-Algorithm-Based Full Coverage Search Approach With Multiple AUVs to Unknown Environments in Internet of Underwater Things

Hybrid TOA-AOA Cooperative Localization for Multiple AUVs in the Absence of Anchors

Hybrid TOA-AOA Cooperative Localization for Multiple AUVs in the Absence of Anchors

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Dynamic task allocation in multi autonomous underwater vehicle confrontational games with multi-objective evaluation model and particle swarm optimization algorithm

In this paper, a non-zero-sum game model based on a multi-objective evaluation model was adopted to solve the dynamic task allocation problem of underwater multiple Autonomous Underwater Vehicle (multi-AUV). By introducing a functional coordination mechanism, the high degree of coordination in practical confrontation scenarios was reflected, and the autonomous decision-making and task allocation process of multiple AUVs were clarified. Secondly, a multi-objective evaluation model including survival value, strike income, and ammunition loss was constructed, and weighted processing was performed on the multiple objectives using the Analytic Hierarchy Process (AHP) method, obtaining the benefits of each non-zero-sum strategy in dynamic games. In addition, a particle swarm optimization (PSO) algorithm that combines the theory of good point sets theory (G) and speed control factors (S) for improvement, called GSPSO, was used to find the optimal strategy in the game and allocate tasks. Finally, simulation analysis showed that the collaborative system with functional coordination mechanisms significantly improved the combat capabilities of underwater multiple AUVs. The multi-objective evaluation model combined with AHP can correctly and comprehensively evaluate the advantages and disadvantages of each strategy and correctly respond to changing confrontation tasks and decision preferences. The proposed algorithm improved the convergence speed and global search ability by enhancing the diversity of the population and the effectiveness of iterative solutions, ensuring real-time decision-making and task allocation in complex high-dimensional dynamic games.

Distributed Two-Layered Leader–Follower Affine Formation Control for Multiple AUVs in 3-D Space

Distributed Two-Layered Leader–Follower Affine Formation Control for Multiple AUVs in 3-D Space

An improved particle swarm optimization based on age factor for multi-AUV cooperative planning

Multi-AUV planning of cooperative path represents the intelligence level of AUV formation system, which has attracted significant attention due to its flexibility and robustness. To enhance the speed and exploration capability of the multi-AUV autonomous path planner, this paper proposes an Age-based Multi-population Particle Swarm Optimization (AMPSO) for three-dimensional path planning of multiple AUVs. Firstly, reverse chaotic mapping is employed to enhance the initial distribution, enabling them to better adapt to the optimization process. In addition, the introduction of multiple populations helps to enhance the diversity within the population, thereby accelerating the convergence rate. Taking into account the search ability, the concept of an "age" coefficient is proposed to optimize the velocity updating formula to reduce the effect of inferior particles. Furthermore, a velocity-independent updating mode is suggested to enhance the population diversity by selecting different update modes. To evaluate the performance of the proposed algorithm, Monte Carlo simulations are conducted for AUV formations across two tracking tasks within the underwater environment established in this paper. The results demonstrate that the proposed algorithm outperforms other commonly used algorithms in terms of both speed and optimality, so the proposed AMPSO can be used for multi-AUV cooperative planning.

Discrete-time AUV formation control with leader-following consensus under time-varying delays

In order to enhance the development of marine resources, this study investigates the problem of discrete-time multi-autonomous underwater vehicles (AUV) formation coordination control with leader-following consensus under time-varying delays. First, a discrete-time second-order integration model for AUVs is presented. Second, considering the existence of time-varying delays in AUV communication, the communication between multiple AUVs is classified as valid or invalid based on the maximum delay limit. The problem of AUV formation control under invalid communication is transformed into a formation control problem under switching communication topologies. Then, a time-varying delay communication synchronization strategy is designed to convert asynchronous state information into synchronous state information. Based on synchronous state information, a leader-following consensus control protocol with time-varying delays is proposed, and a controllability analysis is conducted using Lyapunov–Krasovskii functionals. Finally, the stability and effectiveness of the proposed formation-coordination controller and delay synchronization strategy were validated through simulation experiments.

Global path planning for multiple AUVs using GWO

Global path planning for multiple AUVs using GWO

Adaptive finite-time leader-follower formation control for multiple AUVs regarding uncertain dynamics and disturbances

Adaptive finite-time leader-follower formation control for multiple AUVs regarding uncertain dynamics and disturbances

Cooperative Navigation Algorithm of Extended Kalman Filter Based on Combined Observation for AUVs

The navigation and positioning of multi-autonomous underwater vehicles (AUVs) in the complex and variable marine environment is a significant and much-needed area of attention, especially considering the fact that cooperative navigation technology is the essential method for multiple AUVs to solve positioning problems. When the extended Kalman filter (EKF) is applied for underwater cooperative localization, the outliers in the sensor observations cause unknown errors in the measurement system due to deep-sea environmental factors, which are difficult to calibrate and cause a significant reduction in the co-location accuracy of AUVs, and can even cause problems with a divergence of estimation error. In this paper, we proposed a cooperative navigation method of the EKF algorithm based on the combined observation of multiple AUVs. Firstly, the corresponding cooperative navigation model is established, and the corresponding measurement model is designed. Then, the EKF model based on combined observation is designed and constructed, and the unknown error is eliminated by introducing a previously measured value. Finally, simulation tests and lake experiments are designed to verify the effectiveness of the algorithm. The results indicate that the EKF algorithm based on combined observation can approximately eliminate errors and improve the accuracy of cooperative localization when the unknown measurement error cannot be calibrated by common EKF methods. The effect of state estimation is improved, and the accuracy of co-location can be effectively improved to avoid serious declines in—and divergence of—estimation accuracy.

Fault-Tolerant Formation Control for Multiple Stochastic AUV System under Markovian Switching Topologies

Formation control, which is a core problem in multi-autonomous underwater vehicle (AUV) systems, plays an important role in realizing safe and accurate cooperation of multi-AUV systems. This paper provides a study on fault-tolerant formation control for multiple stochastic AUV systems under Markovian switching topologies. Considering the effect of noise and Markovian switching communication topology, a novel leader-following group formation control protocol with an actuator fault for a multi-AUV system is developed under the influence of the two independent stochastic processes. The designed controller is proved by an infinitesimal generator with Lyapunov stability theory and can guarantee that the follower AUVs’ states will eventually converge to the leader’s state in each subgroup while forming the desired sub-formation. Finally, the effectiveness of the theoretical analysis is verified by a simulation experiment.

Finite-time extended state observer based collision-free leaderless formation control of multiple AUVs via event-triggered control

Finite-time extended state observer based collision-free leaderless formation control of multiple AUVs via event-triggered control

Discrete-time event-based coordinated formation control of multiple AUVs with time-varying delay and alterable topology

This article considers the issue of event-based coordinated formation control for multiple autonomous underwater vehicles subject to time-varying communication delay and alterable topology under discretized time. First, to derive the dynamics model in the discrete-time domain, the state feedback technique and the forward difference approach are employed for the continuous-time nonlinear model of autonomous underwater vehicle. For each follower, the event-triggering function and the coordinated controller are designed, using only discrete-time state information of the leader or other followers. Under this control strategy, the controller will not be updated at each discrete-time instant. Then, the coordinated control problem is turned into the asymptotic stability problem of the multi-autonomous underwater vehicle system. According to the stability analysis, sufficient conditions are presented for successfully completing the formation assignment without and with time-varying delay, respectively. Finally, by performing some numerical simulation experiments, the efficacity of our proposed event-based controller and the correctness of the main theoretical results are demonstrated. The impact of different communication conditions on the control system is revealed by the comparison of several scenarios.

An adaptive event-driven fault-tolerant consensus proposed for decentralized multiple AUVs with actuator multiplicative faults

The present paper studies a distributed leaderless multi-autonomous underwater vehicle (AUV) system under a heterogeneous topology network where multiplicative faults and input saturation affect actuators, and the actuator fault severity could not be equal in each AUV. For this system, an adaptive event-driven fault-tolerant consensus control protocol is proposed. The present paper also introduces adaptive parameters in the trigger function to improve the self-regulation capability of the event-driven mechanism system. The proposed fully distributed event-driven fault-tolerant consensus strategy depends on the adaptive coupling gain of the system, and it eliminates the global information restriction. It was found that the designed event-driven controller has a good fault tolerance effect on multiplicative faults of actuators. The states of the multi-AUV system could asymptotically converge to the same conditions without Zeno behavior. Finally, the Lyapunov functional method proves the system's astringency and stability, and the numerical simulation presents the strategy's effectiveness.

Adaptive fuzzy sliding-mode consensus control of nonlinear under-actuated agents in a near-optimal reinforcement learning framework

This study presents a new framework for merging the Adaptive Fuzzy Sliding-Mode Control (AFSMC) with an off-policy Reinforcement Learning (RL) algorithm to control nonlinear under-actuated agents. In particular, a near-optimal leader-follower consensus is considered, and a new method is proposed using the framework of graphical games. In the proposed technique, the sliding variables’ coefficients are considered adaptively tuned policies to achieve an optimal compromise between the satisfactory tracking performance and the allowable control efforts. Contrary to the conventional off-policy RL algorithms for consensus control of multi-agent systems, the proposed method does not require partial knowledge of the system dynamics to initialize the RL process. Furthermore, an actor-critic fuzzy methodology is employed to approximate optimal policies using the measured input/output data. Therefore, using the tuned sliding vector, the control input for each agent is generated which includes a fuzzy term, a robust term, and a saturation compensating term. In particular, the fuzzy system approximates a nonlinear function, and the robust part of the input compensates for any possible mismatches. Furthermore, the saturation compensating gain prevents instability due to any possible actuator saturation. Based on the local sliding variables, the fuzzy singletons, the bounds of the approximation errors, and the compensating gains are adaptively tuned. Closed-loop asymptotic stability is proved using the second Lyapunov theorem and Barbalat's lemma. The method's efficacy is verified by consensus control of multiple REMUS AUVs in the vertical plane.