Communication-Aware Formation Control of AUVs With Model Uncertainty and Fading Channel via Integral Reinforcement Learning

Most formation approaches of autonomous underwater vehicles(AUVs)focus on the control techniques,ignoring the influence of underwater channel.This paper is concerned with a communication-aware formation issue for AUVs,subject to model uncertainty and fading channel.An integral reinforcement learning(IRL)based estimator is designed to calculate the probabilistic channel parameters,wherein the multivariate probabilistic collocation method with orthogonal fractional factorial design(M-PCM-OFFD)is employed to evaluate the uncertain channel measurements.With the estimated signal-to-noise ratio(SNR),we employ the IRL and M-PCM-OFFD to develop a saturated formation controller for AUVs,dealing with uncertain dynamics and current parameters.For the proposed formation approach,an integrated optimization solution is presented to make a balance between formation stability and communication efficiency.Main innovations lie in three aspects:1)Construct an integrated communication and control optimization framework;2)Design an IRL-based channel prediction estimator;3)Develop an IRL-based formation controller with M-PCM-OFFD.Finally,simulation results show that the formation approach can avoid local optimum estimation,improve the channel efficiency,and relax the dependence of AUV model parameters.

Model-Free Formation Control of Autonomous Underwater Vehicles:A Broad Learning-Based Solution

Dear Editor,We develop a broad learning-based algorithm to enforce the formation control of AUVs.Compared with the deep learning(DL)based formation solutions,our solution employs the broad learning system(BLS)to remodel the learning framework without a retraining process.

Communication-Aware Mobile Relaying via an AUV for Minimal Wait Time:A Broad Learning-Based Solution

Dear Editor,This letter studies the communication-aware mobile relaying via an autonomous underwater vehicle(AUV)for minimal wait time.Compared with the analysis-based channel prediction solution,the proposed discrete Kirchhoff approximation solution has a higher estimation accuracy.

Position Measurement Based Slave Torque Feedback Control for Teleoperation Systems With Time-Varying Communication Delays

Bilateral teleoperation system is referred to as a promising technology to extend human actions and intelligence to manipulating objects remotely.For the tracking control of teleoperation systems,velocity measurements are necessary to provide feedback information.However,due to hardware technology and cost constraints,the velocity measurements are not always available.In addition,the time-varying communication delay makes it challenging to achieve tracking task.This paper provides a solution to the issue of real-time tracking for teleoperation systems,subjected to unavailable velocity signals and time-varying communication delays.In order to estimate the velocity information,immersion and invariance(I&I)technique is employed to develop an exponential stability velocity observer.For the proposed velocity observer,a linear relationship between position and observation state is constructed,through which the need of solving partial differential and certain integral equations can be avoided.Meanwhile,the mean value theorem is exploited to separate the observation error terms,and hence,all functions in our observer can be analytically expressed.With the estimated velocity information,a slave-torque feedback control law is presented.A novel Lyapunov-Krasovskii functional is constructed to establish asymptotic tracking conditions.In particular,the relationship between the controller design parameters and the allowable maximum delay values is provided.Finally,simulation and experimental results reveal that the proposed velocity observer and controller can guarantee that the observation errors and tracking error converge to zero.