考虑信息成功传递概率的多无人机协同目标最优观测与跟踪

针对考虑通信因素的多无人机协同目标最优观测与跟踪问题, 引入费舍信息矩阵对无人机探测所获取的信息进行表征, 考虑无线通信链路特性并对无人机间信息成功传递概率进行建模. 以无人机群体所获取的关于目标的信息量为指标函数, 分别建立是否考虑通信因素情况下的多机协同目标最优观测及跟踪问题模型. 对两种情况下的多机协同目标观测与跟踪进行仿真比较, 仿真结果验证了所建模型的有效性, 并体现了通信因素的重要影响.

     

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.