In this paper, we investigate the trajectory tracking control problem for autonomous underwater vehicles (AUVs) with time‐varying external disturbances of currents and waves in the sea. To solve the problem, a fixed‐time extended state observer (FTESO)‐based tracking controller is proposed. Firstly, by using the extended state observer, the FTESO is proposed for estimating the external disturbances and the velocities of the AUV within the fixed convergence time. Based on the FTESO, the fixed‐time controller is proposed for reducing the tracking errors and improving robustness performance. Theoretical analysis is given, which shows that the unknown external disturbances can be compensated and tracking error can converge to zero within the fixed time. Simulation results show that the proposed FTESO controller not only guarantees the fixed‐time convergence but also reduces the trajectory tracking errors of the AUV.
For clustered wireless networks, the conventional dual-hop cooperation technique is inefficient since relays with imbalanced channels to the source and the destination may become the bottleneck of the overall transmission. In this paper, a novel cooperative framework called the three-stage relaying (TSR) scheme is proposed to address this problem. In TSR, relays are divided into two clusters/groups, and a virtual multiple-input-multiple-output (MIMO) antenna array is formed by introducing the transmission between the two relay groups. This "relay-to-relay" communication enables transmissions over shorter distances, which naturally breaks the bottleneck emerging in the dual-hop cooperation scheme. An optimization problem is formulated to deal with the relay selection for these two groups, aiming at maximizing the received signal-to-noise ratio (SNR) at the destination, subject to the number-of-relay constraint. This problem turns out to be nontrivial due to the coupling of the relays between two groups. We tackle this challenging problem by decomposing it into two subproblems: One deals with the selection of transmitting relays, and the other deals with the selection of receiving relays. We then propose two heuristic algorithms that achieve the tradeoff between the optimality of the solution and the computational complexity. Through extensive numerical simulations, we show the superiority of TSR over the dual-hop cooperation schemes in both the clustered and centered wireless networks in terms of symbol error rate and throughput.Index Terms-Clustered networks, cooperative multiple-input multiple-output (MIMO), optimization, spatial diversity.
0018-9545
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