Formation Learning Control of Multiple Autonomous Underwater Vehicles With Heterogeneous Nonlinear Uncertain Dynamics

Abstract

In this paper, a new concept of formation learning control is introduced to the field of formation control of multiple autonomous underwater vehicles (AUVs), which specifies a joint objective of distributed formation tracking control and learning/identification of nonlinear uncertain AUV dynamics. A novel two-layer distributed formation learning control scheme is proposed, which consists of an upper-layer distributed adaptive observer and a lower-layer decentralized deterministic learning controller. This new formation learning control scheme advances existing techniques in three important ways: 1) the multi-AUV system under consideration has heterogeneous nonlinear uncertain dynamics; 2) the formation learning control protocol can be designed and implemented by each local AUV agent in a fully distributed fashion without using any global information; and 3) in addition to the formation control performance, the distributed control protocol is also capable of accurately identifying the AUVs' heterogeneous nonlinear uncertain dynamics and utilizing experiences to improve formation control performance. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed results.

Cooperative Adaptive Learning Control for a Group of Nonholonomic UGVs by Output Feedback

“…Theorem 1 Consider the closed-loop system consisting of the n vehicles in the MAS described by equation (2) and (11), reference trajectory q r i (t), high-gain observer (13) and (14), adaptive NN controller (25) with the virtual velocity (17), and the online weight updating law (26), under the assumptions 1 and 2, then for any bounded initial condition of all the vehicles andŴ i = 0, the tracking errorq i converges asymptotically to a small neighborhood around zero for all vehicle agents in the MAS.…”

“…Theorem 3 Consider the closed-loop system consisting of equation (2) and (11), reference trajectory q ri ∈ ∪ n j=1 q j (t), high-gain observer (14) and (13), and the experience-based controller (38) with virtual velocity (17). For any bounded initial condition, the tracking errorq i converges asymptotically to a small neighborhood around zero.…”

“…The learned knowledge of the system uncertainties, presented by the RBFNNs, cannot be directly applied on a different control task, and it will need a significant amount of storage space for a large number of different tasks. In recent years, distributed control is a rising topic regarding the control of multiple coordinated agents [15,16,17,18,19,20]. In this chapter, we took the idea of communicating inside the multi-agent system (MAS) and apply it on DL, such that in the learning phase, any vehicle in the MAS is able to learn the unmodeled dynamics not only along its own trajectory, but along the trajectories of all other vehicle agents in this MAS as well.…”

Distributed Adaptive Neural Network Control Applied to a Formation Tracking of a Group of Low-Cost Underwater Drones in Hazardous Environments

“…Its advantages are it is an effective, economic, and efficient solution, compared to a single expensive underwater vehicle. An essential topic in the cooperative control of multi-UUVs is the formation tracking problem [1][2][3]. It requires that all the UUVs reach into a formation with a desired shape from an arbitrary initial pose while the centroid of formation tracks a reference trajectory.…”

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

“…e research of nonlinear control theory has been playing an increasingly important role for MASs [9,25,[30][31][32] and has also become necessary in the design of the consensus control for the multi-UUVs, including the back stepping-based [33], adaptive-based [34,35], slide mode-2 Complexity based [36], and neutral networks-based [37][38][39] consensus control.…”