This article studies distributed pose (orientation and position) estimation of leader–follower multi-agent systems over κ-layer graphs in 2-D plane. Only the leaders have access to their orientations and positions, while the followers can measure the relative bearings or (angular and linear) velocities in their unknown local coordinate frames. For the orientation estimation, the local relative bearings are used to obtain the relative orientations among the agents, based on which a distributed orientation estimation algorithm is proposed for each follower to estimate its orientation. For the position estimation, the local relative bearings are used to obtain the position constraints among the agents, and a distributed position estimation algorithm is proposed for each follower to estimate its position by solving its position constraints. Both the orientation and position estimation errors converge to zero asymptotically. A simulation example is given to verify the theoretical results.

This paper proposes a novel tube model predictive control approach for dynamic positioning of autonomous underwater vehicles with state and input constraints. The cost function is selected as a mixture of weighted 1-norms and ∞-norms of states and inputs, which can be reconstructed into linear summation form. Specially, a method is presented to calculate the weighted matrix of the terminal cost so that closed-loop stability is achieved. The proposed control problem can be solved by linear programming, which provides high computational efficiency. Experimental results on “UVIC-I” AUV are conducted to validate the viability and validity of the presented method.