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This article addresses the trajectory consensus problem of coordinating the trajectories of vehicles at multiple future time points. The objective is the consensus of the geometry of the vehicles' planned trajectories. The geometric feature of trajectories is parameterized by a set of trajectory states defined as required lengths along the trajectory to reduce the distance to its ending point to specific values. To solve this special consensus problem involving coupled state variables, the conventional consensus model is extended by attaching it to a mapping from the state variables to the trajectory's geometry. This mapping is established using a homotopic structure that creates a compact and efficient form for the mapping. The geometry of the homotopic structure is based on the shapes of its envelopes, and the elements in the structure are derived from their deformation. Through a homotopic search in the structure, an asymptotic consensus of trajectory states is achieved. Simulation results show the proposed coupled state consensus method can achieve better performance on the consensus of multiple vehicles than the conventional isolated state consensus method.

In this paper, a novel adaptive sliding mode control (ASMC) strategy is presented for attitude and altitude stabilization of a quadrotor unmanned aerial vehicle (UAV), where the input saturation is taken into consideration. The nonlinear dynamic model of quadrotor UAV subject to system uncertainty and external disturbance is firstly derived using the Euler-Lagrange mechanical equation. To ensure system stability, as well as eliminating the negative effects of saturation features, a saturated adaptive sliding mode control law (ASMCSAT) is proposed, which takes advantages of the sliding mode control and the adaptive control. Compared with conventional control strategies, better tracking performance and disturbance and uncertainty rejection can be achieved with the root mean square of the tracking error (RMSE) being reduced by 34.91% in vertical take-off and 30.11% in trajectory tracking. Simulations and experiments are provided to verify the effectiveness and practicability of the proposed control method.

This brief proposes the first model reference adaptive control (MRAC) method for aortic pressure (AoP) regulation and maintaining the heart’s physiological aerobic metabolism in ex vivo heart perfusion (EVHP). A mathematical model of the EVHP was established to describe the hemodynamic behavior of EVHP and quantify the changes in cardiac parameters. A reference model consisting of a virtual proportional–integral–derivative (PID) controller and the EVHP model was employed to generate the reference trajectory of AoP. An adaptation algorithm tunes the control parameters based on the reference model and the isolated heart. Experiments were conducted using large animal hearts (50 ± 5 kg porcine) to validate the adaptive controller’s performance for stepwise and fast switching AoP references. The results confirmed the effectiveness of the proposed controller for regulating the AoP in isolated porcine hearts, in an accurate (mean error less than 2 mmHg) and fast (4–8 s of settling time) manner.

In this paper, the traditional shortest path planning problem for vehicle is advanced to length-targeted path planning problem, i.e., to plan path with its length being as close to a specified value as possible. Lengthening and shortening of given initial paths are used to solve this problem. Based on an operation set consisting of three basic path homotopies, we build a comprehensive and systematic framework to plan paths with target length, which is a generalization of the existing related studies. Thereby, the expected paths can be independently searched through such deformation processes within topological classes. The proposed framework can produce the largest length coverage in different scenarios and under different conditions, and it can also generate expected paths with arbitrary topological classification in terms of the curvature constraint and the obstacle constraint. Examples show that our lengthening and shortening method can effectively solve the length-targeted path planning problem in environment without or with obstacles. Note to Practitioners —This paper presents a curvature-bounded lengthening and shortening approach for path planning of vehicles. The curvature and obstacle constraints in the path planning problem are handled using the topological technique. In addition, a rigorous classification system is presented to systematically categorize the potential paths. Within a path class, any path can be deformed to other paths while keeping the curvature bounded. Then, the path deformation process is conducted via a series of basic homotopy operations to approach the target length. The length coverage range of this process is reduced when some endpoint conditions are meet or the vehicle is trapped into a closed region.

A triple-stage path prediction algorithm is proposed for real-time mission planning. In every planning horizon, each unmanned aerial vehicle (UAV) utilises the A* algorithm first to estimate the path to every task. Then, the estimated result serve as the input of the cluster method to generate the quasi-optimal task assignment. The shortest path to the assigned task is further calculated using the A* algorithm. Finally, it is smoothed to obtain the flyable reference path to guide the UAV by using the cubic B-spline curve. Numerical experiments demonstrate the effectiveness and efficiency of the proposed algorithm.