This paper deals with the problem of trajectory planning and tracking of a quadcopter system based on the property of differential flatness. First, B-spline characterisations of the flat output allow for optimal trajectory generation subject to waypoint constraints, thrust and angle constraints while minimising the trajectory length. Second, the proposed tracking control strategy combines feedback linearisation and nested saturation control via flatness. The control strategy provides bounded inputs (thrust, roll and pitch angles) while ensuring the overall stability of the tracking error dynamics. The control parameters are chosen based on the information of the a priori given reference trajectory. Moreover, conditions for the existence of these parameters are presented. The effectiveness of the trajectory planning and the tracking control design is analysed and validated through simulation and experimental results over a real nano-quadcopter platform, the Crazyflie 2.0.

We propose two different nonlinear model predictive control (NMPC) schemes (with and without terminal cost and constraints) for stabilizing the translational dynamics of thrust-propelled vehicles. Both approaches make use of an elaborated nonlinear feedback linearization controller and its associated ellipsoidal invariant set under restrictive input constraints, hence guaranteeing the closed-loop asymptotic stability. The terminal constraint set of the corresponding NMPC design is easy to tune due to its clear formulation expressed directly in terms of the tuning variables, while for the NMPC scheme without terminal constraint, the design allows to stabilize the system with a significantly shorter prediction horizon in comparison with the existing method in the literature. Simulation and experimental tests over a nano-drone platform validate the proposed approaches.

This paper proposes a novel methodology for path generation in known and congested multi-obstacle environments. Our aim is to solve an open problem in navigation within such environments: the feasible space partitioning in accordance with the distribution of obstacles. It is shown that such a partitioning is a key concept towards the generation of a corridor in cluttered environments. Once a corridor between an initial and a final position is generated, the selection of a path is considerably simplified in comparison with the methods which explore the original non-convex feasible regions of the environment. The core of the methodology presented here is the construction of a convex lifting which boils down to a convex optimization. The paper covers both the mathematical foundations and the computational details of the implementation and aims to illustrate the concepts with geometrical examples.

This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles).