Abstract
The formation flight of quadrotor unmanned aerial vehicles (UAVs) is a complex multi-constraint process. When designing a formation controller, the dynamic model of the UAV itself has modeling errors and uncertainties. Model predictive control (MPC) is one of the best control methods for solving the constrained problem. First, a mathematical model of the quadrotor considering disturbance and uncertainty is established using the Lagrange–Euler formulation and is divided into a rotational subsystem (RS) and a translational subsystem (TS). Here, an improved MPC (IMPC) strategy based on an error model is introduced for the control of UAVs. The tracking errors caused by synthesis disturbance can be eliminated because of the integrator embedded in the augmented model. In addition, by modifying the parameters of the cost function, not only can the degree of stability of the closed-loop subsystem be specified, but also numerical problems in the MPC calculation can be improved. The simulation results demonstrate the stability of the designed controller in formation maintenance and its robustness to external disturbances and uncertainties.
Highlights
The unmanned aerial vehicle (UAV) system is the fastest developing and most practical application in the field of unmanned systems, with its low cost, convenient operation, and flexible characteristics
Since this paper focuses on the improvement of control performance as well as trajectory tracking capability, communication loss is not considered and it is assumed that all states of the UAVs can be shared among each other
∆ = {∆1, ∆2, . . . , ∆n } is the set of quadrotors; ε ⊆ ∆ × ∆ is the edge of the graph, which means the states can be obtained between UAVs; and eij indicates the weight of the edge between ∆i and ∆ j . eij = 1 if ∆i and ∆ j can receive information from each other; otherwise, eij = 0
Summary
The unmanned aerial vehicle (UAV) system is the fastest developing and most practical application in the field of unmanned systems, with its low cost, convenient operation, and flexible characteristics. Kuriki et al designed a formation controller with obstacle avoidance based on distributed MPC and linearized model [9]. Solved the obstacle avoidance problems in UAV formations by combining a particle swarm optimization algorithm and MPC [27]. Dubay et al investigated the problem of the collision avoidance of UAVs in the process of reaching consensus [28] Most of these studies were based on ideal models and the physical constraints of UAVs were not paid enough attention [29], which is not in line with reality. A modified multi-constrained MPC is designed for multiple UAVs to achieve the stability of the formation and trajectory tracking.
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