Trajectory generation for a UAV in urban terrain, using nonlinear MPC

Receding horizon control of vectored thrust flight experiment

“…In this Section we present the result of commanding a large change in the equilibrium of the system using the cost in (8) and the constraints in (9) and (10). This aggressive command results in highly nonlinear motion of the system.…”

“…Representative examples include the mixed integer linear programming approach of Richards et al [6], the LMI framework for receding horizon control of Bhattacharya et al [7], and the work of Singh et al [8], which provide simulation results for stabilisation of an unmanned aerial vehicle about an open loop trajectory using receding horizon control. Applications to missiles have been considered by Kim et al [9] and others.…”

Online Control Customization via Optimization‐Based Control

“…An overview of the evolution of commercially available MPC technology is given in [27] and a survey of the current state of stability theory of MPC is given in [20]. Closely related to the work in this chapter, Singh and Fuller [29] have used MPC to stabilize a linearized simplified UAV helicopter model around an open-loop trajectory, while respecting state and input constraints.…”

Distributed Particle Filtering over Sensor Networks for Autonomous Navigation of UAVs

“…The motivation for using particle filters is that they can represent almost arbitrary probability distributions, thus becoming well-suited to accommodate the types of uncertainty and nonlinearities that arise in the distributed estimation (Rigatos 2009a), (Rigatos 2009b) (ii) nonlinear control of the UAVs based on the state estimates provided by the particle filtering algorithm. Various approaches have been proposed for the UAV navigation using nonlinear feedback control (Ren & Beard 2004), (Beard et al 2002), (Singh & Fuller 2001). The paper proposes flatness-based control for the UAV models.…”