Nonlinear Model Predictive Control for Multiple UAVs Formation Using Passive Sensing

UAVs in Formation and Dynamic Encirclement Via Model Predictive Control

Switching between the formation flight tactic and the dynamic encirclement tactic for a team of Unmanned Aerial Vehicles (UAVs) is done using a decentralized approach. A team formed from N UAVs, accomplishes a line-of-breast formation then dynamic encirclement around a desired target. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution...

Improved Optimization Process for Nonlinear Model Predictive Control of PMSM

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Nonlinear Model Predictive Guidance for Fixed-wing UAVs Using Identified Control Augmented Dynamics

As off-the-shelf (OTS) autopilots become more widely available and user-friendly and the drone market expands, safer, more efficient, and more complex motion planning and control will become necessary for fixed-wing aerial robotic platforms. Considering typical low-level attitude stabilization available on OTS flight controllers, this paper first develops an approach for modeling and identifica...

Model Predictive Control for a Swarm of Fixed Wing Uavs

This paper describes an algorithm for the control of a swarm of UAVs based on decentralized MPC. For each UAV, our algorithm first determines the trajectory taking into account the obstacles and the constraints on the aircraft performance. Then basing on a robust MPC algorithm, optimal guidance laws are calculated and tracked by the UAVs by means of local PIDs controllers. Our approach also all...

Nonlinear Model Predictive Control