Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment

Hafez, Ahmed T.; Givigi, Sidney N.

doi:10.1109/syscon.2016.7490605

Cited by 13 publications

(3 citation statements)

References 26 publications

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“…A flocking algorithm in the presence of arbitrary shape obstacle avoidance is presented in [23] both in the 2D space and 3D space. In [27], learning-based model predictive control approach is applied to the flocking problem of UAVs in the presence of uncertainties and obstacles. A distributed framework that ensures reliably collision-free behaviours in large-scale multirobot systems with switching interaction topologies is presented in [28].…”

Section: Introductionmentioning

confidence: 99%

Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Cheng

Wang

2020

Mathematical Problems in Engineering

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Flocking control problem of mobile robots under environment with unknown obstacles is addressed in this paper. Based on the simulated annealing algorithm, a flocking behaviour for mobile robots is achieved which converges to alignment while avoiding obstacles. Potential functions are designed to evaluate the positional relationship between robots and obstacles. Unlike the existing analytical method, simulated annealing algorithm is utilized to search the quasi-optimal position of robots in order to reduce the potential functions. Motion control law is designed to drive the robot move to the desired position at each sampling period. Experiments are implemented, and the results illustrate the effectiveness of the proposed flocking control method.

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Section: Introductionmentioning

confidence: 99%

Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Cheng

Wang

2020

Mathematical Problems in Engineering

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“…Applying the learning algorithm to the MPC will improve the performance of the system and guarantee safety, robustness and convergence in the presence of states and control inputs constraints [42] . In [43] , the problem of formation reconfiguration for a group of multiple cooperative UAVs using a combination between state transformation technique and decentralized Learning Based Model Predictive Control (LBMPC) controller in the presence of uncertainties and in an obstacle-loaded environment was solved. The introduced controller succeed to learn the unmodeled dynamics using the learning approach and solve the optimization control problem using the MPC.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Hafez

Givigi

Yousefi³

et al. 2017

Eng. Sci. and Milit. Techno.

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Teams of cooperative Unmanned Aerial Vehicles (UAVs) require intelligent and flexible control strategies to allow the accomplishment of a multitude of challenging group tasks. In this paper, we introduce a solution for the problem of tactic switching between the formation flight tactic and the dynamic encirclement tactic for a team of cooperative UAVs using a predictive decentralize control approach. Decentralized Model Predictive Control (MPC) is used to generate tactics for a team of UAVs in simulation and real-world validation. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of a desired formation, while a combination of decentralized LMPC and Feedback Linearization (FL) is applied to the UAV team to accomplish dynamic encirclement. The decision of switching from one tactic to the other is derived by a fuzzy logic controller, which, in its turn, is derived by a Reinforcement Learning (RL) approach. The main contributions of this paper are: (i) solution of the problem of tactic switching for a team of cooperative UAVs using LMPC and a fuzzy controller derived via RL; (ii) simulations demonstrating the efficiency of the method; and (iii) implementation of the solution to on-board real-time controllers on Qball-X4 quadrotors.

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“…Based on optimal trajectory generator coupled with a modified sliding controller for tracking the trajectory and avoiding collisions, the literature [27] accomplishes a UAV formation reconfiguration control scheme with autonomous collision avoidance system for application in 3D space. When facing the uncertainties and obstacles, the literature [28] adopts the learning based model predictive control (LBMPC) to solve the formation reconfiguration problem for a group of cooperative UAVs forming a desired formation. The literature [29] proposes a distributed linear MPC approach to solve the trajectory planning problem for rotary-wing UAVs, and the simulation results show that this method is valid.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

Zhou¹,

Kang²,

Dai³

et al. 2016

Mathematical Problems in Engineering

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For various threats in the enemy defense area, in order to achieve covert penetration and implement effective combat against enemy, the unmanned aerial vehicles formation needs to be reconfigured in the process of penetration; the mutual collision avoidance problems and communication constraint problems among the formation also need to be considered. By establishing the virtual-leader formation model, this paper puts forward distributed model predictive control and finite state machine formation manager. Combined with distributed cooperative strategy establishing the formation reconfiguration cost function, this paper proposes that adopting the revised quantum-behaved particle swarm algorithm solves the cost function, and it is compared with the result which is solved by particle swarm algorithm. Simulation result shows that this algorithm can control multiple UAVs formation autonomous reconfiguration effectively and achieve covert penetration safely.

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Formation reconfiguration of cooperative UAVs via Learning Based Model Predictive Control in an obstacle-loaded environment

Cited by 13 publications

References 26 publications

Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Flocking Control of Mobile Robots with Obstacle Avoidance Based on Simulated Annealing Algorithm

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

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