Design and Flight Testing Evaluation of Formation Control Laws

Gu, Yu; Seanor, Brad; Campa, Giampiero; Napolitano, Marcello R.; Rowe, L.; Gururajan, Srikanth; Wan, Sheng

doi:10.1109/tcst.2006.880203

Cited by 201 publications

(83 citation statements)

References 7 publications

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“…The optimal control variables * are given by solving the nonlinear equation (21) and can be used to construct the optimal control profile defined by (17). The input is limited in a reasonable range according to different controlled objects.…”

Section: Explicit Nonlinear Model Predictive Controlmentioning

confidence: 99%

Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle

Zhang

2013

Advances in Mechanical Engineering

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A lifting body unmanned aerial vehicle (UAV) generates lift by its body and shows many significant advantages due to the particular shape, such as huge loading space, small wetted area, high-strength fuselage structure, and large lifting area. However, designing the control law for a lifting body UAV is quite challenging because it has strong nonlinearity and coupling, and usually lacks it rudders. In this paper, an explicit nonlinear model predictive control (ENMPC) strategy is employed to design a control law for a saucer-shaped UAV which can be adequately modeled with a rigid 6-degrees-of-freedom (DOF) representation. In the ENMPC, control signal is calculated by approximation of the tracking error in the receding horizon by its Taylor-series expansion to any specified order. It enhances the advantages of the nonlinear model predictive control and eliminates the time-consuming online optimization. The simulation results show that ENMPC is a propriety strategy for controlling lifting body UAVs and can compensate the insufficient control surface area.

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Section: Explicit Nonlinear Model Predictive Controlmentioning

confidence: 99%

Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle

Zhang

2013

Advances in Mechanical Engineering

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“…These studies include a formation flight experiment with UAVs utilizing bidirectional communication, and a guidance system utilizing relative velocity, attitude, and the position of the leader [1][2][3][4][5][6][7][8]. Sending and receiving its own position and attitude data through communication between the leader and follower and use the relative values.…”

Section: Introductionmentioning

confidence: 99%

Monocular Vision-Based Guidance and Control for a Formation Flight

Cheon¹,

Kim

Min³

et al. 2015

International Journal of Aeronautical and Space Sciences

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This paper describes a monocular vision-based formation flight technology using two fixed wing unmanned aerial vehicles. To measuring relative position and attitude of a leader aircraft, a monocular camera installed in the front of the follower aircraft captures an image of the leader, and position and attitude are measured from the image using the KLT feature point tracker and POSIT algorithm. To verify the feasibility of this vision processing algorithm, a field test was performed using two light sports aircraft, and our experimental results show that the proposed monocular vision-based measurement algorithm is feasible. Performance verification for the proposed formation flight technology was carried out using the X-Plane flight simulator. The formation flight simulation system consists of two PCs playing the role of leader and follower. When the leader flies by the command of user, the follower aircraft tracks the leader by designed guidance and a PI control law, and all the information about leader was measured using monocular vision. This simulation shows that guidance using relative attitude information tracks the leader aircraft better than not using attitude information. This simulation shows absolute average errors for the relative position as follows: X-axis: 2.88 m, Y-axis: 2.09 m, and Z-axis: 0.44 m.

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“…There has been considerable focus on theoretical studies in the area of multiple UAVs, [7][8][9][10][11] however, experimental results 8 validating the design of a formation controller are still rare. In general, the workload required to maintain a formation and to complete any mission in real time will be proportional to the number of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Formation Flying Using Bifurcating Potential Fields

Suzuki

Uchiyama

Bennet

et al. 2009

AIAA Guidance, Navigation, and Control Conference

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This paper describes the design of a three-dimensional formation flying guidance and control algorithm for a swarm of autonomous Unmanned Aerial Vehicles (UAVs), using the new approach of bifurcating artificial potential fields. We consider a decentralized control methodology that can create verifiable swarming patterns, which guarantee obstacle and vehicle collision avoidance. Based on a steering and repulsive potential field the algorithm supports flight that can transition between different formation patterns by way of a simple parameter change. The algorithm is applied to linear longitudinal and lateral models of a UAV. An experimental system to demonstrate formation flying is also developed to verify the validity of the proposed control system.

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Design and Flight Testing Evaluation of Formation Control Laws

Cited by 201 publications

References 7 publications

Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle

Explicit Nonlinear Model Predictive Control for a Saucer-Shaped Unmanned Aerial Vehicle

Monocular Vision-Based Guidance and Control for a Formation Flight

Three-Dimensional Formation Flying Using Bifurcating Potential Fields

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