Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

Foust, Rebecca; Chung, Soon‐Jo; Hadaegh, Fred Y.

doi:10.2514/1.g004590

Cited by 43 publications

(19 citation statements)

References 35 publications

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“…The nonlinear-corrected version of SCP (Problem 3) will be referred to as SCPn, with the inequality constraints represented as g i . Proofs of convergence to the KKT point of the nonconvex problem can be found in [11,12]. For now, we show that the optimal solutions of SCPn will result in a cost that decreases.…”

Section: Mpc-scp With Nonlinear Dynamics Correctionmentioning

confidence: 81%

“…For now, we show that the optimal solutions of SCPn will result in a cost that decreases. A more thorough, updated handling of the theory behind this method can be found in [11].…”

Section: Mpc-scp With Nonlinear Dynamics Correctionmentioning

confidence: 99%

“…If the agents are sufficiently dense, this may cause problems in execution. To ameliorate this issue, we point readers to the Modified SCPn algorithm presented in [11,12], which provides the same theoretical guarantees but with less restriction of the feasible set and faster execution.…”

Section: Mpc-scp With Nonlinear Dynamics Correctionmentioning

confidence: 99%

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Autonomous in-orbit satellite assembly from a modular heterogeneous swarm

et al. 2020

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This paper presents a decentralized, distributed guidance and control scheme to combine a heterogeneous swarm of component satellites into a large satellite structure. The component satellites for the heterogeneous swarm are chosen to promote flexibility in final shape inspired by crystal structures and Islamic tile art. After the ideal fundamental building blocks are selected, basic nanosatellite-class satellite designs are made to assist in simulations involving attitude control. The Swarm Orbital Construction Algorithm (SOCA) is a guidance and control algorithm to allow for the limited type heterogeneity and docking ability required for in-orbit assembly. The algorithm consists of two parts, a distributed auction which uses barrier functions to ensure the proper agent selection for each target, and a trajectory generation portion which leverages model predictive control and sequential convex programming to achieve optimal collision-free trajectories to the desired target point even with nonlinear system dynamics. The optimization constraints use a boundary layer to determine whether the collision avoidance or the docking constraints should be applied. The algorithm was tested in a simulated perturbed 6-DOF spacecraft dynamic environment for planar and out-of-plane final structures and on two robotic platforms, including a swarm of frictionless spacecraft simulation robots.

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Section: Mpc-scp With Nonlinear Dynamics Correctionmentioning

confidence: 81%

“…For now, we show that the optimal solutions of SCPn will result in a cost that decreases. A more thorough, updated handling of the theory behind this method can be found in [11].…”

Section: Mpc-scp With Nonlinear Dynamics Correctionmentioning

confidence: 99%

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Autonomous in-orbit satellite assembly from a modular heterogeneous swarm

et al. 2020

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“…We solve this challenge by directly limiting the change of the interaction forces placed on all neighbors when we plan for a robot. This concept is similar to trust regions in sequential optimization [32].…”

Section: Interaction-aware Multi-robot Planningmentioning

confidence: 97%

“…≈ ρ large φ small (x (31) ) + φ small (x (32) ) + φ env (x (34) ) , for the large robot 3, where φ small captures the interaction with the small robot 1 and 2, and φ env captures the interaction with the environment 4, e.g., ground effect and air drag.…”

Section: Continuous Function If and Only If It Has The Representationmentioning

confidence: 99%

Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions

et al. 2022

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We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned Deep Neural Networks (DNNs) with strong Lipschitz properties. We evolve two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: i) spectral normalization for stability and generalization guarantees of unseen data and ii) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multi-robot motion planning and the nonlinear tracking control designs because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors. Video is available at https://youtu.be/Y02juH6BDxo.

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Trajectory optimization with obstacles avoidance via strong duality equivalent and hp‐pseudospectral sequential convex programming

Xia

Wang

Gao

2021

Optim Control Appl Methods

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A trajectory optimization algorithm is developed in this article to compute a trajectory that avoids obstacles for an unmanned aerial vehicle and minimize the load factor. Quadratic and polyhedron obstacles are both considered in this article. To satisfy the safety distance requirements in engineering practice, a distance‐based obstacle avoidance constraint is formulated, which restricts the minimum distance that a vehicle can approach the obstacle. Strong duality equivalent converts the constraints into equivalent forms without introducing binary variables. Subsequently, the trajectory optimization problem is numerically solved via a hp‐pseudospectral sequential convex programming method, which iteratively solves a sequence of second‐order cone programming subproblems until convergence is attained. Furthermore, a modified trust‐region constraint and a customized nominal trajectory update rule are proposed to accelerate the convergence of the algorithm. A group of simulations are performed to demonstrate that the algorithm is computationally efficient and capable of generating a trajectory that satisfies safety distance requirements, preventing conservativeness in the trajectory, reducing the incidence of intersample constraints violation.

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Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming

Cited by 43 publications

References 35 publications

Autonomous in-orbit satellite assembly from a modular heterogeneous swarm

Autonomous in-orbit satellite assembly from a modular heterogeneous swarm

Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions

Trajectory optimization with obstacles avoidance via strong duality equivalent and hp‐pseudospectral sequential convex programming

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