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Inverse Dynamics Particle Swarm Optimization Applied to Constrained Minimum-Time Maneuvers Using Reaction Wheels

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The paper deals with the problem of spacecraft time-optimal reorientation maneuvers by means of internal torques (using reaction wheels), with boundary and path constraints. When searching for solutions to optimal attitude-control problems, spacecraft can be easily modeled as controlled by external torques; however, when using actuators such as reaction wheels, conservation of the total angular momentum must be taken into account and the wheel dynamics must be included. A rest-to-rest slew maneuver is considered where an optical sensor cannot be exposed to sources of bright light such as the Earth, the Sun and the Moon. The motion must be constrained to prevent the sensor axis from entering into established keep-out cones. The minimum-time solution is proposed using the Inverse Dynamics Particle Swarm Optimization technique: the attitude and the kinematics of the satellite evolve, leading to the successive attainment of the wheel control input via fixed-step numerical integration. Numerical results are presented evaluating the proposed technique over different scenarios. It is established that the computation of minimum time maneuvers with the proposed technique leads to near optimal solutions, which fully satisfy all the boundary and path constraints. The rapid convergence along with the ability to perform in a variety of difficult scenarios characterizes the proposed technique as a feasible future on-board path-planner.
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Article
This paper is devoted to the implementation and application of an improved version of the metaheuristic algorithm called magnetic charged system search. Some modifications and novelties are introduced and tested. Firstly, the authors’ attempt is to develop a self-adaptive and user-friendly algorithm which can automatically set all the preliminary parameters (such as the numbers of particles, the maximum iterations number) and the internal coefficients. Indeed, some mathematical laws are proposed to set the parameters and many coefficients can dynamically change during the optimization process based on the verification of internal conditions. Secondly, some strategies are suggested to enhance the performances of the proposed algorithm. A chaotic local search is introduced to improve the global best particle of each iteration by exploiting the features of ergodicity and randomness. Moreover, a novel technique is proposed to handle bad-defined boundaries; in fact, the possibility to self-enlarge the boundaries of the optimization variables is considered, allowing to achieve the global optimum even if it is located on the boundaries or outside. The algorithm is tested through some benchmark functions and engineering design problems, showing good results, followed by an application regarding the problem of time-suboptimal manoeuvres for satellite formation flying, where the inverse dynamics technique, together with the B-splines, is employed. This analysis proves the ability of the proposed algorithm to optimize control problems related to space engineering, obtaining better results with respect to more common and used algorithms in literature.
SAPIENZA-PSU) IAC-16,C1
  • D Spiller
D. Spiller (SAPIENZA-PSU) IAC-16,C1,8,1,x31975