Abstract
This chapter introduces a new hybrid optimal control solver to solve the constrained SMV trajectory optimization problem. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the effectiveness and quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability, when compared with other typical optimal control solvers. A Monte Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chai, R., Savvaris, A., Tsourdos, A.: Violation learning differential evolution-based hp-adaptive pseudospectral method for trajectory optimization of space maneuver vehicle. IEEE Trans. Aerosp. Electron. Syst. 53(4), 2031–2044 (2017). https://doi.org/10.1109/TAES.2017.2680698
Elsayed, S.M., Sarker, R.A., Essam, D.L.: An improved self-adaptive differential evolution algorithm for optimization problems. IEEE Trans. Ind. Inform. 9(1), 89–99 (2013). https://doi.org/10.1109/TII.2012.2198658
Liu, D., Yang, X., Wang, D., Wei, Q.: Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints. IEEE Trans. Cybern. 45(7), 1372–1385 (2015). https://doi.org/10.1109/TCYB.2015.2417170
Gong, Y.J., Li, J.J., Zhou, Y., Li, Y., Chung, H.S.H., Shi, Y.H., Zhang, J.: Genetic learning particle swarm optimization. IEEE Trans. Cybern. 46(10), 2277–2290 (2016). https://doi.org/10.1109/TCYB.2015.2475174
Shen, Y., Wang, Y.: Operating point optimization of auxiliary power unit using adaptive multi-objective differential evolution algorithm. IEEE Trans. Ind. Electron. 64(1), 115–124 (2017). https://doi.org/10.1109/TIE.2016.2598674
Nelder, J.A., Mead, R.: A simplex method for function minimization. Computer Journal 7(4), 308–313 (1965)
Kevin, B., Michael, R., David, D.: Optimal nonlinear feedback guidance for reentry vehicles. In: Guidance, Navigation, and Control and Co-located Conferences. American Institute of Aeronautics and Astronautics (2006). https://doi.org/10.2514/6.2006-6074
Benson, D.A., Huntington, G.T., Thorvaldsen, T.P., Rao, A.V.: Direct trajectory optimization and costate estimation via an orthogonal collocation method. J. Guid. Control. Dyn. 29(6), 1435–1440 (2006). https://doi.org/10.2514/1.20478
Kim, J.J., Lee, J.J.: Trajectory optimization with particle swarm optimization for manipulator motion planning. IEEE Trans. Ind. Inform. 11(3), 620–631 (2015). https://doi.org/10.1109/TII.2015.2416435
Ergezer, M., Simon, D.: Mathematical and experimental analyses of oppositional algorithms. IEEE Trans. Cybern. 44(11), 2178–2189 (2014). https://doi.org/10.1109/TCYB.2014.2303117
Hausler, A.J., Saccon, A., Aguiar, A.P., Hauser, J., Pascoal, A.M.: Energy-optimal motion planning for multiple robotic vehicles with collision avoidance. IEEE Trans. Control. Syst. Technol. 24(3), 867–883 (2016). https://doi.org/10.1109/TCST.2015.2475399
Chen, Z., Zhang, H.T.: A minimal control multiagent for collision avoidance and velocity alignment. IEEE Trans. Cybern. 47(8), 2185–2192 (2017). https://doi.org/10.1109/TCYB.2017.2712641
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Chai, R., Savvaris, A., Tsourdos, A., Chai, S. (2020). Hybrid Optimization Methods with Enhanced Convergence Ability. In: Design of Trajectory Optimization Approach for Space Maneuver Vehicle Skip Entry Problems. Springer Aerospace Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-9845-2_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-9845-2_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9844-5
Online ISBN: 978-981-13-9845-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)