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Supplemental material, sj-docx-1-jet-10.1177_15266028221136434 for Long-Term Outcome of a “Mother-Child” Coaxial Dual-Catheter Technique for Percutaneous Transluminal Angioplasty of Central Vein Stenosis or Occlusion in Hemodialysis Patients by Qi-Quan Lai, Hui Zhang, Yu Zhou, Bo Chen, Bo Hu, Fan He, Yong Xu, Wen-Bo Zhao and Zi-Ming Wan in Journal of Endovascular Therapy

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The framework of Multi-Objective Pareto collaborative optimization (MOPCO) methods is proposed and then was applied to RLV reentry Trajectory Design. One of focus idea is to use NSGA-II algorithm to search the Pareto Front. In order to avoid the huge computation cost of the NSGA-II in CO, modify the conventional sequential CO to concurrent mode by Datacenter based on neural network response surface. The mathematic test problems and RLV reentry trajectory optimization validate MOPCO’ feasibility. We extend the conventional CO to solve Pareto Front of multi-objective multidisciplinary optimization successfully.

Put forward a closed form initial guidance law based on feed forward and feedback neural network control for finite propulsive maneuverable reentry vehicle . Minimum energy trajectories was generated by genetic algorithm, which used as training samples of three tiers feed forward BP neural network controller. A neural PID controller acts as a sate feedback controller compensating the control errors of feed forward controller . Simulating results indicate the efficiency, robustness and adaptiveness of the closed form nerual guidance law.

§Reentry trajectory Optimization is an important job in RLV preliminary design. A great many of optimization algorithms have been applied in solving this problem. But most of these applications just consider a single object such as minimum heat load or maximum maneuverable range. In practically, multi -objective reentry trajectory optimization is often necessary such as minimum heat load and maximum maneuverable range. These types of optimization problems have tr aditionally been solved by averaging each objective with a weighting factor, and then combine the objectives into a single scalar objective. Such reduction techniques eli minate the need for a more complex multi -objective algorithm, but introduce new parameters in the form of weighting factors. Except that，every run the algorithm can produce one optimal trajectory. NSGA -II algorithm is a good multi -objective genetic optimiz ation algorithm based on Pareto -optimal front with low computational requirements, elitist approach, parameter -less niche approach and simple constraint handling strategy. In this paper, NSGA -II algorithm is used to the RLV multi -objective reentry optimiza tion design with minimum heat load and maximum maneuverable range. The simulation result indicates that NSGA -II has good performance in reentry trajectory design and can produce all of the optimal results for different weighting factor.