Chai Runqi, Savvaris Al, Tsourdos Antonios, Xia Yuanqing, Chai Senchun
IEEE Trans Cybern. 2020 Apr;50(4):1630-1643. doi: 10.1109/TCYB.2018.2881190. Epub 2018 Nov 22.
Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems.
高度约束的轨迹优化问题通常难以求解。由于一些实际需求，典型的轨迹优化模型可能需要制定包含多个目标。由于车辆动力学和任务目标中的不连续性或非线性，生成一个既能满足约束又能优化目标的折衷轨迹具有挑战性。为了解决多目标轨迹规划问题，本文应用了一种特定的多段离散化技术与最新的NSGA-III优化算法，并构建了一种新的进化最优控制求解器。此外，三种约束处理算法被纳入到这个进化最优控制框架中。详细分析了使用不同约束处理策略的性能。将所提出的方法与其他成熟的多目标技术进行了比较。实验研究表明，本文提出的方法在帕累托最优解的收敛能力和分布方面优于本文研究的其他基于进化的求解器。因此，本文提出的进化最优控制求解器更具吸引力，可为优化多目标连续时间轨迹优化问题提供一种替代方案。
