基于FBDE的非线性最佳控制及其对AGV的应用
IEEE transactions on cybernetics
|July 19, 2024
概括
本研究引入了一种新的深度神经网络方法来解决非线性最佳控制问题,克服传统线性化方法的局限性,以改善自主系统的轨迹跟踪.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 线性化是非线性动态系统的常见但有限的方法.
- 解决非线性最佳控制问题需要复杂的合微分方程.
- 现有的方法在准确性和适用性方面面临挑战.
研究的目的:
- 为有限地平线非线性最佳控制开发一种强大的方法.
- 在实际应用中克服线性化的局限性.
- 为了实现自动驾驶系统的高精度轨迹跟踪.
主要方法:
- 建立了非线性微分方程和一个新的优化问题之间的等价关系.
- 开发了一个深度神经网络框架,利用监督学习原则.
- 使用训练有素的深度残余网络实现了一个数值算法.
主要成果:
- 提出的方法有效地解决了高度合的非线性微分方程.
- 在自动引导车辆模拟中实现了高精度的轨迹跟踪.
- 与传统的线性化技术相比,表现出更高的性能.
结论:
- 基于深度残余网络的最佳控制算法对非线性系统有效.
- 这种方法为复杂的控制任务提供了强大的替代方案.
- 该方法显示出在自动驾驶汽车控制中的应用潜力很大.
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