滑动灵活的规定的性能边界引导强化学习控制输入受约束的非线性系统.
IEEE transactions on cybernetics
|September 25, 2025
概括
本研究引入了一种新的控制方法,用于具有输入约束的非线性系统. 滑动灵活的规定的性能边界引导增强学习 (SFPPB-RL) 方法提高了控制性能和安全性.
科学领域:
- 控制系统工程 控制系统工程
- 人工智能的人工智能
- 非线性动力学是一种非线性动力学.
背景情况:
- 输入受约束的非线性系统 (ICNS) 存在重大控制挑战.
- 现有的规定的性能控制 (PPC) 方法通常需要参数调整,可能会损害初始性能.
- 在实际应用中,平衡输入约束与所需性能至关重要.
研究的目的:
- 为ICNSs开发一种新的滑动灵活的规定的性能边界引导强化学习 (SFPPB-RL) 控制方法.
- 克服现有的PPC方法在参数调整和初始错误处理方面的局限性.
- 为了实现输入安全和控制性能之间的平衡.
主要方法:
- 滑动灵活的规定的性能边界 (PPB) 的设计,可以适应初始错误并动态修改约束放松.
- 集成辅助系统来管理输入和性能约束之间的合.
- 结合了基于标识符-关键-行为体结构的强化学习 (RL) 策略和退步技术.
主要成果:
- 拟议的SFPPB-RL方法消除了重复参数调试的需要.
- 它解决了初始过渡性性能和错误要求之间的权衡.
- 该方法有效地平衡了输入安全和控制性能,最大限度地降低了成本函数.
结论:
- 开发的SFPPB-RL控制算法确保了输入安全性和ICNS的规定的性能指标.
- 模拟结果验证了拟议方法的有效性.
- 这种方法为复杂的控制问题提供了适应性和强大的解决方案.
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