基于NN的强化学习对不平等受约束的非线性离散时间系统具有扰乱的最佳控制
概括
本研究介绍了一种使用演员关键神经网络 (NN) 的最佳控制器,用于管理带有干扰的受约束非线性系统. 该方法确保了系统的稳定性和有效的控制性能.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 非线性系统是非线性系统.
背景情况:
- 有关非线性离散时间系统中的受约束控制问题由于系统动态和外部干扰而具有挑战性.
- 现有的方法可能面临复杂的约束和不确定性,需要先进的控制策略.
- 演员关键神经网络 (NN) 为适应性和最佳控制提供了一个有希望的框架.
研究的目的:
- 开发一种最佳的控制器,用于受约束和干扰的非线性离散时间系统.
- 为了利用关键演员的NN来生成控制信号和评估控制器的性能.
- 在不利条件下确保控制系统的稳定性和有限性能.
主要方法:
- 用于控制信号生成的行为者-NN和性能评估的批评者-NN.
- 通过将惩罚函数纳入成本函数,将受约束的最佳控制转化为不受约束的问题.
- 运用游戏理论来确定最优的控制输入,考虑最坏的干扰情况和莱普诺夫稳定性理论来保证性能.
主要成果:
- 拟议的关键参与者NN-based控制器有效地解决了非线性离散时间系统中的受约束控制问题.
- 控制器确保控制信号具有统一的最终界限 (UUB),保证系统的稳定性.
- 在第三级动态系统上的数值模拟验证了开发的控制算法的有效性.
结论:
- 演员关键的NN方法为具有干扰的受约束非线性系统的最佳控制提供了强大的解决方案.
- 游戏理论和利亚普诺夫稳定理论的整合提高了控制器的可靠性和性能保证.
- 拟议的方法显示了需要精确和稳定的控制的实际应用的巨大潜力.
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