Analysis and Design of a Global Adaptive Critic Controller

各种技术和应用的最新进展要求提高性能和可靠性，同时加剧了系统及其周围环境的复杂性和不确定性。尽管经典的控制和系统理论已经允许高度的机器自动化，但对显示智能行为的系统的新兴趣正在跨学科出现。该项目将正式研究一种新设计的稳定性和鲁棒性，该设计已被证明对全非线性飞机仿真的全包络在线学习控制特别有效，受未建模动力学和意外控制故障的影响。这种自适应控制系统将在新的更先进的飞机模拟器上进一步测试，该模拟器能够再现高度非线性现象，如气动弹性效应和失速/失速后行为。一个主要目标是在比传统形式的“可重构飞行控制”更极端的条件下降低飞机甚至航天器的损失率。该设计由两阶段学习过程组成，该过程通过神经网络函数逼近的新技术实现。首先，通过求解线性方程组，将全局经典控制设计融入离线神经网络网络中。其次，通过双启发式编程自适应批评架构，随着时间的推移逐步更新或微调网络参数。作者最近表明，在一系列不可预见的条件下，这种在线学习控制方法可以大大提高经典控制设计的性能。提出的研究将一种新的代数训练方法与积分二次约束技术相结合，以证明闭环稳定性并建立该全局控制系统的性能保证。这个项目的智力价值包括为理解智能控制系统的行为、质量和特征提供一个系统的、严格的框架。设计的系统不仅具有适应性和可重构性，而且安全可靠，这将扩大可行应用的范围，从而鼓励在这一领域的进一步研究。所提出的研究的更广泛的影响是创建新的性能指标来比较智能和经典控制系统。此外，提出的目标将增强我们对理性智能作为处理复杂性的可行范例的理解。

Recent advances in a variety of technologies and applications call for improved performance and reliability, while exacerbating the complexity and uncertainty of systems and their surroundings. Although classical control and system theory already allow for a high degree of machine automation, a renewed interest in systems that display intelligent behavior is emerging across disciplines. This project will formally investigate the stability and robustness of a novel design that has proven particularly effective for the full-envelope on-line learning control of a full nonlinear aircraft simulation, subject to unmodeled dynamics and unexpected control failures. This adaptive control system will be further tested on anew and more advanced aircraft simulator capable of reproducing highly nonlinear phenomena, such as aeroelastic effects, and stall/post-stall behavior. One major goal is to reduce the rate of loss of aircraft or even spacecraft under conditions even more extreme then those addressed by more conventional forms of "reconfigurable flight control."The design consists of a two-phase learning procedure that is realized through novel techniques for neural-network function approximation. Firstly, a global classical control design is incorporated in a network of neural networks off line, by solving linear systems of equations. Secondly, the network parameters are updated or fine tuned incrementally over time, through dual-heuristic-programming adaptive-critic architecture. The author recently showed that this approach to on-line learning control could considerably improve performance with respect to the classical control design, under a range of unforeseen conditions. The proposed research would combine a novel algebraic training approach with integral-quadratic-constraint techniques to prove closed-loop stability and establish performance guarantees for this global control system.The intellectual merit of this project consists of providing a systematic, rigorous framework for understanding the behavior, quality, and characteristics of intelligent control systems. The design of systems that are not only adaptive and reconfigurable, but also safe and reliable would widen the range of workable applications and, thus, encourage further research in this field. The broader impact of the proposed research is the creation of new performance metrics for comparing intelligent vs. classical control systems. Furthermore, the proposed objectives would enhance our understanding of rational intelligence as a viable paradigm for dealing with complexity.