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环境的复杂性和不确定性。尽管经典控制和系统理论已经允许机器高度自动化，但人们对显示智能行为的系统的兴趣正在跨学科重新兴起。该项目将正式调查一种新设计的稳定性和稳健性，该设计已被证明对完全非线性飞机模拟的全包络在线学习控制特别有效，受到未建模动态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行进一步测试，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更传统形式的“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。该设计由两个阶段的学习过程组成，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结合到神经网络离线网络中。其次，通过双重启发式编程自适应批评结构，随时间增量地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显著提高相对于经典控制设计的性能。提出的研究将结合一种新的代数训练方法和积分-二次约束技术来证明闭环系统的稳定性，并为该全局控制系统建立性能保证。该项目的智力优势在于为理解智能控制系统的行为、质量和特征提供了一个系统、严格的框架。设计不仅具有自适应性和可重构性，而且安全可靠的系统将扩大可行的应用范围，从而鼓励这一领域的进一步研究。拟议研究的更广泛影响是创建了新的性能指标，用于比较智能控制系统和经典控制系统。此外，拟议的目标将加强我们对理性智力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.