Development and Rubustness Analysis of Fifth-Level Adaptive Critics for Control

第五级自适应控制批评的发展和鲁棒性分析

• 批准号: 9634127
• 负责人: Sivasubramanya Balakrishnan
• 金额: $ 21.22万
• 依托单位: Missouri University of Science and Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-08-15 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9634127&HistoricalAwards=false
• 关键词: Development; Rubustness; Analysis; Fifth; Level

9634127 Balakrishnan This study will build on previous supported research into brain-like intelligent control, using an advanced form of neural network design. This research has three major objectives: the first is to develop two level-5 adaptive critic designs. (Increasing level numbers indicate increasing level of capabilities to solve engineering problems and closer mimicking of perceived human intelligence. Currently, the highest level of critic design that has been well understood and used in a few applications is a level- 4 critic.) The first critic design based on approximate dynamic programming outputs the optimal return function and the derivatives of cost with respect to system states. The second critic design outputs the Hamiltonian and the derivatives of the Hamiltonian with respect to states and control. Advantages of these designs include retrieving Ricatti mattrix from the critic, and being able to check the Legendre-Clebsch condition, necessary for minimality. The second major objective is to investigate the robustness of the synthesized controller to: a) parametric variations, b) bound but unmodelled disturbances, and c) stochastic disturbances. The robustness analysis will be based on singular value based techniques, Liapunov concepts, and Montecarlo simulations. The third major objective is to use a set of two critics in conjunction to solve bench mark engineering problems involving inner loop and outer loop to account for slow dynamics and fast dynamics. The significance or major goal of this research is to combine human problem solving (brain like intelligence) philosophy embedded in a level-5 critic design and firm mathematical foundations offered by modern control and dynamic programming to solve difficult control problems in engineering. This approach not only finds optimal solutions but attempts to analyze the resulting solutions through robustness analysis. Such relations to establish relative stability of neurocontrolle rs are expected to help in the acceptance of neurocontrol for implementation in engineering. *** ??

小行星9634127 这项研究将建立在以前支持的研究类脑智能控制，使用先进的神经网络设计形式。 本研究有三个主要目的：第一是发展两个第五级适应性批评设计。 （不断增加的水平数字表明解决工程问题的能力不断提高，并更接近于模仿人类智能。 目前，在一些应用程序中已经很好地理解和使用的最高等级的评论家设计是4级评论家。基于近似动态规划的第一次评价设计输出最优的回报函数和成本对系统状态的导数。 第二个评论家设计输出的哈密顿量和哈密顿量的导数的状态和控制。 这些设计的优点包括从评论家那里获得Ricatti矩阵，并且能够检查Legendre-Clebsch条件，这是极小性所必需的。 第二个主要目标是研究综合控制器对以下扰动的鲁棒性：a）参数变化，B）有界但未建模的扰动，以及c）随机扰动。 稳健性分析将基于奇异值技术、Liapunov概念和蒙特卡洛模拟。 第三个主要目标是使用一组两个批评结合解决基准工程问题，涉及内循环和外循环，以考虑慢动态和快动态。 本研究的意义或主要目标是将联合收割机人类问题解决（类脑智能）哲学嵌入到5级批判设计中，并结合现代控制和动态规划提供的坚实的数学基础来解决工程中困难的控制问题。 这种方法不仅可以找到最优的解决方案，但试图通过鲁棒性分析分析得到的解决方案。 这种建立神经控制器相对稳定性的关系，可望有助于神经控制在工程上的应用。 *** ??