SBIR PHASE I: Advanced Adaptive Critic Designs for Optimization and Control of Complex Nonlinear Dynamic Systems

SBIR 第一阶段：用于复杂非线性动态系统优化和控制的高级自适应批评设计

• 批准号: 9561171
• 负责人: Donald Sofge
• 金额: $ 7.5万
• 依托单位: NeuroDyne Incorporated
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-04-01 至 1996-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9561171&HistoricalAwards=false
• 关键词: SBIR; PHASE; Advanced; Adaptive; Critic

This Small Business Innovation Research (SBIR) Phase I project will design an adaptive critic based controller for use in optimizing control of complex large-scale, multi-goal systems which exhibit nonlinear dynamics. Currently implemented Adaptive Critic controllers often work well on small-scale problems, but convergence is slow on complex large-scale problems. In addition, the need to optimize performance with competing goals makes learning and convergence of adaptive critic controllers difficult. Phase I will demonstrate this adaptive critic controller on a typical but challenging problem: optimization of auto driving characteristics over a standard driving cycle. The controller will optimize throttle control, acceleration, deceleration, and braking so as to minimize energy expenditure over the course while also traversing the cycle in a minimum time span. This is similar to a long-haul truck driver searching for an optimal sequence of gas pedal and brake controls to go from New York to Los Angeles in minimum time and burning minimum fuel (and traveling through hills and mountains in between). Commercial applications of adaptive critic controllers of this kind are expected semiconductor manufacturing and flight control as well as in the automotive control problem.

这个小型企业创新研究 (SBIR) 第一阶段项目将设计一种基于自适应批评的控制器，用于优化表现出非线性动态的复杂大规模、多目标系统的控制。 目前实现的自适应批评控制器通常在小规模问题上表现良好，但在复杂的大规模问题上收敛速度很慢。 此外，需要通过竞争目标来优化性能，这使得自适应批评控制器的学习和收敛变得困难。 第一阶段将在一个典型但具有挑战性的问题上演示这种自适应批评控制器：在标准驾驶周期上优化自动驾驶特性。 控制器将优化油门控制、加速、减速和制动，以最大限度地减少整个过程中的能量消耗，同时在最短的时间内完成循环。 这类似于长途卡车司机寻找油门踏板和制动控制的最佳顺序，以便在最短时间内从纽约开往洛杉矶，燃烧最少的燃料（并穿越其间的丘陵和山脉）。 此类自适应临界控制器的商业应用预计将用于半导体制造和飞行控制以及汽车控制问题。