EPCN - Online Optimization for the Control of Small Autonomous Vehicles

EPCN - 小型自动驾驶车辆控制在线优化

• 批准号: 1608880
• 负责人: Joao Hespanha
• 金额: $ 35.98万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1608880&HistoricalAwards=false
• 关键词: EPCN; Online; Optimization; Control; Small

This project aims at carrying out fundamental research towards taking advantage of the opportunities enabled by low-cost and low-power microprocessors, digital sensors, and communication devices for the design of feedback control systems. While the project's goal is to develop methodologies that can find application on a wide range of control systems, the control of small autonomous networked mobile agents will be used to focus the research and to validate the technologies developed. The interest in mobile agents for civilian applications is at an all-time high, motivated by a large number of applications, including mapping and surveying, infrastructure inspection, environmental monitoring, agricultural monitoring, precision agriculture, livestock management, among many others. In fact, it was foreseen that the legalization of commercial drones will create an economic impact of $82 billion until 2025 and that agriculture would provide the most substantial portion of that growth. This proposal is focused on autonomous and networked mobile agents, as autonomy and network connectivity allow groups of agents to carry out tasks faster and more reliably. The proposed activities will have a strong educational component aimed at motivating students to pursue advanced degrees in the sciences and engineering, through a combination of activities aimed attracting high-school students to the STEM disciplines through a Summer Internship program, exposing undergraduate students to research, and expanding the opportunities for mentoring and professional development for graduate students.The wide range of small, low-power, low-cost microprocessors, solid-sate sensors, and communication devices point to control architectures in which feedback loops are composed of multiple units connected through shared communication networks. These units will include controllers, sensors, and actuators, each endowed with the ability to perform some level of local computation. The use of shared networks results in architectures that are extremely flexible, easy to deploy, and highly reconfigurable, but also introduce additional challenges because the traditional unity feedback loop that operates in continuous time or at a fixed sampling rate is not adequate when sensor data arrives from multiple sources, asynchronously, delayed, and possibly corrupted. However, this challenge is mitigated by the availability of significant computational power at each node, enabling novel forms of control design. An attractive alternative to traditional forms of feedback control design relies in the use of online optimization algorithms that search directly for control actions and directly incorporate in this search all (or most of) the desired design constraints, including those constraints related to performance and robustness with respect to faults. Until a few years ago, the use of online optimization was mostly restricted to the control of slow processes, such as in chemical process control, supply chain management, or enterprise control, because of the long times required by the optimization engines. Nevertheless, when sampling was sufficiently slow, techniques like Model Predictive Control (MPC) and Moving Horizon Estimation (MHE) enjoyed an impressive success and became the de-facto standard in many domains. The technological advances in microprocessors, solid-sate sensors, and communication devices mentioned above have the potential to enable online-optimization to reach a wide range of applications with fast sampling times and limited energy budgets, including the control of small autonomous networked mobile agents. The research proposed will make significant contributions towards the development of principled approaches for control based on the integration of Model Predictive Control (MPC) and Moving Horizon Estimation (MHE) to achieve in stable and high performance feedback control systems.

该项目旨在开展基础研究，以利用低成本和低功耗微处理器，数字传感器和通信设备为反馈控制系统的设计提供机会。虽然该项目的目标是开发可以在广泛的控制系统中找到应用的方法，但小型自主网络移动的代理的控制将用于集中研究并验证所开发的技术。对民用移动的代理人的兴趣空前高涨，其动机是大量的应用，包括测绘和测量、基础设施检查、环境监测、农业监测、精确农业、牲畜管理等。事实上，预计到2025年，商用无人机的合法化将产生820亿美元的经济影响，而农业将提供这一增长的最大部分。该建议的重点是自主和联网的移动的代理，因为自主和网络连接允许代理组更快，更可靠地执行任务。拟议的活动将有一个强大的教育组成部分，旨在激励学生追求科学和工程的高级学位，通过一系列活动，旨在通过暑期实习计划吸引高中生到STEM学科，让本科生接触研究，并扩大研究生的指导和专业发展机会。低成本微处理器、固态传感器和通信设备指向这样的控制体系结构，其中反馈回路由通过共享通信网络连接的多个单元组成。这些单元将包括控制器、传感器和执行器，每个单元都具有执行某种程度的本地计算的能力。共享网络的使用导致架构非常灵活，易于部署和高度可重新配置，但也带来了额外的挑战，因为当传感器数据从多个源异步，延迟和可能损坏时，以连续时间或固定采样率操作的传统单位反馈回路是不够的。然而，这一挑战被每个节点处的显著计算能力的可用性所缓解，从而实现新颖形式的控制设计。传统形式的反馈控制设计的一个有吸引力的替代方案依赖于使用在线优化算法，该算法直接搜索控制动作，并直接将所有（或大部分）期望的设计约束（包括与性能和故障鲁棒性相关的约束）合并到该搜索中。直到几年前，在线优化的使用主要限于缓慢过程的控制，例如化学过程控制，供应链管理或企业控制，因为优化引擎需要很长的时间。然而，当采样足够慢时，模型预测控制（MPC）和移动地平线估计（MHE）等技术取得了令人印象深刻的成功，并成为许多领域事实上的标准。上述微处理器、固态传感器和通信设备的技术进步有可能使在线优化达到具有快速采样时间和有限能量预算的广泛应用，包括控制小型自主联网的移动的代理。提出的研究将作出重大贡献的原则性方法的发展控制的基础上集成的模型预测控制（MPC）和滚动时域估计（MHE），以实现稳定和高性能的反馈控制系统。