EPCN - Online Optimization for the Control of Small Autonomous Vehicles

该项目旨在开展基础研究，以利用低成本和低功率微处理器、数字传感器和通信设备所带来的机会来设计反馈控制系统。虽然该项目的目标是开发能够在广泛的控制系统上应用的方法，但小型自主联网移动代理的控制将用于重点研究和验证所开发的技术。由于大量应用，包括测绘和测绘、基础设施检查、环境监测、农业监测、精准农业、牲畜管理等，人们对民用移动代理的兴趣空前高涨。事实上，据预测，商用无人机合法化将产生820亿美元的经济影响，直到2025年，农业将提供这一增长的最大部分。该方案的重点是自主和联网的移动代理，因为自主和网络连接允许代理组更快、更可靠地执行任务。拟议的活动将具有强大的教育成分，旨在激励学生攻读科学和工程方面的高级学位，通过暑期实习计划吸引高中生进入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。广泛的小型、低功耗、低成本微处理器、固态传感器和通信设备指向控制体系结构，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级别的本地计算的能力。共享网络的使用带来了极其灵活、易于部署和高度可重新配置的体系结构，但也带来了额外的挑战，因为当传感器数据从多个来源异步、延迟或可能损坏时，以连续时间或固定采样率运行的传统单位反馈环路是不够的。然而，这一挑战通过在每个节点上提供显著的计算能力而得到缓解，从而实现了新形式的控制设计。传统形式的反馈控制设计的一种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，并在该搜索中直接结合所有(或大部分)所需的设计约束，包括与故障的性能和稳健性相关的那些约束。直到几年前，由于优化引擎需要很长的时间，在线优化的使用大多局限于对慢过程的控制，如在化工过程控制、供应链管理或企业控制中。然而，当采样足够慢时，模型预测控制(MPC)和移动水平估计(MHE)等技术获得了令人印象深刻的成功，并成为许多领域的事实上的标准。上述微处理器、固态传感器和通信设备方面的技术进步有可能使在线优化达到广泛的应用，具有快速采样时间和有限的能量预算，包括控制小型自主联网移动代理。所提出的研究将对发展基于模型预测控制(MPC)和移动水平估计(MHE)相结合的原则性控制方法，以实现稳定和高性能的反馈控制系统具有重要意义。

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.