Collaborative Research: Real-Time Iteration Governor for Constrained Nonlinear Model Predictive Control

协作研究:约束非线性模型预测控制的实时迭代调节器

基本信息

  • 批准号:
    1904441
  • 负责人:
  • 金额:
    $ 38.46万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-07-01 至 2024-06-30
  • 项目状态:
    已结题

项目摘要

Model predictive control is considered as a desirable candidate for high performance control of industrial processes and for safe and efficient operation of autonomous systems. Despite its remarkable success in the chemical process control industry, the successful transition of model predictive control into time-and-safety-critical applications is limited by the computational challenges faced in its real-time implementation. This research project lays the theoretical foundations for a new real-time implementation strategy that will significantly reduce the runtime of model predictive control, increase its overall reliability, and extend its applicability to the automotive and aerospace domains. Traditional model predictive control is implemented by fully solving an optimal control problem at every time step. This research project focuses on an alternative scheme where the solution to the optimal control problem is tracked with a bounded error over multiple time steps. By treating the numerical solver as a dynamic system that evolves in parallel to the controlled plant, the project will identify sufficient conditions for asymptotic stability and constraint satisfaction using Input-to-State Stability arguments. Recursive stability and constraint satisfaction will then be ensured by introducing the Real-Time Iteration Governor, which is an add-on supervision layer that suitably manipulates the reference of the model predictive controller so that the solution to the optimal control problem can be tracked with an acceptably small error. These theoretical contributions will be supplemented by dedicated numerical algorithms and analog circuits that take full advantage of running at a faster timescale with respect to the rate of change of the optimal control problem. The proposed framework will also address nonconvex constraints by using the Real-Time Iteration Governor to progressively steer the numerical solver away from undesirable local minima. Finally, to demonstrate the effectiveness of the Real-Time Iteration Governor and its supporting theory, the proposed framework will be applied to practical engineering challenges, which include the autonomous navigation of small spacecraft in close proximity to asteroids, the development of efficient control algorithms for self-driving cars, and the implementation of constrained coordination strategies for unmanned aerial vehicles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
模型预测控制被认为是工业过程高性能控制和自治系统安全高效运行的理想选择。尽管模型预测控制在化工过程控制行业取得了显著的成功,但其成功过渡到时间和安全关键型应用中受到其实时实现所面临的计算挑战的限制。该研究项目为一种新的实时实施策略奠定了理论基础,该策略将显着减少模型预测控制的运行时间,提高其整体可靠性,并将其适用性扩展到汽车和航空航天领域。传统的模型预测控制是通过在每一个时间步上完全求解最优控制问题来实现的。这个研究项目的重点是一个替代方案,其中的最优控制问题的解决方案是在多个时间步长的有界误差跟踪。通过将数值求解器视为与受控对象并行演化的动态系统,该项目将使用输入-状态稳定性参数确定渐近稳定和约束满足的充分条件。递归稳定性和约束满足,然后将确保通过引入实时迭代总督,这是一个附加的监督层,适当地操纵模型预测控制器的参考,使最优控制问题的解决方案可以跟踪一个可接受的小误差。这些理论贡献将得到专用的数值算法和模拟电路的补充,这些算法和电路充分利用了最优控制问题的变化率在更快的时间尺度上运行。拟议的框架还将解决非凸约束,通过使用实时迭代总督逐步引导数值求解器远离不需要的局部极小值。最后,为了证明实时迭代总督及其支持理论的有效性,所提出的框架将应用于实际工程挑战,其中包括靠近小行星的小型航天器的自主导航,自动驾驶汽车的有效控制算法的开发,以及无人驾驶飞行器的约束协调策略的实施。该奖项反映了NSF的法定使命,并通过使用基金会的学术价值和更广泛的影响审查标准。

项目成果

期刊论文数量(5)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Analysis of Time-Distributed Model Predictive Control When Using a Regularized Primal–Dual Gradient Optimizer
使用正则化原始双梯度优化器时的时间分布模型预测控制分析
  • DOI:
    10.1109/lcsys.2022.3186631
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    3
  • 作者:
    Skibik, Terrence;Nicotra, Marco M.
  • 通讯作者:
    Nicotra, Marco M.
A Feasibility Governor for Enlarging the Region of Attraction of Linear Model Predictive Controllers
  • DOI:
    10.1109/tac.2021.3123224
  • 发表时间:
    2020-11
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Terrence Skibik;Dominic Liao-McPherson;T. Cunis;I. Kolmanovsky;M. Nicotra
  • 通讯作者:
    Terrence Skibik;Dominic Liao-McPherson;T. Cunis;I. Kolmanovsky;M. Nicotra
A Terminal Set Feasibility Governor for Linear Model Predictive Control
线性模型预测控制的终端集可行性调节器
  • DOI:
    10.1109/tac.2022.3216967
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    6.8
  • 作者:
    Skibik, Terrence;Liao-McPherson, Dominic;Nicotra, Marco M.
  • 通讯作者:
    Nicotra, Marco M.
Time-distributed optimization for real-time model predictive control: Stability, robustness, and constraint satisfaction
实时模型预测控制的时间分布式优化:稳定性、鲁棒性和约束满足
  • DOI:
    10.1016/j.automatica.2020.108973
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    6.4
  • 作者:
    Liao-McPherson, Dominic;Nicotra, Marco M.;Kolmanovsky, Ilya
  • 通讯作者:
    Kolmanovsky, Ilya
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Marco Nicotra其他文献

Thermal Behaviour of Ski-boot Liners: Effect of Materials on Thermal Comfort in Real and Simulated Skiing Conditions.
  • DOI:
    10.1016/j.proeng.2014.06.066
  • 发表时间:
    2014-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Martino Colonna;Matteo Moncalero;Marco Nicotra;Alessandro Pezzoli;Elena Fabbri;Lorenzo Bortolan;Barbara Pellegrini;Federico Schena.
  • 通讯作者:
    Federico Schena.

Marco Nicotra的其他文献

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{{ truncateString('Marco Nicotra', 18)}}的其他基金

CAREER: Hypersampled Model Predictive Control: Reconciling Analog Systems with Digital Controllers
职业:超采样模型预测控制:协调模拟系统与数字控制器
  • 批准号:
    2046212
  • 财政年份:
    2021
  • 资助金额:
    $ 38.46万
  • 项目类别:
    Standard Grant

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