Data-Driven Predictive Control for Continuous-Time Industrial Processes with Completely Unknown Dynamics

Optimal Management of Renewable Generation and Uncertain Demand with Reverse Fuel Cells by Stochastic Model Predictive Control

This paper proposes a control strategy for a Reverse Fuel Cell used to manage a Renewable Energy Community. A two-stage scenario-based Model Predictive Control algorithm is designed to define the best economic strategy to be followed during operation. Renewable energy generation and users' demand are forecasted by a suitably defined Discrete Markov Chain based method. The control algorithm is able to take into account the uncertainties of forecasts and the nonlinear behaviour of the Reversible Fuel Cell. The performance of proposed approach is tested on a Renewable Energy Community composed by…

基于观测器的非线性采样数据系统预测控制的稳定性

Stability of Observer Based Predictive Control for Nonlinear Sampled-data Systems

提出一种完全基于状态观测器的模型预测控制方法，证明闭环系统的半全局实际渐近稳定性，并通过数值示例验证。

面向周期运行的经济模型预测控制：一种二次规划方法

Economic Model Predictive Control for Periodic Operation: A Quadratic Programming Approach

针对周期系统经济MPC实时计算受限问题，提出基于单个二次规划的单层资源高效方案，并证明其可行性、稳定性与最优收敛性。

使用基于规范的总结性评估教授预测控制

Teaching Predictive Control Using Specification-based Summative Assessments

本文介绍伦敦帝国理工学院2018-2021年预测控制课程从线性MPC扩展到非线性MPC，并提出基于规范的MATLAB总结性评估框架，通过30多种真实问题变体评估学生控制器设计能力。

Approximate explicit model predictive control via piecewise nonlinear system identification

This article presents an identification methodology to capture general relationships, with application to piecewise nonlinear approximations of model predictive control for constrained (non)linear systems. The mathematical formulation takes, at each iteration, the form of a constrained linear (or quadratic) optimization problem that is mathematically feasible as well as numerically tractable. The efficiency of the devised methodology is demonstrated via two industrial applications. Results suggest the possibility to achieve high approximate precision with limited number of regions, leading to …

面向不同电价策略下批量制造系统调度的能量感知模型预测控制

Energy-Aware Model Predictive Control for Batch Manufacturing System Scheduling Under Different Electricity Pricing Strategies

本文提出一种能量感知 MPC 框架，将其表述为 MIQP 以动态调度批量制造过程，在满足生产约束下降低能耗成本，并为应对不同电价策略提供实用见解。

Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that uses a high-fidelity nonlinear model but is subject to communication delays with a local MPC that exploits simplified dynamics (due to limited computation) but has timely feedback. Unlike traditional cloud-based control that treats the cloud as powerful, remote, and sole controller in a networked-system control setting, the proposed …

工业过程智能控制中强化学习与贝叶斯优化的交叉：一种基于安全MPC并使用多目标BO的DPG方法

Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO

针对MPC-RL收敛慢、策略次优及安全性差的问题，提出结合多目标贝叶斯优化的MPC-RL-MOBO框架，利用CDPG估计阶段成本与梯度并通过EHVI采集函数高效安全地调整MPC参数，提升了闭环性能。

Aerial navigation in obstructed environments with embedded nonlinear model predictive control

We propose a methodology for autonomous aerial navigation and obstacle avoidance of micro aerial vehicles (MAV) using nonlinear model predictive control (NMPC) and we demonstrate its effectiveness with laboratory experiments. The proposed methodology can accommodate obstacles of arbitrary, potentially non-convex, geometry. The NMPC problem is solved using PANOC: a fast numerical optimization method which is completely matrix-free, is not sensitive to ill conditioning, involves only simple algebraic operations and is suitable for embedded NMPC. A C89 implementation of PANOC solves the NMPC prob…

基于闭环数据的数据驱动预测控制：一种工具变量方法

Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach

提出基于工具变量的闭环数据驱动预测控制新方法，解决反馈控制下输入与噪声相关性问题，并通过新型正则化项平衡控制成本与数据拟合，提升控制性能。

Multiscale Model Predictive Control of Battery Systems for Frequency Regulation Markets using Physics-Based Models

We propose a multiscale model predictive control (MPC) framework for stationary battery systems that exploits high-fidelity models to trade-off short-term economic incentives provided by energy and frequency regulation (FR) markets and long-term degradation effects. We find that the MPC framework can drastically reduce long-term degradation while properly responding to FR and energy market signals (compared to MPC formulations that use low-fidelity models). Our results also provide evidence that sophisticated battery models can be embedded within closedloop MPC simulations by using modern nonl…

因果感知的数据驱动预测控制

Causality-Informed Data-Driven Predictive Control

针对数据驱动预测控制因缺乏因果性导致高方差的问题，提出基于LQ分解的两阶段因果感知公式，不增加成本，在数值和加热炉应用中显著提升性能。

不确定环境下大规模分布式参数系统的鲁棒模型预测控制

Robust model predictive control for large-scale distributed parameter systems under uncertainty

针对不确定非线性分布式参数系统的高维与非凸优化难题，提出结合PCE、POD降阶与RNN的MPC框架，通过MILP求解全局最优控制，并在化工与生物反应器案例中验证有效性。