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Extended model predictive control scheme for smooth path following of autonomous vehicles

《机械工程前沿（英文）》 2022年第17卷第1期页码 4-4 doi: 10.1007/s11465-021-0660-4

摘要： This paper presents an extended model predictive control (MPC) scheme for implementing optimal path following of autonomous vehicles, which has multiple constraints and an integrated model of vehicle and road dynamics. Road curvature and inclination factors are used in the construction of the vehicle dynamic model to describe its lateral and roll dynamics accurately. Sideslip, rollover, and vehicle envelopes are used as multiple constraints in the MPC controller formulation. Then, an extended MPC method solved by differential evolution optimization algorithm is proposed to realize optimal smooth path following based on driving path features. Finally, simulation and real experiments are carried out to evaluate the feasibility and the effectiveness of the extended MPC scheme. Results indicate that the proposed method can obtain the smooth transition to follow the optimal drivable path and satisfy the lateral dynamic stability and environmental constraints, which can improve the path following quality for better ride comfort and road availability of autonomous vehicles.

关键词： autonomous vehicles vehicle dynamic modeling model predictive control path following optimization algorithm

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An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

《化学科学与工程前沿（英文）》 2022年第16卷第2期页码 237-250 doi: 10.1007/s11705-021-2058-6

摘要： Advanced model-based control strategies, e.g., model predictive control, can offer superior control of key process variables for multiple-input multiple-output systems. The quality of the system model is critical to controller performance and should adequately describe the process dynamics across its operating range while remaining amenable to fast optimization. This work articulates an integrated system identification procedure for deriving black-box nonlinear continuous-time multiple-input multiple-output system models for nonlinear model predictive control. To showcase this approach, five candidate models for polynomial and interaction features of both output and manipulated variables were trained on simulated data and integrated into a nonlinear model predictive controller for a highly nonlinear continuous stirred tank reactor system. This procedure successfully identified system models that enabled effective control in both servo and regulator problems across wider operating ranges. These controllers also had reasonable per-iteration times of ca. 0.1 s. This demonstration of how such system models could be identified for nonlinear model predictive control without prior knowledge of system dynamics opens further possibilities for direct data-driven methodologies for model-based control which, in the face of process uncertainties or modelling limitations, allow rapid and stable control over wider operating ranges.

关键词： nonlinear model predictive control black-box modeling continuous-time system identification machine learning industrial applications of process control

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Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

《能源前沿（英文）》 doi: 10.1007/s11708-023-0912-6

摘要： With the promotion of “dual carbon” strategy, data center (DC) access to high-penetration renewable energy sources (RESs) has become a trend in the industry. However, the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids. In this paper, a multi-timescale optimal scheduling model is established for interconnected data centers (IDCs) based on model predictive control (MPC), including day-ahead optimization, intraday rolling optimization, and intraday real-time correction. The day-ahead optimization stage aims at the lowest operating cost, the rolling optimization stage aims at the lowest intraday economic cost, and the real-time correction aims at the lowest power fluctuation, eliminating the impact of prediction errors through coordinated multi-timescale optimization. The simulation results show that the economic loss is reduced by 19.6%, and the power fluctuation is decreased by 15.23%.

关键词： model predictive control interconnected data center multi-timescale optimized scheduling distributed power supply landscape uncertainty

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多目标自适应优化模型预测控制——降低氧化锌回转窑的碳排放 Article

Ke Wei, Keke Huang, Chunhua Yang, Weihua Gui

《工程（英文）》 2023年第27卷第8期页码 96-105 doi: 10.1016/j.eng.2023.01.017

摘要：

The zinc oxide rotary kiln, as an essential piece of equipment in the zinc smelting industrial process, is presenting new challenges in process control. China's strategy of achieving a carbon peak and carbon neutrality is putting new demands on the industry, including green production and the use of fewer resources; thus, traditional stability control is no longer suitable for multi-objective control tasks. Although researchers have revealed the principle of the rotary kiln and set up computational fluid dynamics (CFD) simulation models to study its dynamics, these models cannot be directly applied to process control due to their high computational complexity. To address these issues, this paper proposes a multi-objective adaptive optimization model predictive control (MAO-MPC) method based on sparse identification. More specifically, with a large amount of data collected from a CFD model, a sparse regression problem is first formulated and solved to obtain a reduction model. Then, a two-layered control framework including real-time optimization (RTO) and model predictive control (MPC) is designed. In the RTO layer, an optimization problem with the goal of achieving optimal operation performance and the lowest possible resource consumption is set up. By solving the optimization problem in real time, a suitable setting value is sent to the MPC layer to ensure that the zinc oxide rotary kiln always functions in an optimal state. Our experiments show the strength and reliability of the proposed method, which reduces the usage of coal while maintaining high profits.

关键词： Zinc oxide rotary kiln Model reduction Sparse identification Real-time optimization Model predictive control Process control

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Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

LIU Hongyi, WANG Fei, WANG Lei

《机械工程前沿（英文）》 2007年第2卷第2期页码 168-174 doi: 10.1007/s11465-007-0028-4

摘要： To precisely implement the force control of robot manipulators in an unknown environment, a control strategy based on fuzzy prediction of the reference trajectory in the impedance model is developed. The force tracking experiments are executed in an open-architecture control system with different tracking velocities, different desired forces, different contact stiffnesses and different surface figurations. The corresponding force control results are compared and analyzed. The influences of unknown parameters of the environment on the contact force are analyzed based on experimental data, and the tunings of predictive scale factors are illustrated. The experimental results show that the desired trajectory in the impedance model is predicted exactly and rapidly in the cases that the contact surface is unknown, the contact stiffness changes, and the fuzzy force control algorithm has high adaptability to the unknown environment.

关键词： predictive tracking corresponding stiffness algorithm

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飞航导弹高精度自适应预测控制设计

孙明玮,陈增强,袁著祉,任强,杨明

《中国工程科学》 2005年第7卷第10期页码 23-27

摘要：

飞航导弹的飞行主要是通过姿态稳定与调节来实现的。通过以小扰动模型为基础的导弹动力学特性分析，建立了导弹姿态控制回路的串级控制结构，并且以离散模型作为基于递推最小二乘法的自适应预测控制的被控对象，把原先响应较慢的质心控制转换为反应较快而且精度高的弹道角控制。根据导弹的特性，在姿态内回路采用广义预测控制，在弹道外回路采用一种积分形式的预测控制。在参考信号上，实现了质心指令到弹道指令的有效变换，为高精度小超调跟踪奠定了基础。这种方法实现了姿态参考信号与导引指令的统一，姿态控制与质心控制的统一，充分降低了对气动等数据的精度要求，参数选择简单。数值仿真结果说明了这种方法的有效性；提出了进一步的研究方向。

关键词：飞航导弹自适应控制模型预测控制鲁棒性

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HVAC系统的模糊预测函数控制器设计

吕红丽,贾磊,王雷,高瑞

《中国工程科学》 2006年第8卷第9期页码 65-68

摘要：

针对暖通空调HVAC系统中由于存在高度非线性、时变特征以及扰动和不确定性等因素而难以控制的特点，提出基于Takagi-Sugeno(T-S)模糊模型的预测函数控制器设计方法。该方法通过最小二乘辨识算法建立系统的模糊T-S模型，然后基于模糊全局线性化预测模型，采用预测函数控制算法设计系统控制律。仿真实验结果表明该算法是一种跟踪性能好、鲁棒性强的有效控制方法。与常规的PID控制器相比，该方法具有超调量小、调整时间短等优良的动态性能。

关键词： T-S模糊模型预测函数控制最小二乘算法 HVAC系统

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时滞系统的辨识及NARMA模型的修正

王冬青

《中国工程科学》 2006年第8卷第2期页码 39-43

摘要：

对现有神经网络对非线性时滞系统的时滞辨识方法进行了补充说明和分析，同时指出现有的NARMA模型修正方法对时滞系统的不当之处。以时滞系统神经网络预测控制为例，介绍了NARMA模型的正确修正方法，仿真证明了所提出的修正方法能获得好的控制性能及抗干扰能力。

关键词：辨识 NARMA模型神经网络预测控制

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A comprehensive review of wind power based power system frequency regulation

《能源前沿（英文）》 2023年第17卷第5期页码 611-634 doi: 10.1007/s11708-023-0876-6

摘要： Wind power (WP) is considered as one of the main renewable energy sources (RESs) for future low-carbon and high-cost-efficient power system. However, its low inertia characteristic may threaten the system frequency stability of the power system with a high penetration of WP generation. Thus, the capability of WP participating in the system frequency regulation has become a research hotspot. In this paper, the impact of WP on power system frequency stability is initially presented. In addition, various existing control strategies of WP participating in frequency regulation are reviewed from the wind turbine (WT) level to the wind farm (WF) level, and their performances are compared in terms of operating principles and practical applications. The pros and cons of each control strategy are also discussed. Moreover, the WP combing with energy storage system (ESS) for system frequency regulation is explored. Furthermore, the prospects, future challenges, and solutions of WP participating in power system frequency regulation are summarized.

关键词： frequency regulation strategies wind turbine generators grid-forming control model predictive control energy storage system

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用于并网逆变器的改进三矢量无差拍模型预测直接功率控制策略 None

Chen-wen CHENG, Heng NIAN, Long-qi LI

《信息与电子工程前沿（英文）》 2018年第19卷第11期页码 1420-1431 doi: 10.1631/FITEE.1601874

摘要：用于并网逆变器的传统模型预测直接功率控制，在每个控制周期只使用一个逆变器电压矢量，导致并网电流和功率出现大量谐波分量，降低电能质量。为提高稳态性能，预测控制需要很高的采样频率，同时由于计算复杂，预测算法需要较长执行时间，二者存在矛盾。为解决这个问题，提出一种新的无差拍模型预测直接功率控制策略，在每个控制周期使用2个有效电压矢量和1个零电压矢量。采用三矢量策略保证开关频率恒定，提高电流和功率质量。不同于传统三矢量模型预测直接功率控制策略，该方法根据功率误差而非电网电压矢量位置选择三个矢量，保证矢量计算时间始终为正。同时，无需迭代计算，有利于在数字信号处理芯片上执行算法。最后，搭建1 kW逆变器实验平台，验证了该方法的有效性。

关键词：并网逆变器；模型预测控制；直接功率控制；三矢量；恒定开关频率；功率误差

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基于频域的PID广义预测控制器的鲁棒性分析

王繁珍,陈增强,姚向峰,袁著祉

《中国工程科学》 2006年第8卷第10期页码 66-70

摘要：

推导了PID广义预测控制器(PID-GPC)的闭环反馈结构, 采用小增益定理获得了存在建模误差(MPM)情况下PID-GPC鲁棒稳定的一个充分条件。然后在频域内分析了PID-GPC控制器的参数选择对其鲁棒性的影响。

关键词：预测控制鲁棒性频域小增益定理 PID控制

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基于模型预测控制的多微电网系统能量管理 None

Ke-yong HU, Wen-juan LI, Li-dong WANG, Shi-hua CAO, Fang-ming ZHU, Zhou-xiang SHOU

《信息与电子工程前沿（英文）》 2018年第19卷第11期页码 1340-1351 doi: 10.1631/FITEE.1601826

摘要：为降低多微电网能量管理优化算法计算复杂度，提出一种基于模型预测控制能量优化管理方法。首先，采用分解协调实现供需平衡，协调多微电网系统剩余能量，使供电成本最小化。然后，根据多微电网潮流特性，建立能量管理模型并提出能量优化问题。接着，采用对偶分解法将优化问题分为两部分，引入基于全局优化的分布式预测控制算法，经过算法迭代与协调实现最优解。仿真结果表明，该方法能实时向用户提供所需能源，提高可再生能源利用率。此外，与粒子群算法（particle swarm optimization，PSO）进行比较。比较结果表明，该算法具有更好性能、更快收敛速度和更高效率。

关键词：微电网；能源管理；预测控制；可再生能源；可控能源

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不确定路面附着系数条件下一种基于双层非线性模型预测控制的自动驾驶卡车轨迹规划方法 Research Articles

王鸿超1,张伟伟1,吴训成1,曹昊天2,高巧明3,罗素云1

《信息与电子工程前沿（英文）》 2020年第21卷第7期页码 963-1118 doi: 10.1631/FITEE.1900185

摘要：提出一种双层控制算法以规划配备四轮轮毂电机的自动驾驶卡车的行驶轨迹。该控制算法主要由主层非线性模型预测控制（MLN-MPC）算法和次层非线性模型预测控制（SLN-MPC）算法组成，其中，MLN-MPC控制算法用于规划合理的卡车行驶轨迹，SLN-MPC控制算法将车轮纵向滑移率限制在稳定区域，避免卡车在驱动过程中发生过度打滑。总体而言，该控制算法为一个闭环控制系统。在离线仿真环境下，通过AMESim、Simulink、dSPACE和TruckSim仿真软件联合仿真。仿真结果表明，本文所提算法能规划一条合理的车辆避障行驶轨迹，在不确定路面附着系数条件下能将车辆纵向滑移率控制在合理范围。此外，为评估该算法在实际应用中的可行性，在联合仿真系统中加入驾驶员模型验证该算法的稳定性与鲁棒性。与传统的基于PID控制算法相比，该算法具有更低的计算能耗。

关键词：自动驾驶卡车；轨迹规划；非线性模型预测控制；纵向滑移率

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MPC-based interval number optimization for electric water heater scheduling in uncertain environments

Jidong WANG, Chenghao LI, Peng LI, Yanbo CHE, Yue ZHOU, Yinqi LI

《能源前沿（英文）》 2021年第15卷第1期页码 186-200 doi: 10.1007/s11708-019-0644-9

摘要： In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy.

关键词： electric water heater load scheduling interval number optimization model predictive control uncertainty

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Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Kang Yuan,Yanjun Huang,Shuo Yang,Zewei Zhou,Yulei Wang,Dongpu Cao,Hong Chen,

《工程（英文）》 doi: 10.1016/j.eng.2023.03.018

摘要： Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment. This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data- and model-driven method. First, a data-driven decision-making module based on deep reinforcement learning (DRL) is developed to pursue a rational driving performance as much as possible. Then, model predictive control (MPC) is employed to execute both longitudinal and lateral motion planning tasks. Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements. Finally, two principles of safety and rationality for the self-evolution of autonomous driving are proposed. A motion envelope is established and embedded into a rational exploration and exploitation scheme, which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent. Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted, and the results show that the proposed online-evolution framework is able to generate safer, more rational, and more efficient driving action in a real-world environment.

关键词： Autonomous driving Decision-making Motion planning Deep reinforcement learning Model predictive control

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标题作者时间类型操作

Extended model predictive control scheme for smooth path following of autonomous vehicles

期刊论文

An integrated approach for machine-learning-based system identification of dynamical systems under control: application towards the model predictive control of a highly nonlinear reactor system

期刊论文

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

期刊论文

多目标自适应优化模型预测控制——降低氧化锌回转窑的碳排放

Ke Wei, Keke Huang, Chunhua Yang, Weihua Gui

期刊论文

Fuzzy force control of constrained robot manipulators based on impedance model in an unknown environment

LIU Hongyi, WANG Fei, WANG Lei

期刊论文

飞航导弹高精度自适应预测控制设计

孙明玮,陈增强,袁著祉,任强,杨明

期刊论文