MODEL REFERENCE ADAPTIVE CONTROL BASED ON NONLINEAR COMPENSATION FOR TURBOFAN ENGINE

The design of a turbofan rotor speed control system, using model reference adaptive control（MRAC） method with input and output measurements, is discussed for the purpose of practical application. The nonlinear compensator based on functional link neural network is used to deal with the engine nonlinearity and the hardware-in-loop simulation is also developed. The results show that the nonlinear MRAC controller has the adequate performance of compensating and adapting nonlinearity arising from the change of engine state or working environment. Such feature demonstrates potential practical applications of MRAC for aeroengine control system.

MULTIVARIABLE MODEL REFERENCE ADAPTIVE CONTROL FOR A TURBOFAN ENGINE

A decentralized model reference adaptive control (MRAC) scheme is proposed and applied to design a multivariable control system of a dual-spool turbofan engine.Simulation studies show good static and dynamic performance of the system over the fullflight envelope. Simulation results also show the good effectiveness of reducing interactionin the multivariable system with significant coupling. The control system developed has awide frequency band to satisfy the strict engineering requirement and is practical for engineering applications.

Robustness Assessment and Adaptive FDI for Car Engine

A new on-line fault detection and isolation （FDI） scheme proposed for engines using an adaptive neural network classifier is evaluated for a wide range of operational modes to check the robustness of the scheme in this paper. The neural classifier is adaptive to cope with the significant parameter uncertainty, disturbances, and environment changes. The developed scheme is capable of diagnosing faults in on-line mode and the FDI for the closed-loop system with can be directly implemented in an on-board crankshaft speed feedback is investigated by diagnosis system （hardware）. The robustness of testing it for a wide range of operational modes including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all these changes occurring at the same time. The evaluations are performed using a mean value engine model （MVEM）, which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

An improved high-fidelity adaptive model for integrated inlet-engine-nozzle based on mechanismdata fusion

Nowadays,there has been an increasing focus on integrated flight propulsion control and the inlet-exhaust design for the aero-propulsion system.Traditional component-level models are inadequate due to installed performance deviations and mismatches between the real engine and the model,failing to meet the accuracy requirements of supersonic conditions.This paper establishes a quasi-one-dimensional model for the inlet-exhaust system and conducts experimental calibration.Additionally,a mechanism-data fusion adaptive modeling scheme using an Extreme Learning Machine based on the Salp Swarm Algorithm(SSA-ELM)is proposed.The study reveals the inlet model’s efficacy in reflecting installed performance,flow matching,and mitigating pressure distortion,while the nozzle model accurately predicts flow coefficients and thrust coefficients,and identifies various operational states.The model’s output closely aligns with typical experimental parameters.By combining offline optimization and online adaptive correction,the mechanismdata fusion adaptive model substantially reduces output errors during regular flights and varying levels of degradation,and effectively handles gradual degradation within a single flight cycle.Notably,the mechanism-data fusion adaptive model holistically addresses total pressure errors within the inlet-exhaust system and normal shock location correction.This approach significantly curbs performance deviations in supersonic conditions.For example,at Ma=2.0,the system error impressively drops from 34.17%to merely 6.54%,while errors for other flight conditions consistently stay below the 2.95%threshold.These findings underscore the clear superiority of the proposed method.

Adaptive Constrained On-board Guidance Technology forPowered Glide Vehicle

To make full use of expanded maneuverability and increased range,adaptive constrained on-board guidance technology is the key capability for a glide vehicle with a double-pulse rocket engine,especially under the requirements of desired target changing and on-line reconfigurable control and guidance.Based on the rapid footprint analysis,whether the new target is within the current footprint area is firstly judged.If not,the rocket engine ignites by the logic obtained from the analysis of optimal flight range by the method of hp-adaptive Gauss pseudospectral method(hp-GPM).Then,an on-board trajectory generation method based on powered quasi-equilibrium glide condition(QEGC)and linear quadratic regulator(LQR)method is used to guide the vehicle to the new target.The effectiveness of the guidance method consisted of powered on-board trajectory generation,LQR trajectory tracking,footprint calculation,and ignition time determination is indicated by some simulation examples.

Energy Management Optimization Based on Aging Adaptive Functional State Model of Battery for Internal Combustion Engine Vehicles

This paper presents an energy management optimization system based on an adaptive functional state model of battery aging for internal combustion engine vehicles(ICEVs).First,the functional characteristics of batteries in ICEVs are investigated.Then,an adaptive functional state model is proposed to represent battery aging throughout the entire battery service life.A battery protection scheme is developed,including over-discharge and graded over-current protection to improve battery safety.A model-based energy management strategy is synthesized to comprehensively optimize fuel economy,battery life preservation,and vehicle performance.The performance of the proposed scheme was examined under comprehensive test scenarios based on field and bench tests.The results show that the proposed energy management algorithm can effectively improve fuel economy.

ADAPTIVE REPAIR APPROACH FOR RECOVERING COMPONENTS FROM DEFECTS

The geometry of each worn part is unique, so that a repair has to be tailored to each part individually. To ensure that a high quality repair is carried out, tool paths have to be generated adaptively for the tmique geometry and pose of the part being repaired. A polygonal modelling approach is introduced to rapidly construct a geometric model of the part to be repaired, together with a defect-free model with identical geometry and poise. The two models are compared so that the defects are identified for direct use by the laser cladding, machining and inspection processes.

Adaptive Air-Fuel Ratio Control with MLP Network

This paper presents an application of adaptive neural network model-based predictive control (MPC) to the air-fuel ratio of an engine simulation. A multi-layer perceptron (MLP) neural network is trained using two on-line training algorithms: a back propagation algorithm and a recursive least squares (RLS) algorithm. It is used to model parameter uncertainties in the nonlinear dynamics of internal combustion (IC) engines. Based on the adaptive model, an MPC strategy for controlling air-fuel ratio is realized, and its control performance compared with that of a traditional PI controller. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving nonlinear programming (NLP) problems, is implemented to speed up nonlinear optimization in the MPC. Keywords Air-fuel ratio control - IC engine - adaptive neural networks - nonlinear programming - model predictive control Shi-Wei Wang PhD student, Liverpool John Moores University; MSc in Control Systems, University of Sheffield, 2003; BEng in Automatic Technology, Jilin University, 2000; Current research interests automotive engine control, model predictive control, sliding mode control, neural networks.Ding-Li Yu obtained B.Eng from Harbin Civil Engineering College, Harbin, China in 1981, M.Sc from Jilin University of Technology, Changchun, China in 1986 and PhD from Coventry University, U.K. in 1995, all in control engineering. He is currently a Reader in Process Control at Liverpool John Moores University, U.K. His current research interests are in process control, engine control, fault detection and adaptive neural nets. He is a member of SAFEPROCESS TC in IFAC and an associate editor of the IJMIC and the IJISS.

新疆煤层气大规模高效勘探开发关键技术领域研究进展与突破方向

新疆煤层气资源量7.5万亿m^(3)(2000 m以浅),已施工煤层气井450口,年产气量达到0.8亿m^(3),新疆维吾尔自治区提出了2025年实现煤层气产量25亿m^(3)的目标,煤层气大规模高效开发成为紧迫的重大需求。从煤层气富集模式与选区技术、“甜点”预测探测技术、加速滚动开发与快速增储上产策略、地质适配性开发技术、煤层气与煤炭、油气协同开发技术5个关键技术领域,系统梳理了新疆煤层气勘探开发已取得的主要研究进展,分析提出了可能突破方向。研究表明:新疆煤储层具有多-厚煤层普遍、低阶煤发育、急倾斜煤层多见、煤体变形与构造控制显著、水文条件和露头条件复杂,和三“低”(含气量低、甲烷体积分数低、含气饱和度低)五“高”(高含气强度、高孔隙度、高地应力变化、高储层压力变化、高渗透率变化)的含气性及物性等煤层气地质独特性;煤层气成因与富集模式具有多样性,包括生物成因气、热成因气或生物-热复合成因及其相应富集模式,生物成因气藏或生物成因气贡献普遍;煤层气分布赋存规律呈现前陆盆地、山间盆地显著差异性;创新形成基于“两”分开(浅部与深部,低阶煤与中高阶煤)“两”结合(地质评价与工程评价,多元数据)的科学评价与基于“机器学习+三维地质建模”的精准选区技术是第1个突破方向。深部煤层气/煤系气“甜点”发育区域主要为盆内坳陷的凸起、盆内隆起的凹陷、盆缘斜坡,高产井位多为构造高点,发育层位为割理裂隙发育的原生结构煤层或孔裂隙发育的煤系砂砾岩储层;基于“地球物理+岩石物理+岩石力学地层新方法”和“地质甜点+工程甜点新理念”的深部煤层气/煤系气“甜点”预测探测技术是第2个突破方向。低风险、短周期、高效率、多批次工程部署是加速滚动开发的基本原则;中浅部煤层气快速增储上产技术策略是在优选新区块布井建井、对老区块煤层气井进行增产改造;深部煤层气快速增储上产技术策略是在大型盆地缓坡深部和盆内凸起“甜点”区优先部署开发;科学加速滚动开发与高效快速增储上产的工程部署方法与技术策略是第3个突破方向。在井网井型差异性优化部署、低储层伤害钻井固井、高可靠性录井测井试井、多井型高效分段压裂、低套压-控压排采管控等工程技术取得重要进展;发展构建新疆煤层气大规模高效勘探开发地质适配性技术体系是第4个突破方向。开展先采气后采煤、煤层气与煤共采、煤层气与原位富油煤共采,推动中浅部煤层气与煤炭协同勘探开发;开展煤系叠合型气藏开发、煤层气与煤系气共探共采、煤系全油气系统勘探开发,推动深部煤层气与油气协同勘探开发,已有关注和探索;煤层气与煤、油气共探共采是第5个突破方向。成果试图为新疆煤层气大规模高效勘探开发提供技术支持和工程决策参考。

An adaptive turbo-shaft engine modeling method based on PS and MRR-LSSVR algorithms

In order to establish an adaptive turbo-shaft engine model with high accuracy, a new modeling method based on parameter selection （PS） algorithm and multi-input multi-output recursive reduced least square support vector regression （MRR-LSSVR） machine is proposed. Firstly, the PS algorithm is designed to choose the most reasonable inputs of the adaptive module. During this process, a wrapper criterion based on least square support vector regression （LSSVR） machine is adopted, which can not only reduce computational complexity but also enhance generalization performance. Secondly, with the input variables determined by the PS algorithm, a mapping model of engine parameter estimation is trained off-line using MRR-LSSVR, which has a satisfying accuracy within 5＆. Finally, based on a numerical simulation platform of an integrated helicopter/ turbo-shaft engine system, an adaptive turbo-shaft engine model is developed and tested in a certain flight envelope. Under the condition of single or multiple engine components being degraded, many simulation experiments are carried out, and the simulation results show the effectiveness and validity of the proposed adaptive modeling method.

Model reference adaptive control with smooth switching scheme for piecewise linear systems and its application in turbofan engine control

A model reference adaptive control(MRAC)with smooth switching scheme was proposed for piecewise linear systems,and the method was utilized in turbofan engine control to avoid the discontinuity of control input.In this scheme,each sub-region of the operating envelope had its own MRAC controller,and smooth indicator function based smooth switching scheme was introduced to switch multiple controllers smoothly at the boundary of adjacent sub-regions.The Lyapunov stability analysis indicated that the proposed smooth switching scheme can guarantee the convergence of the closed-loop system during the controllers switching.The tracking error system was converted into a switched system to analyze the global stability of the closed-loop system.The advantage of the method was that the chattering of system output and instability caused by asynchronous switching can be eliminated.The simulation illustrates the effectiveness of the proposed control scheme in comparison with the existing MRAC controller with gain scheduling for turbofan engine.

SA765Gr.Ⅱ合金钢热拉伸本构模型的参数反求

在温度为950~1150℃,应变速率为0.01~5 s^(-1)的条件下,使用Gleeble-1500D热模拟实验机对SA765Gr.Ⅱ合金钢进行了等温热拉伸实验以研究其热拉伸变形行为。首先通过线性回归方法推导了SA765Gr.Ⅱ合金钢的Norton-Hoff模型参数,之后提出了一种基于自适应模拟退火(ASA)算法求解本构模型参数的方法(反求方法)。结果表明:相比于回归方法,反求方法构建的模型预测相关系数R从0.9831提高到0.9958、均方根误差RMAE由6.392降低至3.603、平均相对误差AARE由5.38%降低至3.69%。线性回归方法构建的模型预测误差期望与标准偏差分别为0.97和8.76,反求方法构建的模型预测误差期望与标准偏差分别为0.13和5.14。通过反求方法构建的Norton-Hoff模型预测精度得到了提高。

自适应循环发动机快速推力变化过渡过程性能初步研究

自适应循环发动机是基于变循环发动机设计理念基础并充分考虑飞发一体化设计、热管理等综合性能的面向未来飞行平台的先进动力装置,可通过诸多可调机构实现发动机涵道比、增压比、流量以及工作模式的灵活改变,从而适应不同飞行任务的复杂任务剖面需求,获得更大飞行包线内的性能优化。本文基于自适应循环发动机的性能计算仿真模型,在相关研究基础上结合模式转换和加减速过程实现快速推力变化过程,根据模式转换与加减速控制规律设计,开展典型算例下的快速推力变化过渡过程性能分析并验证其控制规律设计方法可行性,在亚声速巡航工况下,发动机在以M3模式叶尖风扇最小角度状态下,低压转子相对物理转速从0.6加速至1.0,最大可使终止推力增大32.4%,相较于单一模式加减速过渡过程,推力变化范围更宽,加速时间更短,转速变化范围更小。

带FLADE的ACE典型工况不同工作模式性能仿真分析

为实现典型工况模式选取方案,针对一种带FLADE(Fan on blade)的自适应循环发动机展开研究。基于各部件气动热力学原理在MATLAB/Simulink平台建立了该构型3外涵模式整机计算模型。在此模型基础上,通过减少迭代变量与残差变量的思路,分析了该构型在节流过程中两种工作模式不同典型工况下的性能。仿真结果表明:在地面工况条件下,3外涵模式与单+第三外涵模式相比在低转速下推力较高,而高转速下则相反,地面大功率状态起飞可以采用单+第三外涵模式;对推力需求不高的亚声速巡航工况,可在飞机爬升后开启MSV(Mode selection valve)使发动机以3外涵模式工作。

LLaMA-LoRA Neural Prompt Engineering:A Deep Tuning Framework for Automatically Generating Chinese Text Logical Reasoning Thinking Chains

The exption of Chinese natural language processing(NLP)has stimulated research in the broader NLP domain.However,existing large language models have limitations in comprehending and reasoning in Chinese.This paper addresses these limitations by enhancing Chinese language models comprehension and reasoning capabilities while minimizing resource requirements.We propose LLaMA-LoRA,a neural prompt engineering framework that builds upon the LLaMA-13B model and incorporates the Low-Rank Adaptation(LoRA)of Large Language Models technique for refinement.Chain-of-Thought(CoT)are crucial for generating intermediate reasoning chains in language models,but their effectiveness can be limited by isolated language patterns.Erroneous reasoning resulting from conventional prompts negatively impacts model performance.Automatic prompts are introduced to encourage reasoning chain generation and accurate answer inference.Training the model with an extensive corpus of Chinese CoT data enhances its comprehension and reasoning abilities.The LLaMA-LoRA model demonstrates exceptional performance across numerous Chinese language tasks,surpassing benchmark performance achieved by related language models such as GPT-3.5,Chat-GLM,and OpenAssistant,delivering accurate,comprehensive,and professional answers.The availability of our open-source model code facilitates further research in the field of Chinese text logical reasoning thinking chains.

全渠道电商库存路径及定价问题优化研究

针对全渠道模式下电商库存路径及定价问题,考虑各前置仓需求不确定因素,本文提出一种按照不同售卖渠道的商品差异化定价策略,通过设置电商关于需求不确定风险态度的保守系数,构建以总利润最大化为目标的混合整数非线性鲁棒优化模型,并设计自适应模拟退火粒子群算法进行求解。选取含有10个和20个前置仓的两组算例,验证本文模型算法的适用性和有效性。实验分析结果显示,相比于统一定价,差异化定价可以提高电商总利润分别约5%和6%。敏感性分析结果表明,提升线上客户的线下购物体验以增加线上自提渠道客户数量,并组织线上营销活动以提高线上客户对电商促销努力的敏感度,能给电商带来更高的利润;掌控未来市场波动风险并准确预测需求信息以降低电商保守系数和需求最大偏离系数,亦可提高电商总利润。研究结论可为电商制定前置仓库存配送路径策略与各渠道商品定价方案提供参考。

基于差分进化-自适应Metropolis算法的桥梁工程研究

提出1种结合差分进化-自适应Metropolis算法(DREAM KZS)和克里金模型的多链贝叶斯模型更新框架。该框架融合敏感度分析以减少参数维度并便于参数选择。此外,利用克里金模型来节省计算成本。该框架还采用DREAM KZS算法与多采样策略,通过考虑模型参数、测量和模型输出之间的交叉协方差来增强后验分布的探索效率。通过增加样本多样性同时减少并行链的数量,实现快速准确的模型更新。结果表明通过对简支梁的数值研究,验证DREAM KZS算法的收敛速度和精度,提出的DREAM KZS贝叶斯模型更新框架在六跨铁路高架桥的模型更新应用中表现出优异的性能,由于需要重复的模型评估,高度耗时和高保真度的有限元模型阻碍有效的模型更新。

A multi-fidelity simulation method research on front variable area bypass injector of an adaptive cycle engine

Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Computational Fluid Dynamics(CFD) simulation. This simulation method proposes a form of Front-VABI characteristic and its matching calculation method between it and the ACE performance model, constructs a coupling method between the(2-D) Front-VABI model and the(0-D) ACE performance model. The result shows, when ACE works in triple bypass mode, the approximate model cannot account for the effect of FrontVABI pressure loss on Core Driven Fan Stage(CDFS) design pressure ratio, and the calculated error of high-pressure turbine inlet total temperature is more than 40 K in mode transition condition(the transition operating condition between triple bypass mode and double bypass mode). In double bypass mode, the approximate model can better simulate the performance of FrontVABI by considering the local loss of area expansion. This method can be applied to the performance-optimized design of Front-VABI and the ACE control law design during mode transition.

基于自然驾驶数据的车辆自适应巡航系统控制方法

在常规的基于模型预测控制自适应巡航系统的算法中,舒适性约束均是采用固定加速度的形式,没有深入考虑不同速度下的加速度极值范围问题,这会导致在追求安全性和速度跟随性情况下,系统加速度和冲击度变化过大,从而降低了驾乘的舒适性。根据NGSIM自然驾驶数据集所得到了车辆的速度-加速度关系,并以此作为加速度的约束目标,将常规模型预测控制的定约束改为随速度变化的变约束,从而达到根据自然驾驶数据提高跟车舒适性的目的,并通过仿真试验验证了该方法的合理性。研究结果表明:该方法可使ACC系统在保证安全跟车和速度跟随的同时,使得加速度和冲击度波动变小;最大冲击度相比定加速度约束的模型预测控制减小了14.96%,可有效改善ACC系统的舒适性。

基于参数摄动模型的航空发动机鲁棒弹性自适应控制

航空发动机特性随飞行条件和工作状态变化,在复杂工作环境中同时存在模型不确定性和控制器自身参数变化问题,很大程度上影响整个飞行包线的控制性能。为此,文章提出一种基于参数摄动模型的航空发动机鲁棒弹性自适应控制方法。其首先建立了航空发动机参数摄动结构化模型;然后针对被控对象模型的不确定性和控制器增益的摄动,利用李雅普诺夫稳定性(Lyapunov stability)理论和线性矩阵不等式约束,设计了增益摄动有界但上界未知时的鲁棒弹性自适应控制律,将控制器的设计问题转化为线性矩阵不等式的可行解问题,使控制器的设计仅依赖线性矩阵不等式解矩阵的存在性,并证明了算法的稳定性。在此基础上,文章对飞行包线内发动机不同工作状态进行控制仿真验证。结果显示,调节时间小于1.8 s,超调量小于5%,这表明所设计控制器具有良好的稳定性和控制性能。