Kernel-Based State-Space Kriging for Predictive Control

In this paper, we extend the state-space kriging(SSK) modeling technique presented in a previous work by the authors in order to consider non-autonomous systems. SSK is a data-driven method that computes predictions as linear combinations of past outputs. To model the nonlinear dynamics of the system, we propose the kernel-based state-space kriging(K-SSK), a new version of the SSK where kernel functions are used instead of resorting to considerations about the locality of the data. Also, a Kalman filter can be used to improve the predictions at each time step in the case of noisy measurements. A constrained tracking nonlinear model predictive control(NMPC) scheme using the black-box input-output model obtained by means of the K-SSK prediction method is proposed. Finally, a simulation example and a real experiment are provided in order to assess the performance of the proposed controller.

Predictive functional control based on fuzzy T-S model for HVAC systems temperature control

In heating, ventilating and air-conditioning （HVAC） systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno （T-S） fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

Predictive cruise control for heavy trucks based on slope information under cloud control system

With the advantage of fast calculation and map resources on cloud control system(CCS), cloud-based predictive cruise control(CPCC) for heavy trucks has great potential to improve energy efficiency, which is significant to achieve the goal of national carbon neutrality. However, most investigations focus on the on-board predictive cruise control(PCC) system,lack of research on CPCC architecture under CCS. Besides, the current PCC algorithms have the problems of a single control target and high computational complexity, which hinders the improvement of the control effect. In this paper, a layered architecture based on CCS is proposed to effectively address the realtime computing of CPCC system and the deployment of its algorithm on vehicle-cloud. In addition, based on the dynamic programming principle and the proposed road point segmentation method(RPSM), a PCC algorithm is designed to optimize the speed and gear of heavy trucks with slope information. Simulation results show that the CPCC system can adaptively control vehicle driving through the slope prediction, with fuel-saving rate of 6.17% in comparison with the constant cruise control. Also,compared with other similar algorithms, the PCC algorithm can make the engine operate more in the efficient zone by cooperatively optimizing the gear and speed. Moreover, the RPSM algorithm can reconfigure the road in advance, with a 91% roadpoint reduction rate, significantly reducing algorithm complexity.Therefore, this study has essential research significance for the economic driving of heavy trucks and the promotion of the CPCC system.

Model Predictive Current Control with Adaptive-adjusting Timescales for PMSMs

A model predictive current control(MPCC)with adaptive-adjusting method of timescales for permanent magnet synchronous motors(PMSMs)is proposed in this paper to improve the dynamic response and prediction accuracy in transient-state,while lessening the computational burden and improving the control performance in steady-state.The timescale characteristics of different parts of MPCC,such as signal sampling,prediction calculation,control output,model error correction,are analyzed,and the algorithm architecture of MPCC with multi-timescale is proposed.The difference between reference and actual speed,and the change rate of actual speed are utilized to discriminate the transient state of speed change and load change,respectively.Adaptive-adjusting method of control period and prediction stepsize are illustrated in detail after operation condition discrimination.Experimental results of a PMSM are presented to validate the effectiveness of proposed MPCC.In addition,comparative evaluation of single-step MPCC with fixed timescale and proposed MPCC is conducted,which demonstrates the superiority of proposed control strategy.

Multi-loop Constrained Iterative Model Predictive Control Using ARX -PLS Decoupling Structure

A multi-loop constrained model predictive control scheme based on autoregressive exogenous-partial least squares(ARX-PLS) framework is proposed to tackle the high dimension, coupled and constraints problems in industry processes due to safety limitation, environmental regulations, consumer specifications and physical restriction. ARX-PLS decoupling character enables to turn the multivariable model predictive control(MPC) controller design in original space into the multi-loop single input single output(SISO) MPC controllers design in latent space.An idea of iterative method is applied to decouple the constraints latent variables in PLS framework and recursive least square is introduced to identify ARX-PLS model. This algorithm is applied to a non-square simulation system and a stirred reactor for ethylene polymerizations comparing with adaptive internal model control(IMC) method based on ARX-PLS framework. Its application has shown that this method outperforms adaptive IMC method based on ARX-PLS framework to some extent.

Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System

This paper is concerned with the distributed model predictive control(MPC) problem for a class of discrete-time Markovian jump linear systems(MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities(LMIs). An iterative algorithm is developed to achieve the online computation. Finally,a simulation example is employed to show the effectiveness of the proposed algorithm.

Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

Temporal Prediction of Aircraft Loss-of-Control: A Dynamic Optimization Approach

Loss of Control (LOC) is the primary factor responsible for the majority of fatal air accidents during past decade. LOC is characterized by the pilot’s inability to control the aircraft and is typically associated with unpredictable behavior, potentially leading to loss of the aircraft and life. In this work, the minimum time dynamic optimization problem to LOC is treated using Pontryagin’s Maximum Principle (PMP). The resulting two point boundary value problem is solved using stochastic shooting point methods via a differential evolution scheme (DE). The minimum time until LOC metric is computed for corresponding spatial control limits. Simulations are performed using a linearized longitudinal aircraft model to illustrate the concept.

基于蒙特卡洛仿真的航空用测控装置可靠性预计

文章研究了某面向航空应用的高可靠性测控装置的可靠性预计问题。根据测控装置设计原理图建立了可靠性框图,基于GJB/Z299C手册和应力分析法得到了元器件可靠性数据,建立了基于蒙特卡洛仿真方法的系统可靠性预计数学模型,预计了测控装置的可靠性指标,并通过分析查找可靠性薄弱环节,改进了设计方案,提高了可靠性,最终达到设计要求。文章研究表明,蒙特卡洛仿真方法对于混连复杂电子系统的可靠性预计具有较好的实用性。

外包钢壳混凝土拱形桥塔节段拼装几何姿态预测研究

赣州市集结大桥主桥为外包钢壳混凝土拱形桥塔斜拉桥,为确保钢混组合拱形桥塔节段拼装精准合龙,采用MIDAS Civil软件建立拱形桥塔空间几何模型,分析外包钢壳和混凝土湿重对桥塔变形的影响,采用切线初始位移法对桥塔施工阶段位移进行预测,通过求解制造线形对桥塔待拼装节段进行预偏修正,并与实测数据进行对比。结果表明:外包钢壳能显著减小桥塔变形;施工阶段桥塔变形主要由混凝土湿重引起,临时支撑能有效减小混凝土浇筑产生的横向变形。基于切线初始位移法的几何姿态预测方法能有效预测桥塔拼装全过程几何姿态,实测成桥阶段桥塔各节段最大偏位为6 mm,小于施工控制要求,具有较高的实施精度,可保证成桥状态下桥塔几何姿态的准确性。

基于双估计强化学习结合前向预测控制的自动驾驶运动控制研究

运动控制研究是实现自动驾驶目标的重要组成部分,针对传统强化学习算法在求解中因单步决策局限而导致控制序列次优的问题,提出了一种基于双估计强化学习算法及前向预测控制方法结合的运动控制框架(DEQL-FPC)。在该框架中引入双估计器以解决传统强化学习方法动作值过估计问题并提高训练优化的速度,设计前向预测多步决策方法替代传统强化学习的单步决策,以有效提高全局控制策略的性能。通过虚拟驾驶环境仿真,证明了该控制框架应用在自动驾驶汽车的路径跟踪以及安全避障的优越性,保证了运动控制中的精确性、安全性、快速性以及舒适性。

基于MPC的智能车辆路径规划与跟踪控制

针对在障碍物环境下的避障路径动态规划效果较差,以及在面对复杂工况和曲率较大的路况时,跟踪控制的效果仍然不理想等问题,本文以智能车辆为研究对象,提出了一种模型预测控制(MPC)结合人工势场(APF)算法的路径规划跟踪系统。将改进的势场模型函数引入到MPC的目标函数和约束中,设计了基于MPC和APF的避障路径动态规划器。。运用模糊控制对MPC的车辆横向路径跟踪控制器的权重系数进行优化。仿真结果表明:在干燥路面下,与MPC控制器相比,模糊MPC路径跟踪控制器的最大横向偏差减少19.14%。在湿润路面下,模糊MPC控制器最大横向偏差减少0.55 m。基于MATLAB/Simulink与Carsim软件搭建避障路径规划与跟踪控制联合仿真模型,选择动态障碍物不同速度进行障碍物路径动态规划及跟踪控制仿真试验。实验结果表明:跟踪规划路径过程中的最大横向偏差约为0.170 m,说明规划的避障路径能够安全有效地避开障碍物。

基于城市超车工况的智能车辆避障规划方法研究

在城市道路行驶中,超车驾驶既是常见的驾驶行为,也是常见的交通事故工况之一。基于改进人工势场法(artificial potential field method)与模型预测控制(model predicition control)提出了一种避障规划控制方法。首先,构建目标点引力势场函数、道路及车道线势场函数以及障碍物车辆势场函数;其次,基于势场函数建立统一化模型函数;最后,对系统进行模拟实验评估。结果表明,提出的避障规划控制方法能够生成安全、平滑的路径,其前轮转角变化范围在[−0.67°,1.02°],质心侧偏角变化范围在[−0.22°,0.32°],具有稳定的控制能力。

大水位变幅下多级闸控河渠自适应控制方法研究

【目的】提高多级闸控河渠水位变幅较大时的河渠水位控制精度,减小河渠水位振荡,提升控制算法的收敛效率。【方法】利用河渠实时水位动态修正ID模型参数并调整闸坝启闭频率,提出自适应预测控制(APC)算法,对比分析6种工况下不同控制算法的精度与效率。【结果】与基于ID模型的线性二次型控制(LQR)算法与模型预测控制(MPC)算法相比,APC算法可分别缩短19%~32%和8%~40%的调控时长,可减少47%~97%和14%~93%的河渠水位累计波动,可减小24%~77%和5%~59%的河渠水位平均绝对偏差。【结论】APC算法提高了多级闸控河渠水位的控制精度和算法的收敛性,可在不同的河渠水位变幅下保持良好的控制性能,能够为多级闸控河渠的智慧水利建设提供技术支持。

基于改进坐标变换的某型航空装备成像画面研究

为帮助某型航空装备飞行员排除干扰快速精准地锁定目标,提出一种基于改进坐标变换的某型装备成像画面预测方法。将其同仿真软件的图形用户界面功能相结合开发了一套预测系统,通过利用该套预测系统进行目标成像画面预测,仿真输出结果可辅助飞行员排除干扰从而提升制导率。结果表明:该方法填补了某型装备成像画面预测方面的技术空白,具有实际意义与推广价值。

基于MPC的直接电流控制光伏最大功率点跟踪

传统光伏电池最大功率点追踪技术(MPPT)能追踪光伏系统的最大功率点(MPP)并使系统运行在MPP点。但是,在追踪过程中由于追踪速度和追踪精度的矛盾,使得系统在MPP点附近发生振荡。提出一种模型预测直接电流控制(MPC-DCC)和改进了的电导增量最大功率点追踪技术。系统采用Boost升压型电路进行DC-DC控制,先由改进了的电导增量法得到参考电流,再由MPC环节进行直接电流控制。对所研究的系统进行了Simulink仿真和硬件在环实验验证,结果表明该方法能够使用改进电导增量法快速准确找到MPPT最大功率点,并发挥MPC-DCC的控制器带宽及控制精度高的双重优点。

基于模型预测控制的车辆避障路径规划与跟踪

针对智能车辆避障路径规划问题,提出一种基于改进人工势场法的避障路径规划设计方法,首先在传统斥力势场函数中引入车辆速度信息,并设计了车道中心线、道路边界势场,以及路径避障规划控制器;然后针对智能车辆路径跟踪控制问题,建立了三自由度车辆动力学模型,分别采用线性时变模型预测控制和反向传播-比例-积分-微分(BP-PID)控制算法设计横、纵向跟踪控制器,对车辆的横向位置和纵向速度进行跟踪;最后基于CarSim/Simulink联合仿真平台,在双移线工况和直线工况下,验证设计方法的有效性。仿真结果表明,采用改进的人工势场法可获得安全可靠的避障路径,所设计的横纵向跟踪控制器具有良好的跟踪性能。

基于元器件应力法的微波等离子体质谱仪控制系统可靠性预计

在微波等离子体质谱仪控制系统的设计阶段,元器件应力法是一种有效的获取设计方案可靠性指标的预计方法,然而,元器件应力法直接求和获取同类型元器件总失效率的方法会导致总失效率标准差的线性上升,给预计结果带来不确定性。本文根据总失效率及其标准差采用基于置信上限的估计方法,使预计结果具有更高的置信水平,最后实现了控制系统的可靠性预计,并根据预计结果提出了优化方案。

基于FDC氢燃料电池堆在线智能监测系统

在氢燃料电池汽车中,Fuel Cell DC-DC变换器(FDC)在氢燃料电池堆(氢堆)和逆变器之间起着关键的接口作用,其输出直流母线电压的稳定是保证电机等负载正常工作的关键环节。为此本文提出一种新型滑模预测控制,首先改进FDC拓扑结构并建立基于滑模面的预测模型,然后设计基于滑模面价值函数提高FDC跟踪精度;在此基础上为实现氢堆在线智能监测,进一步采用变步长线性插值查表法对氢堆阻抗进行在线精确估算,首先通过数字控制使FDC自生成变频的微小正弦谐波,并根据FDC输入侧电压电流采样,估算氢堆多频段阻抗值;然后采用变步长分段线性插值查表法,对比阻抗估算值与设计标准值,评判氢堆当前状态,实现氢堆在线智能监测。最后进行仿真及实验验证,验证数据显示:①当实验参考电流设定为100 A,传统PI控制时系统电流超调及响应时间分别为20 A、15 ms,应用滑模预测控制时系统电流超调及响应时间分别为1.5 A、2.5 ms;②额定工况下,FDC对自生成谐波的跟踪精度能控制在2%以内;③采用变步长线性插值查表法能实现氢堆阻抗的精确估算,验证算法的有效性。

电力动态负荷数据概率区间预测方法设计

电力负荷具有较大的波动性,且负荷数据存在噪声、缺失或异常值等问题,影响概率区间预测结果的准确性。为此,提出一种优化的电力动态负荷数据概率区间预测方法,并对该方法展开验证分析。根据当前预测需求及标准的变化,对电力动态负荷数据预处理,计算动态负荷数据概率感知区间合适值,压缩概率感知区间范围。采用多层级的方式,细化负荷数据概率区间预测范围。设计电力动态负荷数据概率区间预测流程,采用多目标PF-Elman集成处理,实现概率区间预测。测试结果表明,此次所设计的动态负荷数据概率区间预测方法最终得出的APFE值均被较好地控制在4以下,当前所设计的预测方法更加灵活、高效,预测的效率更佳,预测结果的区间覆盖率都在95%以上,具有实际的应用价值。