Uncertainty and disturbance estimator-based model predictive control for wet flue gas desulphurization system

Wet flue gas desulphurization technology is widely used in the industrial process for its capability of efficient pollution removal.The desulphurization control system,however,is subjected to complex reaction mechanisms and severe disturbances,which make for it difficult to achieve certain practically relevant control goals including emission and economic performances as well as system robustness.To address these challenges,a new robust control scheme based on uncertainty and disturbance estimator(UDE)and model predictive control(MPC)is proposed in this paper.The UDE is used to estimate and dynamically compensate acting disturbances,whereas MPC is deployed for optimal feedback regulation of the resultant dynamics.By viewing the system nonlinearities and unknown dynamics as disturbances,the proposed control framework allows to locally treat the considered nonlinear plant as a linear one.The obtained simulation results confirm that the utilization of UDE makes the tracking error negligibly small,even in the presence of unmodeled dynamics.In the conducted comparison study,the introduced control scheme outperforms both the standard MPC and PID(proportional-integral-derivative)control strategies in terms of transient performance and robustness.Furthermore,the results reveal that a lowpass-filter time constant has a significant effect on the robustness and the convergence range of the tracking error.

Proportion integral-type active disturbance rejection generalized predictive control for distillation process based on grey wolf optimization parameter tuning

The high-purity distillation column system is strongly nonlinear and coupled,which makes it difficult to control.Active disturbance rejection control(ADRC)has been widely used in distillation systems,but it has limitations in controlling distillation systems with large time delays since ADRC employs ESO and feedback control law to estimate the total disturbance of the system without considering the large time delays.This paper designs a proportion integral-type active disturbance rejection generalized predictive control(PI-ADRGPC)algorithm to control the distillation column system with large time delay.It replaces the PD controller in ADRC with a proportion integral-type generalized predictive control(PI-GPC),thereby improving the performance of control systems with large time delays.Since the proposed controller has many parameters and is difficult to tune,this paper proposes to use the grey wolf optimization(GWO)to tune these parameters,whose structure can also be used by other intelligent optimization algorithms.The performance of GWO tuned PI-ADRGPC is compared with the control performance of GWO tuned ADRC method,multi-verse optimizer(MVO)tuned PI-ADRGPC and MVO tuned ADRC.The simulation results show that the proposed strategy can track reference well and has a good disturbance rejection performance.

Disturbance rejection tube model predictive levitation control of maglev trains

Magnetic levitation control technology plays a significant role in maglev trains.Designing a controller for the levitation system is challenging due to the strong nonlinearity,open-loop instability,and the need for fast response and security.In this paper,we propose a Disturbance-Observe-based Tube Model Predictive Levitation Control(DO-TMPLC)scheme combined with a feedback linearization strategy for the levitation system.The proposed strategy incorporates state constraints and control input constraints,i.e.,the air gap,the vertical velocity,and the current applied to the coil.A feedback linearization strategy is used to cancel the nonlinearity of the tracking error system.Then,a disturbance observer is implemented to actively compensate for disturbances while a TMPLC controller is employed to alleviate the remaining disturbances.Furthermore,we analyze the recursive feasibility and input-to-state stability of the closed-loop system.The simulation results indicate the efficacy of the proposed control strategy.

Model-Based Adaptive Predictive Control with Visual Servo of a Rotary Crane System

This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.

Model Predictive Control for Electrical Drive Systems-An Overview

This paper reviews the classification and application of the model predictive control(MPC)in electrical drive systems.Main attention is drawn to the discrete form of MPC,i.e.finite control set model predictive control(FCS-MPC),which outputs directly the switching states of power converters.To show the diversity and simple realization with various control performances of the strategy,in this paper,several different FCS-MPCs with their working mechanisms are introduced.Comparison of FCS-MPC with conventional control strategies for electric drives is presented.Furthermore,extensive control issues,e.g.encoderless control and disturbance observation are also included in this work.Finally,the trend of research hot topics on MPC is discussed.

Refined disturbance observer based prescribed performance fixed-time control of high-speed EMS trains with track irregularities

High-speed Electromagnetic Suspension(EMS)train is continuously impacted by the irregularity of the track,which worsens the levitation performance of the train.In this paper,a composite control scheme for the EMS is proposed to suppress track irregularities by integrating a Refined Disturbance Observer(RDO)and a Prescribed Performance Fixed-Time Controller(PPFTC).The RDO is designed to estimate precisely the track irregularities and lumped disturbances with uncertainties and exogenous disturbances in the suspension system,and reduce input chattering by applying to the disturbance compensation channel.PPFTC is designed to converge the suspension air gap error to equilibrium point with prescribed performance by completing error conversion,and solve the fast dynamic issue of EMS.And the boundary of overshoot and steady-state is limited in the ranged prescribed.A theoretical analysis is conducted on the stability of the proposed control method.Finally,the effectiveness and reasonability of the proposed composite anti-disturbance control scheme is verified by simulation results.

Model Predictive Control for Steering System of Water-Jet Propulsion

Water-jet propulsion is a widely applied ship propulsion technology.Its steering control system has an important impact on manoeuvre performance.In this paper,transfer function model is firstly established on the basis of mechanism analysis of water-jet steering system.Then,by considering the variability of model parameters and input constraints in practical operation,a model predictive controller is designed for steering system control.Subsequently,model based disturbance observer is employed in an attempt to reject environmental disturbances.The performance of the proposed model predictive control (MPC) scheme for a particular steering system is compared with that of conventional proportional integral derivative (PID) control strategy.Simulation results demonstrate that the proposed model predictive controller outperforms conventional PID controller,particularly in robustness,response delay and tracking accuracy.

Modeling and Decentralized Predictive Control of Ejector Circulation‑Based PEM Fuel Cell Anode System for Vehicular Application

The dynamic response of fuel cell vehicle is greatly affected by the pressure of reactants.Besides,the pressure difference between anode and cathode will also cause mechanical damage to proton exchange membrane.For maintaining the relative stability of anode pressure,this study proposes a decentralized model predictive controller(DMPC)to control the anodic supply system composed of a feeding and returning ejector assembly.Considering the important influence of load current on the system,the piecewise linearization approach and state space with current-induced disturbance compensation are com-paratively analyzed.Then,an innovative switching strategy is proposed to prevent frequent switching of the sub-model-based controllers and to ensure the most appropriate predictive model is applied.Finally,simulation results demonstrate the better stability and robustness of the proposed control schemes compared with the traditional proportion integration differentia-tion controller under the step load current,variable target and purge disturbance conditions.In particular,in the case of the DC bus load current of a fuel cell hybrid vehicle,the DMPC controller with current-induced disturbance compensation has better stability and target tracking performance with an average error of 0.15 kPa and root mean square error of 1.07 kPa.

State Predictive Model Following Control System for Linear Time Delays

In this paper, we propose a new state predictive model following control system （MFCS）. The considered system has linear time delays. With the MFCS method, we obtain a simple input control law. The bounded property of the internal states for the control is given and the utility of this control design is guaranteed. Finally, an example is given to illustrate the effectiveness of the proposed method.

Predictive Control of Quad-Rotor Delivering Unknown Time-Varying Payloads Based upon Extended State Observer

In this paper, robust control problem is addressed for quad-rotor delivering unknown time-varying payloads. Firstly, the model of a quad-rotor carrying payloads is built. Dynamics of the payloads are treated as disturbances and added into the model of the quad-rotor. Secondly, to enhance system robust-ness, the extended state observer (ESO) is applied to estimate the disturbances from the payloads for feedback compensation. Then a type of predictive controller targeting multiple-input-multiple-output (MIMO) system is developed to degrade the influences caused by sudden changes from load-ing/dropping of the payloads. Finally, by making comparison with the con-ventional cascade proportional-integral-derivative (CPID) and the sliding mode control (SMC) approaches, superiority of the scheme developed is va-lidated. The simulation results indicate that the CPID method shows poor performance on attitude stabilization and the SMC shows input chattering phenomenon even it can achieve satisfied control performances.

基于广义比例积分观测器的永磁同步电机鲁棒谐振预测电流控制

针对永磁同步电机(PMSM)采用无差拍预测电流控制(DPCC)时,性能易受电机参数失配及死区效应的影响,该文提出一种基于广义比例积分(GPI)观测器的鲁棒谐振预测电流控制(RRPCC)方法。首先,分析PMSM参数失配将产生非周期扰动及逆变器死区效应会导致周期扰动,进而建立带有非周期和周期性扰动的PMSM精确数学模型;其次,基于内模原理,将包含扰动频率的谐振多项式嵌入电流预测模型,可有效地抑制周期性扰动;最后,采用GPI观测器估计由参数失配引起的集总扰动,并通过前馈补偿消除非周期扰动。实验结果表明,该方法能提高系统面对非周期及周期性扰动时的鲁棒性。

计及采样扰动抑制的电压源逆变器三矢量无模型预测电流控制方法

针对传统电压源逆变器无模型预测电流控制(model-free predictive current control,MFPCC)方法存在电流纹波大、电流梯度更新停滞以及预测性能易受采样扰动影响的问题。该文提出一种计及采样扰动的三矢量MFPCC方法。在一个控制周期应用3个基本矢量,并根据价值函数计算矢量作用时间,降低了输出电流纹波;其次,通过建立不同矢量作用下的电流梯度方程组,实现电流梯度数据的实时更新,消除了停滞现象;再次,分析采样扰动对MFPCC的影响,采用扩张状态观测器估计采样扰动以补偿预测电流控制,抑制其对输出电流的影响。最后,通过仿真和实验,对所提方法的有效性进行了验证。

新能源汽车整流器的抗扰模型预测控制策略研究

针对新能源汽车充电(G2V)三相整流器为研究对象,研究基于模型预测控制(model predictive control,MPC)的充电控制策略。传统的MPC算法需要准确的系统模型参数,而当控制器中使用的模型参数与主电路实际参数不匹配时,控制性能可能发生恶化,影响整流器充电控制性能。针对此问题,该文将系统参数不匹配作为扩张状态观测器(extended state observer,ESO)扩张出来的扰动项而进行估计,并将扰动进行补偿,从而设计一种基于ESO的MPC充电控制策略。该方法仅使用了系统的输入和输出数据,而不需要精确的系统模型,因此即使模型参数不匹配时,ESO也能够将不匹配项作为扰动而对预测电流进行准确估计,从而提高MPC对参数变化及不匹配的鲁棒性。仿真与实验结果验证了该方法的可行性和有效性。

海参捕捞机器人运动控制系统的仿真研究

随着水下机器人技术的发展,海参捕捞机器人将逐渐取代费时费力的人工捕捞作业。但是海参捕捞机器人的运动控制精度一直影响其运动稳定性和捕捞效率,一方面是由于海底的作业环境恶劣多变,机器人的结构功能复杂;另一方面是随着海参的累积,机器人的参数发生改变,原有控制模型的控制精度下降。为提高海参捕捞机器人的运动控制精度,在综合考虑机器人各种载荷的基础上,还考虑了海参对机器人造成的干扰,建立更加全面的机器人动力学模型;并运用模型预测控制理论和非线性干扰观测器对机器人的运动过程进行模拟分析。通过分别模拟机器人的定深下潜、运动姿态保持、路径跟踪和载重上升运动过程,定量分析机器人的运动控制精度,最后构建一个系统全面且精度较高的海参捕捞机器人运动控制系统。

刚性联接双电机系统功率平衡控制策略

针对刚性联接双电机系统因参数摄动和负载变化导致的系统输出功率不均衡问题,基于转矩闭环矢量控制系统,提出了一种自抗扰模型预测(ADRC-MPCC)转矩交叉耦合功率平衡控制策略。首先,建立刚性联接双电机系统的统一数学模型,基于该模型分析了功率不平衡产生的原因;其次,通过确定合适的转矩反馈补偿系数,提高了系统中双电机的同步性能;最后,设计自抗扰控制器对转速环、磁链环和转矩环的扰动进行估计和补偿,并通过简化模型预测控制方法对逆变器开关状态进行选择,减少了控制器的运算时间。仿真与实验结果表明,提出的控制策略实现了参数摄动和负载扰动情况下刚性联接双电机系统的输出功率平衡,验证了功率平衡控制策略的有效性。

永磁同步电机改进型鲁棒模型预测转矩控制

针对永磁同步电机传统模型预测转矩控制策略存在脉动大、抗干扰能力差、权重因子需要反复调试等缺点,提出了一种基于自抗扰控制器的改进型鲁棒模型预测转矩控制策略。首先,通过设计转速环自抗扰控制器,并引入一种连续且平滑的新型fal函数,进一步提高了自抗扰控制的控制性能。然后,设计了一种无权重系数的价值函数,优化系统控制性能的同时简化了设计过程,解决了权重因子调试的问题。最后,采用两步预测降低延时对控制系统的影响,提高了控制精度。仿真和实验结果表明,提出的改进型鲁棒模型预测转矩控制策略有效降低了系统脉动,提高了系统的控制性能和参数鲁棒性。

基于滑模扰动观测器的阀门开度预测控制

针对电动阀门在运行过程中,由于外部干扰、系统参数变化、摩擦力等非重复干扰的影响而难以达到高精度控制的问题,提出一种基于滑模扰动观测器的预测控制方法。通过对阀门驱动原理进行深入研究,构建阀门的数学模型,根据模型中电机电流与总负载扰动的正相关性,以电机电流、阀门转速为输入量,采用滑模扰动观测器对系统总负载扰动进行估计。设计模型预测控制器,对扰动估计值进行补偿,实现对阀门开度控制的准确性,鲁棒性以及对总扰动值跟踪的快速性。理论分析、仿真和试验表明:观测器仅0.001 s便跟踪到了设定值,收敛速度快,阀门开度精度误差在0.2%以内,完全达到控制目标,开度控制效果良好。可充分验证该控制方法在不同的负载扰动下,依然能使电动阀门保持良好的开度控制精度以及抗扰动能力。

电磁悬浮系统的改进线性自抗扰控制方法

单点电磁悬浮系统是常导电磁磁浮列车悬浮系统的关键控制单元。针对单点电磁悬浮系统因强干扰影响导致控制性能下降的问题,提出了一种改进型线性自抗扰控制(LADRC)方法。首先,基于电磁悬浮结构及原理建立了单点悬浮系统线性化模型,并对系统稳定性进行了分析。其次,基于自抗扰控制方法设计位置外环控制器以及基于PI调节器的电流内环控制器,对线性扩张状态观测器进行改进,设计两级级联的线性扩张状态观测器提高扰动估计精确度及速度,进而提高LADRC中扩张状态观测器对系统扰动的估计能力,并在频域上分析了改进型LADRC扰动估计性能和抑制能力的优越性。最后,搭建实验环境,对改进线性自抗扰控制方法的有效性进行验证。实验结果表明,所提的改进型LADRC不仅具有良好的位置跟踪性能,同时相比传统LADRC具有更强的抗扰动能力。

基于MPC和ESO-DO的四旋翼轨迹跟踪控制

针对扰动作用和模型不确定性下四旋翼无人机精确轨迹跟踪控制问题,提出了一种主动干扰抑制和模型预测控制(Model Predictive Control, MPC)策略。模型预测控制器通过扩张状态观测器(Extended State Observer, ESO)和扰动观测器(Disturbance Observer, DO)来估计和补偿干扰,从而实现位置环精确控制。在存在外部干扰和参数不确定性的情况下,通过仿真实验,证明了所提出的方法提高了对建模误差和干扰的鲁棒性,同时实现了对参考轨迹的平滑跟踪。

分段式双三相永磁直线同步电机的无模型电流预测控制

分段式结构的双三相永磁直线同步电机(PMLSM),动子穿入和穿出定子分段时,传统的无差拍预测电流控制(DPCC)易受反电动势动态扰动的影响,无法实现精确的电流跟踪和推力控制。同时,该电机模型的参数失配会增大DPCC电流和推力控制的难度。针对以上问题,该文提出了一种基于无模型电流预测控制(MFPCC)的电流控制策略。首先,构建了分段式双三相PMLSM的超局部模型。其次,基于该超局部模型设计了预测电流控制器,并设计扰动观测器以补偿反电动势动态扰动和参数失配的影响。最后,实验验证了所提MFPCC能够抑制分段式双三相PMLSM的反电动势和参数失配的扰动,有效提升参数鲁棒性、电流跟踪性能并降低推力波动。