Robust model predictive control with randomly occurred networked packet loss in industrial cyber physical systems

For a class of linear discrete-time systems that is subject to randomly occurred networked packet loss in industrial cyber physical systems, a novel robust model predictive control method with active compensation mechanism was proposed. The probability distribution of packet loss is described as the Bernoulli distributed white sequences. By using the Lyapunov stability theory, the existing sufficient conditions of the controller are derived from solving a group of linear matrix inequalities. Moreover, dropout-rate with uncertainty and unknown dropout-rate are also considered, which can greatly reduce the conservativeness of the controller. The designed robust model predictive control method not only efficiently eliminates the negative effects of the networked data loss in industrial cyber physical systems but also ensures the stability of closed-loop system. Two examples were provided to illustrate the superiority and effectiveness of the proposed method.

Optimal dispatching method for integrated energy system based on robust economic model predictive control considering source-load power interval prediction

Effective source-load prediction and reasonable dispatching are crucial to realize the economic and reliable operations of integrated energy systems(IESs).They can overcome the challenges introduced by the uncertainties of new energies and various types of loads in the IES.Accordingly,a robust optimal dispatching method for the IES based on a robust economic model predictive control(REMPC)strategy considering source-load power interval prediction is proposed.First,an operation model of the IES is established,and an interval prediction model based on the bidirectional long short-term memory network optimized by beetle antenna search and bootstrap is formulated and applied to predict the photovoltaic power and the cooling,heating,and electrical loads.Then,an optimal dispatching scheme based on REMPC is devised for the IES.The source-load interval prediction results are used to improve the robustness of the REPMC and reduce the influence of source-load uncertainties on dispatching.An actual IES case is selected to conduct simulations;the results show that compared with other prediction techniques,the proposed method has higher prediction interval coverage probability and prediction interval normalized averaged width.Moreover,the operational cost of the IES is decreased by the REMPC strategy.With the devised dispatching scheme,the ability of the IES to handle the dispatching risk caused by prediction errors is enhanced.Improved dispatching robustness and operational economy are also achieved.

An Improved Robust Model Predictive Control Approach to Systems with Linear Fractional Transformation Perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.

ON THE ROBUSTNESS OF A GENERALIZED PREDICTIVE CONTROLLER

In this paper, we present a quantitative analysis of the robustness of a generalized predictive controller. The result of stability analysis shows that, under a specific bounded modelling error, the closed-loop system is BIBO stable in the presence of unmodelled dynamics.

Robust predictive control of polytopic uncertain systems with both state and input delays

A robust model predictive control algorithm for discrete linear systems with both state and input delays subjected to constrained input control is presented, where the polytopic uncertainties exist in both state matrices and input matrices. The algorithm optimizes an upper bound with respect to a state feedback control law. The feedback control law is presented based on the construction of a parameter-dependent Lyapunov function. The above optimization problem can be formulated as a LMI-based optimization. The feasibility of the optimization problem guarantees that the algorithm is robustly stable. The simulation results verify the effectiveness of the proposed algorithm.

THE PRINCIPLE OF ROBUSTNESS IN GENERALIZED PREDICTIVE CONTROL

This paper deeply analyzes the closed-loop nature ofGPCin the fram ework ofinter- nalm odelcontrol(IMC) theory. A new sort ofrelation lies in the feedback structure so that robustreason can be satisfactorily explained. The resultissignificantbecause the previous con- clusions are only applied to open-loop stable plant(orm odel).

Robust model predictive control for continuous uncertain systems with state delay

This paper is concerned with robust model predictive control for linear continuous uncertain systems with state delay and control constraints, A piecewise constant control sequence is calculated by minimizing the upper-bound of the infinite horizon quadratic cost function, At each sampling time, the sufficient conditions for the existence of the model predictive control are derived, and expressed as a set of linear matrix inequalities. The robust stability of the closed-loop svstems is guaranteed bv the proposed design method. A numerical example is given to illustrate the main results.

Robust Control Design of Wheeled Inverted Pendulum Assistant Robot

This paper examines the design concept and mobile control strategy of the human assistant robot I-PENTAR(inverted pendulum type assistant robot). The motion equation is derived considering the non-holonomic constraint of the twowheeled mobile robot. Different optimal control approaches are applied to a linearized model of I-PENTAR. These include linear quadratic regulator(LQR), linear quadratic Gaussian control(LQG), H_2 control and H_∞ control. Simulation is performed for all the approaches yielding good performance results.

The Price Model of Aquatic Products Based on Predictive Control Theory

This paper discusses a disequilibrium cobweb model of price of aquatic products, and applies predictive control theory, so that the system operates stably, and the deviation between supply and demand of aquatic products smoothly tracks the pre-given target. It defines the supply and demand change model, and researches the impact of parameter selection in this model on dynamic state and robustness of the system. I conduct simulation by Matlab software, to get the response curve of this model. The results show that in the early period of commodities coming into the market, affected by lack of market information and many other factors, the price fluctuates greatly in a short time. The market will gradually achieve balance between supply and demand over time, and the price fluctuations in the neighbouring two periods are broadly consistent. The increase in model parameter can decrease overshoot, to promote the stability of system, but the slower the dynamic response, the longer the deviation between supply and demand to accurately track a given target. Therefore, by selecting different parameters, the decision-makers can establish different models of supply and demand changes to meet the actual needs, and ensure stable development of market. Simulation results verify the excellent performance of this algorithm.

Control method on serial type pump-valve coordinated electro-hydraulic servo system

In order to compromise the conflicts between control accuracy and system efficiency of conventional electro-hydraulic servo systems,a novel pump-valve coordinated electro-hydraulic servo system was designed and a corresponding control strategy was proposed.The system was constituted of a pumpcontrolled part and a valve-controlled part,the pump controlled part is used to adjust the flow rate of oil source and the valve controlled part is used to complete the position tracking control of the hydraulic cylinder.Based on the system characteristics,a load flow grey prediction method was adopted in the pump controlled part to reduce the system overflow losses,and an adaptive robust control method was adopted in the valve controlled part to eliminate the effect of system nonlinearity and parametric uncertainties due to variable hydraulic parameters and system loads on the control precision.The experimental results validated that the adopted control strategy increased the system efficiency obviously with guaranteed high control accuracy.

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。

Design of Networked Control Systems with Bounded Arbitrary Time Delays

This paper is mainly concerned with the model predictive control （MPC） of networked control systems （NCSs） with uncertain time delay and data packets disorder. The network-induced time delay is described as bounded and arbitrary process. For the usual state feedback controller, by considering all the possibilities of delays, an augmented state space model of the closed-loop system, which characterizes all the delay cases, is obtained. The stability conditions are given according to the Lyapunov method based on this augmented model. The stability property is inherited in MPC which explicitly considers the physical constraints. A numerical example is given to demonstrate the effectiveness of the proposed MPC.

基于扰动预测的网联车鲁棒协同巡航预测控制

针对车辆巡航过程中前车未来加速度不确定性引起的控制问题,提出了一种用于解决网联车巡航问题的鲁棒模型预测控制(Model predictive control,MPC)算法。采用车辆误差模型,将前车预测加速度与实际加速度之间的偏差视为加性扰动。根据已知的物理约束,先通过滚动预测的方法得到更紧凑的扰动多面体,然后对原系统的约束进行紧缩处理以降低传统tube MPC的保守性。基于传统tube MPC在控制器中引入前馈补偿,通过l2增益特性条件得到相应的线性增益,提出一种基于滚动扰动预测的tube MPC算法,并且证明了该算法是输入到状态稳定的。通过5辆车的数值仿真,验证了该算法的有效性。

基于状态观测器的增量式预测控制

通过状态观测器的状态估计代替内部模型的状态反馈,推导了基于状态观测器的增量式模型预测控制算法,通过增广状态估计实现实测的输出反馈对内部模型进行修正;其次,当模型预测控制内部模型与实际被控对象存在模型失配时,得到存在模型失配的闭环系统的状态方程,研究了闭环系统的鲁棒稳定性;最后,给出一个线性对象和一个非线性对象的仿真示例,并对线性示例进行模型失配下的稳定性分析,仿真结果表明该控制算法具有较好的控制效果,并具有一定的鲁棒稳定性。

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。

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。

执行器随机失效系统鲁棒预测容错切换控制

针对一类具有执行器随机失效问题的离散线性系统,提出一种基于故障概率情况下的鲁棒预测容错切换控制方法。首先,将工业过程建立成新型多自由度状态空间模型,设计含有故障概率的容错控制器;其次,引入系统故障和其恢复时的随机概率,利用李雅普诺夫判据给出基于线性矩阵不等式形式的稳定性条件,再通过指数稳定的相关证明求解出不同执行器切换时的稳定条件,以保证系统故障时容错控制与无故障时常规控制间的切换;然后,控制器设计时还充分考虑了设定值变化时所产生的跟踪误差带来的影响。最后,通过仿真结果验证了所提方法的可行性。

执行器部分失效的鲁棒模糊容错切换控制

针对一类具有不确定性、外部扰动和执行器部分失效的非线性工业过程,提出一种鲁棒模糊容错切换控制方法。首先,构建新型多自由度T-S模糊状态空间模型,在此基础上,结合执行器部分失效的情况设计控制律。其次,给出基于线性矩阵不等式(LMI)的容错切换条件和系统稳定条件,并通过当前系统状态和稳定性条件求取其子模型的控制律及相应权值。最后,通过对连续搅拌釜式反应器的仿真研究来验证该文方法的有效性。

城轨列车全自动运行系统噪声子空间鲁棒预测控制方法

为建立与实际运行状态更吻合、预测精确度更高的城轨列车全自动运行(fully automatic operation, FAO)系统预测控制模型,根据城轨列车运行的历史数据,综合考虑城轨列车运行过程中的噪声干扰,采用子空间辨识方法得到含噪声的预测控制模型,控制过程不断加入实时采集的数据,迭代模型辨识的历史数据,在线更新模型参数,得到抗干扰能力较强的城轨列车FAO系统噪声子空间鲁棒预测控制器。分别采用幅值为0、2、5、10 km/h的随机干扰噪声,在软件MATLAB中进行仿真试验,对比分析城轨列车FAO系统噪声子空间鲁棒预测器与传统子空间预测控制器的预测精度。结果表明:在随机噪声干扰下,城轨列车FAO系统噪声子空间鲁棒预测控制器的预测精度较高,噪声幅值为10 km/h时预测精度比传统子空间预测控制器提高14.21%。城轨列车FAO系统噪声子空间鲁棒预测控制器可在强干扰运行状态下高精度跟踪给定运行曲线。