Robust Predictive Control of Uncertain Systems with Time-delay Using Tight Sets of Predicted States

A robust model predictive control (MPC) algorithm for discrete time linear systems with time-delay (RPC-TDS) subjected to constrained input control is presented, where the polytopic uncertainties exist in state matrices and input matrices. In the algorithm the standard optimization of quadratic objective function has been transformed into optimization of sum of N+1 upper bounds of the quadratic objective function with respect to N control moves and a state feedback control law, where N is the control horizon. The feasibility of the optimization problem guarantees that the algorithm is robustly stable. The simulation results verify the effectiveness of the proposed algorithm.

Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model

An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is presented. In this approach, the minimization problem of the “worst-case” objective function is converted into the linear objective minimization problem in- volving linear matrix inequalities (LMIs) constraints. The state feedback control law is obtained by solving convex optimization of a set of LMIs. Sufficient condition for stability and a new upper bound on robust performance index are given for these kinds of uncertain fuzzy systems with state time-delay. Simulation results of CSTR process show that the proposed robust predictive control approach is effective and feasible.

Observer-based robust predictive control of singular systems with time-delay and parameter uncertainties

The problem of observer-based robust predictive control is studied for the singular systems with norm-bounded uncertainties and time-delay, and the design method of robust predictive observer-based controller is proposed. By constructing the Lyapunov function with the error terms, the infinite time domain ＂min-max＂ optimization problems are converted into convex optimization problems solving by the linear matrix inequality （LMI）, and the sufficient conditions for the existence of this control are derived. It is proved that the robust stability of the closed-loop singular systems can be guaranteed by the initial feasible solutions of the optimization problems, and the regular and the impulse-free of the singular systems are also guaranteed. A simulation example illustrates the efficiency of this method.

Robust Predictive Control for Polytopic Uncertain Systems with Time-delay Subjected to Actuator Saturation

Robust predictive control algorithms were presented for polytopic uncertain linear discrete systems with time-delay subjected to actuator saturation. In the first algorithm, the parameter dependent state feedback model predictive control (MPC) law was obtained from minimizing the upper bound of the cost function subjected to several linear matrix inequality constraints. In order to reduce computation burden, a second robust MPC algorithm based on nominal performance cost was presented. The feasibility of the optimization problems guarantees that the algorithms are robustly stable. The simulation results verify the effectiveness of the proposed algorithms.

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.

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.

An Improved Fuzzy Predictive Control Algorithm and Its Application to an Industrial CSTR Process

A finite horizon predictive control algorithm, which applies a saturated feedback control law as its local control law, is presented for nonlinear systems with time-delay subject to input constraints. In the algorithm, N free control moves, a saturated local control law and the terminal weighting matrices are solved by a minimization problem based on linear matrix inequality （LMI） constraints online. Compared with the algorithm with a nonsaturated local law, the presented algorithm improves the performances of the closed-loop systems such as feasibility and optimality. This model predictive control （MPC） algorithm is applied to an industrial continuous stirred tank reactor （CSTR） with explicit input constraint. The simulation results demonstrate that the presented algorithm is effective.

Research on fuzzy self-adaptive PI-Smith control in long time-delay system

The long time-delay often exists in industrial process. In order to overcome the big overshoot and long regulating time of the long time-delay system control, a new fuzzy self-adaptive PI-Smith control method is proposed. This method combines the Smith predictive control with fuzzy self-adaptive proportional-integral （PI） control. The traditional proportional-integral-derivative （PID） controller in Smith predictive control is replaced by fuzzy PI controller which utilizes the principle of fuzzy control to tune parameters of PI controller on-line. The results of simulation for electric furnace show that the method has the advantages of shortening regulating time, no overshoot, no steady-state error, excellent control accuracy, and good adaptive ability to the change of system model.

Predictive Tracking Control of Network-Based Agents With Communication Delays

This paper investigates the remote tracking control problem of Network-based Agents with communication delays existing in both forward and feedback communication channels.A networked predictive tracking controller is proposed to compensate the negative effects caused by bilateral time-delays in a wireless network. Furthermore, the problem of consecutive data loss in the feedback channel is solved using aforementioned controller, where lateral movement perturbations are introduced.Simulations and experiments are provided for several cases,which verify the realizability and effectiveness of the proposed controller.

An Energy-Saving Control Strategy on Central Air-Conditioning System

An energy-saving control strategy based on predictive control for central air-conditioning systems is proposed in this paper. The cold load model is developed to describe the dynamic characteristics of temperature control systems, and then parameters in the cold load model and in the central air-conditioning system model are estimated. Generalized predictive control (GPC) is used to establish an optimization model to minimize the consumption of energy and the control error of temperature. The simulated annealing (SA) algorithm, combined with quadratic programming, is adopted to solve the optimal problem. Contrasted with the simulation of traditional PID control, the results prove the effectiveness of this proposed strategy.

模糊预测控制在VAV空调系统中的应用

应用模糊控制的逻辑推理性能,借助于神经网络的学习能力,提出一种基于神经网络的模糊预测控制模型。并利用该模型对VAV(变风量)空调系统的提前开机时间进行预测控制,可获得较高的预测精度和较好的控制效果。

VRV-VAV联合空调系统基于负荷预测的优化控制

针对VRV-VAV联合空调系统,本文提出了一种优化控制策略,通过改变屋顶机送风温度设定值实现了系统的优化控制。采用状态参数自适应递归和时间序列分析法建立了系统能效比的预测模型,并针对模型的问题对其进行了修正,进而提出一种组合预测模型。修正后的模型平均误差为0.05%,最大误差仅为0.342%,将其应用于系统的优化控制节能率可以达到6.7%。

A Hybrid Time-delay Prediction Method for Networked Control System

This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.

Determination of Parameters of Time-delayed Embedding Algorithm Using Koopman Operator-based Model Predictive Frequency Control

Power systems around the world have been registering a degenerating inertial response in view of the growth of inverter-based resources along with the withdrawal of conventional coal units.Therefore,there is a need for swift frequency support and its control,preferably by means of power electronic-interfaced storage devices,owing to their beneficial capabilities.Despite being particularly efficient,pragmatically,the traditional model-based non-linear control techniques are not highly popular in power system control design,primarily due to the complications faced in obtaining accurately suitable models for certain power system components.Lately,the modelfree Koopman operator-based model predictive control(KMPC)has proven to be highly conducive for data-driven non-linear control design.The principle behind KMPC is to change the coordinates in a manner to get an approximately linear model,which can then be controlled using a linear model predictive control.In this study,we employed time-delayed embedding of measurements to reconstruct a new set of preferable coordinates,thereby suggesting an approach for finding the optimal number of time lags and the embedding dimensions which are the key parameters of this algorithm.The efficacy of this KMPC framework is established by adopting a decentralized frequency control problem through a decoupled synchronous machine system,which we proposed for both the Kundur two-area system as well as the IEEE 39-bus test system.

神经网络非线性预测优化控制及仿真研究

针对暖通空调等一类时变多输入多输出非线性过程控制系统,采用神经网络作为优化反馈控制器求解优化反馈解,并利用预测控制滚动优化能够克服干扰和不确定性影响的优势,采用基于Hamilton-Lagrange 方法和预测滚动优化算法训练多层前向神经网络,同时对系统中某些不能直接测量且受到多种因素影响、计算复杂的时变参数也利用神经网络进行预测,以实现对象特性的实时预测。利用该控制方法对某变风量暖通空调模型进行了仿真,优化指标取舒适性指标和耗能量之和。仿真结果表明,采用此方法,在模型不确定和存在外在干扰的情况下可以得到较好的控制效果。

变风量空调系统中基于预测的末端再热控制策略

在对多区域变风量空调及其控制系统分析研究的基础上,提出一种优化的新风控制策略———基于预测的末端再热控制策略.该策略通过预测的方法对系统实施前馈控制,使用末端再热器调整区域的送风量,从而能够合理地分配新风.仿真结果表明,该新风控制策略与最大新风比控制策略相比,可以使系统保持较小的总新风吸入量,通过有效地控制各区域新风的配给,在保证各区域新风要求的同时,节约系统的能耗.

变风量空调系统的模型预测控制及仿真研究

采用基于模型预测控制策略的多输入多输出(MIMO)控制器对单通道变风量空调系统的控制问题进行了研究。在分析系统工作机理和系统动力学行为的基础上,建立了内部模型的结构,采用实验确定了模型的参数。通过设定参考轨迹、输入输出约束、控制步长、预测步长及加权矩阵,解决了系统的凸二次型优化问题。通过仿真研究和对实际系统的控制,验证了系统的控制效果。

基于协调的变风量空调系统分布式预测控制

在实验和系统动力学行为分析的基础上,建立了变风量空调实验系统的内部模型,并分解为7个子系统.各个子系统分别采用模型预测控制(MPC)进行局部优化控制.在保证各个子系统之间网络连通和信息共享的基础上,将各个MPC的局部目标组合成系统级目标,从而把大规模的变风量空调控制系统在线优化问题转化为各子系统小规模的分布式优化问题.通过仿真和实验研究,验证了系统控制的效果.

变风量空调的自适应模糊PID复合控制

研究空调系统控制的优化问题,针对变风量空调系统是一个时变、非线性、纯滞后的复杂系统,要求达到舒适节能的环境。传统模糊控制和PID控制方法难以获得较好的控制性能。为解决控制系统中超调量大、振荡和控制精度不高等问题,提出了史密斯预估的自适应模糊-PID复合控制策略。在传统的模糊控制中加入自适应环节,通过模糊自适应校正解决了因模糊规则粗糙而造成控制精度不高,适应能力弱的问题。针对大滞后系统,采用史密斯预估补偿,减小了控制系统的超调和振荡。同时,用模糊与PID相并联的复合控制方式,提高了系统的动态性能指标和稳态精度。仿真结果证明,改进的控制策略响应快、控制精度高、鲁棒性强,为变风量空调系统的智能控制设计提供了参考依据。

变风量空调系统的分布式预测控制

基于预测控制策略的多输入多输出(MIMO)控制器可以解决单通道变风量系统的控制问题,但在输入输出变量较多时,计算就变得非常复杂。在分析系统工作机理和系统动力学行为的基础上,建立了内部模型,在此基础上采用分布式预测控制技术将系统分解成四个子系统,通过设定参考轨迹、输入输出约束及加权矩阵,解决了系统凸二次型的全局优化和纳什问题。通过仿真研究验证了系统控制的效果。