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.

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.

Robust Modeling, Sliding-Mode Controller, and Simulation of an Underactuated ROV Under Parametric Uncertainties and Disturbances

A dynamic model of a remotely operated vehicle(ROV)is developed.The hydrodynamic damping coefficients are estimated using a semi-predictive approach and computational fluid dynamic software ANSYS-CFX?and WAMIT?.A sliding-mode controller(SMC)is then designed for the ROV model.The controller is subsequently robustified against modeling uncertainties,disturbances,and measurement errors.It is shown that when the system is subjected to bounded uncertainties,the SMC will preserve stability and tracking response.The paper ends with simulation results for a variety of conditions such as disturbances and parametric uncertainties.

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 Model-Free Software Sensors for the HIV/AIDS Infection Process

This paper considers the problem of the HIV/AIDS Infection Process filtering characterized by three compounds, namely, the number of healthy T-cells, the number of infected T-cells and free virus particles. Only the first and third of them can be measurable during the medical treatment process. Moreover, the exact parameter values are admitted to be also unknown. So, here we deal with an uncertain dynamic model that excludes the application of classical filtering theory and requires the application of robust filters successfully working in the absence of a complete mathematical model of the considered process. The problem is to estimate the number of infected T-cells based on the available information. Here we admit the presence of stochastic “white noise” in current observations. To do that we apply the Luenberger-like filter (software sensor) with a matrix gain, which should be adjusted at the beginning of the process in such a way that the filtering error would be as less as possible using the Attractive Ellipsoid Method (AEM). It is shown that the corresponding trajectories of the filtering error converge to an ellipsoidal set of a prespecified form in mean-square sense. To generate the experimental data sequences in the test-simulation example, we have used the well-known simplified HIV/ AIDS model. The obtained results confirm the effectiveness of the suggested approach.

Robust model-reference control for descriptor linear systems subject to parameter uncertainties

Robust model-reference control for descriptor linear systems with structural parameter uncertainties is investigated. A sufficient condition for existing a model-reference zero-error asymptotic tracking controller is given. It is shown that the robust model reference control problem can be decomposed into two subproblems： a robust state feedback stabilization problem for descriptor systems subject to parameter uncertainties and a robust compensation problem. The latter aims to find three coefficient matrices which satisfy four matrix equations and simultaneously minimize the effect of the uncertainties to the tracking error. Based on a complete parametric solution to a class of generalized Sylvester matrix equations, the robust compensation problem is converted into a minimization problem with quadratic cost and linear constraints. A numerical example shows the effect of the proposed approach.

Robust Model Averaging Method Based on LOF Algorithm

Model averaging is a good alternative to model selection,which can deal with the uncertainty from model selection process and make full use of the information from various candidate models.However,most of the existing model averaging criteria do not consider the influence of outliers on the estimation procedures.The purpose of this paper is to develop a robust model averaging approach based on the local outlier factor(LOF)algorithm which can downweight the outliers in the covariates.Asymptotic optimality of the proposed robust model averaging estimator is derived under some regularity conditions.Further,we prove the consistency of the LOF-based weight estimator tending to the theoretically optimal weight vector.Numerical studies including Monte Carlo simulations and a real data example are provided to illustrate our proposed methodology.

Novel Lyapunov-based rapid and ripple-free MPPT using a robust model reference adaptive controller for solar PV system

The technological,economic,and environmental benefits of photovoltaic(PV)systems have led to their wide-spread adoption in recent years as a source of electricity generation.However,precisely identifying a PV system’s maximum power point(MPP)under normal and shaded weather conditions is crucial to conserving the maximum generated power.One of the biggest concerns with a PV system is the existence of partial shading,which produces multiple peaks in the P–V characteristic curve.In these circumstances,classical maximum power point tracking(MPPT)approaches are prone to getting stuck on local peaks and failing to follow the global maximum power point(GMPP).To overcome such obstacles,a new Lyapunov-based Robust Model Reference Adaptive Controller(LRMRAC)is designed and implemented to reach GMPP rapidly and ripple-free.The proposed controller also achieves MPP accurately under slow,abrupt and rapid changes in radiation,temperature and load profile.Simulation and OPAL-RT real-time simulators in various scenarios are performed to verify the superiority of the proposed approach over the other state-of-the-art methods,i.e.,ANFIS,INC,VSPO,and P&O.MPP and GMPP are accomplished in less than 3.8 ms and 10 ms,respectively.Based on the results presented,the LRMRAC controller appears to be a promising technique for MPPT in a PV system.

A robust optimization model for demand response management with source-grid-load collaboration to consume wind-power

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of winddriven generators,a min-max-min two-stage robust optimization model is presented,considering the unit commitment,source-network load collaboration,and control of the load demand response.After the constraint functions are linearized,the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method.The minimum-maximum of the original problem was continuously maximized using the iterative method,and the optimal solution was finally obtained.The constraint conditions expressed by the matrix may reduce the calculation time,and the upper and lower boundaries of the original problem may rapidly converge.The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately;otherwise,it is easy to cause excessive accommodation of wind power at some nodes,leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power.Thus,the most economical optimization scheme for the worst scenario of the output power of the generators is obtained,which proves the economy and reliability of the two-stage robust optimization method.

Robust model predictive control for greenhouse temperature based on particle swarm optimization

Application of model predictive control(MPC)in horticultural practice requires detailed models.However,even highly sophisticated greenhouse climate models are often known to have unknown dynamics affected by bounded uncertainties.To enforce robustness during the controller design stage,this paper proposes a particle swarm optimization(PSO)-based robust MPC strategy for greenhouse temperature systems.The strategy is based on a nonlinear physical temperature affine model.The robust MPC technique requires online solution of a minimax optimal control problem,which optimizes the tradeoff between set point tracking and cost requirements reduction.The minimax optimization problem is reformulated to a nonlinear programming problem with constraints.PSO is used to solve the reformulated problem and priority ranking of constraint fitness is proposed to guarantee that the constraints are satisfied.The results of simulations performed using the proposed control system show that the controller can effectively achieve the set point in the presence of disturbances and that it offers more suitable control variables,higher control precision,and stronger robustness than the conventional MPC.

A Sliding Mode Controller Based on Robust Model Reference Adaptive Proportional-integral Control for Stand-alone Three-phase Inverter

This paper proposes a sliding mode controller based on robust model reference adaptive proportional-integral(RMRA-PI)control for a stand-alone voltage source inverter(SA-VSI).The proposed controller has two control loops where the coefficients of PI controller are regulated by the adaptive sliding law.This method is used to regulate the output voltage of the inverter under different load conditions and uncertainty,and adapts the output to the reference model to reduce the total harmonic distortion(THD).In this paper,the stability of the proposed controller is proven by using Lyapunov's theory and Barbalet’s lemma.The proposed controller performs well in voltage regulation such as low THD under sudden load change and uncertainty.Also,the results of the proposed controller are compared with PI controller to show the effectiveness of the presented control system.

Robust Length of Stay Prediction Model for Indoor Patients

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the hospitals.Hospital management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital management.It could allow them to take more patients for admission.As a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment.Therefore,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these issues.For this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,etc.have been analyzed to predict the LOS.We selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation criteria.Our results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected models.By Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the hospital.Based on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other diseases.This finding could help predict the future length of hospital stay of new patients,which will help the hospital administration estimate and manage their resources efficiently.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

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.

Vehicle Active Steering Control Research Based on Two-DOF Robust Internal Model Control

Because of vehicle＇s external disturbances and model uncertainties,robust control algorithms have obtained popularity in vehicle stability control.The robust control usually gives up performance in order to guarantee the robustness of the control algorithm,therefore an improved robust internal model control（IMC） algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness.The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties.In order to separate the design process of model tracking from the robustness design process,the improved 2 degree of freedom（DOF） robust internal model controller structure is given from the standard Youla parameterization.Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm,on the basis of a nonlinear vehicle simulation model with a magic tyre model.Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance,which can enhance the vehicle stability and handling,regardless of variations of the vehicle model parameters and the external crosswind interferences.Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

Generalized Internal Model Robust Control for Active Front Steering Intervention

Because of the tire nonlinearity and vehicle＇s parameters＇uncertainties,robust control methods based on the worst cases,such as H_∞,μsynthesis,have been widely used in active front steering control,however,in order to guarantee the stability of active front steering system（AFS）controller,the robust control is at the cost of performance so that the robust controller is a little conservative and has low performance for AFS control.In this paper,a generalized internal model robust control（GIMC）that can overcome the contradiction between performance and stability is used in the AFS control.In GIMC,the Youla parameterization is used in an improved way.And GIMC controller includes two sections：a high performance controller designed for the nominal vehicle model and a robust controller compensating the vehicle parameters＇uncertainties and some external disturbances.Simulations of double lane change（DLC）maneuver and that of braking on split-μroad are conducted to compare the performance and stability of the GIMC control,the nominal performance PID controller and the H_∞controller.Simulation results show that the high nominal performance PID controller will be unstable under some extreme situations because of large vehicle＇s parameters variations,H_∞controller is conservative so that the performance is a little low,and only the GIMC controller overcomes the contradiction between performance and robustness,which can both ensure the stability of the AFS controller and guarantee the high performance of the AFS controller.Therefore,the GIMC method proposed for AFS can overcome some disadvantages of control methods used by current AFS system,that is,can solve the instability of PID or LQP control methods and the low performance of the standard H_∞controller.

Robust identification for multi_section freeway traffic models

Since it is difficult to fit measured parameters using the conventional traffic model, a new traffic density and average speed model is introduced in this paper. To determine traffic model structures accurately, a model identification method for uncertain nonlinear system is developed. To simplify uncertain nonlinear problem, this paper presents a new robust criterion to identify the multi-section traffic model structure of freeway efficiently. In the new model identification criterion, numerically efficient U-D factofization is used to avoid computing the determinant values of two complex matrices. By estimating the values of U-D factor of data matrix, both the upper and lower bounds of system uncertainties are described. Thus a model structure identification algorithm is proposed. Comparisons between identification outputs and simulation outputs of traffic states show that the traffic states can be accurately predicted by means of the new traffic models and the structure identification criterion.

MODELING AND ROBUST DESIGN OF REMANUFACTURING LOGISTICS NETWORKS BASED ON DESIGN OF EXPERIMENT

The uncertainty of time, quantity and quality of recycling products leads tothe bad stability and flexibility of remanufacturing logistics networks, and general design onlycovered the minimizing logistics cost, thus, robust design is presented here to solve theuncertainty. The mathematical model of remanufacturing logistics networks is built based onstochastic distribution of uncontrollable factors, and robust objectives are presented. Theintegration of mathematical simulation and design of experiment method is performed to do sensitiveanalysis. The influence of each factor and level on the system is investigated, and the main factorsand optimum combination are studied. The numbers of factors, level of each factor and designprocess of experiment are investigated as well. Finally, the process of robust design based ondesign of experiment is demonstrated by a detailed example.

A Two-stage Adaptive Robust Model for Residential Micro-CHP Expansion Planning

This paper addresses the planning problem of residential micro combined heat and power (micro-CHP) systems (including micro-generation units, auxiliary boilers, and thermal storage tanks) considering the associated technical and economic factors. Since the accurate values of the thermal and electrical loads of this system cannot be exactly predicted for the planning horizon, the thermal and electrical load uncertainties are modeled using a two-stage adaptive robust optimization method based on a polyhedral uncertainty set. A solution method, which is composed of column-and-constraint generation (C&CG) algorithm and block coordinate descent (BCD) method, is proposed to efficiently solve this adaptive robust optimization model. Numerical results from a practical case study show the effective performance of the proposed adaptive robust model for residential micro-CHP planning and its solution method.