Search

This paper proposes a Repetitive Dynamic Matrix Control (RDMC) for systems with periodic specifications. The new algorithm is able to track periodic references and reject repetitive disturbances with a known period based on a modified prediction error. A repetitive version of the Generalized DMC (GDMC) is also proposed such that it can be applied to control open-loop unstable systems. Only the step-response coefficients are required to describe the dynamical system such that the RDMC preserves the modeling simplicity of the Dynamic Matrix Control (DMC). The proposed solution can be interpreted as an extension of the DMC for repetitive control applications. A data-driven filter design is proposed in order to ensure null prediction steady-state error in the presence of periodic disturbances even for unstable open-loop systems. Two case studies are presented to show the usefulness of the proposed strategy for control systems with periodic specification and to illustrate the typical advantages and drawbacks of the proposed repetitive control extension of the DMC algorithm., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper presents a Generalised Dynamic Matrix Control (GDMC) algorithm that can be used to control open-loop unstable processes. In contrast to the Dynamic Matrix Control (DMC), the GDMC is able to provide internally stable predictions due to a generalised filtered approach. The conditions to achieve internal stability are shown and a new data-driven filter design procedure is proposed. Two simulation case studies are presented to illustrate the usefulness of the GDMC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)

The SARS-CoV-2 virus was first registered in Brazil by the end of February 2020. Since then, the country counts over 150000 deaths due to COVID-19 and faces a profound social and economic crisis; there is also an ongoing health catastrophe, with the majority of hospital beds in many Brazilian cities currently occupied with COVID-19 patients. Thus, a Nonlinear Model Predictive Control (NMPC) scheme used to plan appropriate social distancing measures (and relaxations) in order to mitigate the effects of this pandemic is formulated in this paper. The strategy is designed upon an adapted data-driven Susceptible-Infected-Recovered-Deceased (SIRD) model, which includes time-varying auto-regressive immunological parameters. A novel identification procedure is proposed, composed of analytical regressions, Least-Squares optimization and auto-regressive model fits. The adapted SIRD model is validated with real data and able to adequately represent the contagion curves over large forecast horizons. The NMPC strategy is designed to generate piecewise constant quarantine guidelines, which can be reassessed (relaxed/strengthened) each week. Simulation results show that the proposed NMPC technique is able to mitigate the number of infections and progressively loosen social distancing measures. With respect to a "no-control" condition, the number of deaths could be reduced in up to 30% if the proposed NMPC coordinated health policy measures are enacted., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper proposes an unified procedure for time-varying dead-time compensation. The method is an adapted version of the Filtered Smith Predictor (FSP), which is coupled to a delay estimation scheme and two adaptive Linear Parameter Varying (LPV) blocks. These LPV blocks allow the DTC to autonomously regulate the amount of closed-loop robustness, with respect to the (estimated) amount of delay. The method is easily adjustable and can be tuned to provide a balance between robustness and performance objectives, while being able to deal with unstable and integrative processes. A series of numerical simulations are included to illustrate the advantages of the proposed method towards reference tracking, noise attenuation, disturbance rejection and uncertainty handling. This novel DTC enables enhanced performances with respect other methods from the literature., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic is the defying crisis of our time. Since mass vaccination has not yet been established, countries still have been facing many issues due to the viral spread. Even in cities with high seroprevalence, intense resurgent waves of COVID-19 have been registered, possibly due to viral variants with greater transmission rates. Accordingly, we develop a new Model Predictive Control (MPC) framework that is able to determine social distancing guidelines and altogether provide estimates for the future epidemiological characteristic of the contagion. For such, the viral dynamics are represented through a Linear Parameter Varying (LPV) version of the Susceptible-Infected-Recovered-Deceased (SIRD) model. The solution of the LPV MPC problem is based on a Sequential Quadratic Program (SQP). This SQP provides convergent estimates of the future LPV scheduling parameters. We use real data to illustrate the efficiency of the proposed method to mitigate this contagion while vaccination is ongoing., (© 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.)

This paper formulates a Model Predictive Control (MPC) policy to mitigate the COVID-19 contagion in Brazil, designed as optimal On-Off social isolation strategy. The proposed optimization algorithm is able to determine the time and duration of social distancing policies in the country. The achieved results are based on data from the period between March and May of 2020, regarding the cumulative number of infections and deaths due to the SARS-CoV-2 virus. This dataset is assumably largely sub-notified due to the absence of mass testing in Brazil. Thus, the MPC is based on a SIR model which is identified using an uncertainty-weighted Least-Squares criterion. Furthermore, this model includes an additional dynamic variable that mimics the response of the population to the social distancing policies determined by the government, which affect the COVID-19 transmission rate. The proposed control method is set within a mixed-logical formalism, since the decision variable is forcefully binary (existence or the absence of social distance policy). A dwell-time constraint is included to avoid too frequent shifts between these two inputs. The achieved simulation results illustrate how such optimal control method would operate in practice, pointing out that no social distancing should be relaxed before mid August 2020. If relaxations are necessary, they should not be performed before this date and should be in small periods, no longer than 25 days. This paradigm would proceed roughly until January/2021. The results also indicate a possible second peak of infections, which has a forecast to the beginning of October. This peak can be reduced if the periods of days with relaxed social isolation measures are shortened., Competing Interests: There is no conflict of interest derived from this manuscript. Authors state that this work has not been published previously, that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out. We also accept that if this submission is accepted, it will not be published elsewhere in the same form, in English or in any other language, without the written consent of the Publisher., (© 2020 Elsevier Ltd. All rights reserved.)

This paper proposes a simplified tuning strategy for the multivariable filtered Smith predictor. It is shown that offset-free control can be achieved with step references and disturbances regardless of the poles of the primary controller, i.e., integral action is not explicitly required. This strategy reduces the number of design parameters and simplifies tuning procedure because the implicit integrative poles are not considered for design purposes. The simplified approach can be used to design continuous-time or discrete-time controllers. Three case studies are used to illustrate the advantages of the proposed strategy if compared with the standard approach, which is based on the explicit integrative action., (Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper proposes a dead-time compensation structure for processes with multiple dead times. The controller is based on the filtered Smith predictor (FSP) dead-time compensator structure and it is able to control stable, integrating, and unstable processes with multiple input/output dead times. An equivalent model of the process is first computed in order to define the predictor structure. Using this equivalent model, the primary controller and the predictor filter are tuned to obtain an internally stable closed-loop system which also attempts some closed-loop specifications in terms of set-point tracking, disturbance rejection, and robustness. Some simulation case studies are used to illustrate the good properties of the proposed approach., (Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The robustness properties of the predictive controller are evaluated with respect to inter- and intrapatient variability. A single-input (propofol) single-output (bispectral index, BIS) model of the patient has been assumed for prediction as well as for simulation. A set of 12 patient models were studied and interpatient variability and disturbances are used to assess robustness of the controller. Furthermore, the controller guarantees the stability in a desired range. The applicability of the predictive controller in a real-life environment via simulation studies has been assessed.