Search

This paper discusses about a general implementation structure of the filtered Smith predictor for single-input single-output processes with a measurable disturbance. Dead-time free models are used to feed-forward the measurable disturbance into the feedback controller. It is demonstrated that the proposed controller can be applied to control open-loop unstable processes with different time delays associated with control and disturbance signals. Different simulated industrial processes are used to illustrate the proposed strategy properties and advantages over other time-delay compensators. [ABSTRACT FROM AUTHOR]

Dead-time is a phenomena that is present in many industrial processes and it presents a challenge for feedback control, especially for multivariable processes. To attenuate the dead-time effects, a common method is the use of predictor structures, which use input/output information of the process to predict the output (or states) of the system after the dead-time. In this paper, the Modified Kalman Predictor (MKP) is proposed, which is a novel predictor for linear multivariable square systems with multiple dead-time (or delays) based on the Kalman Filter that has disturbance estimation and can cope with systems of any order or dynamics, including unstable ones. It uses a specific state-space representation of the process which makes its implementation more straight-forward when compared to other methods. The MKP affects the disturbance rejection but not the closed-loop stability in the nominal case, and it can help to improve closed-loop robustness in the uncertain case. The impacts of the MKP tuning in the closed-loop response considering disturbance rejection and robustness are analyzed using standard frequency domain tools. To illustrate the benefits of the MKP, two examples are used that highlight the tuning guidelines for disturbance rejection and robustness improvements. • A predictor based on the Kalman Filter is proposed for MIMO processes with delays. • It implicitly defines a Filtered Smith Predictor and has the same properties. • Can be applied to MIMO square process with multiple delays. • Can cope with systems of any order or dynamics (non-minimum phase, unstable, etc.). • Tuning rules are simple and independent of system order or dynamics. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the robust stability analysis of the filtered Smith predictor (FSP) dead-time compensator for uncertain processes with time-varying delays. For this purpose, a delay-dependent LMI-based condition is used to compute a maximum delay interval and tolerance to model uncertainties such that the closed-loop system remains stable. Some simulation results illustrate that the proposed controller gives larger delays intervals or better performance than similar approaches proposed in literature applied both to stable and unstable processes. [Copyright &y& Elsevier]

Abstract: This short communication analyzes the results recently presented in the paper “On a novel dead time compensator for stable processes with long dead times” published in the Journal of Process Control. In the mentioned paper it is argued that the proposed strategy, called modified Smith predictor (MSP), gives better performance than the filtered Smith predictor (FSP) dead-time compensator for stable processes with dead time. In fact MSP has the same structure as FSP and only some specific tuning rules of the filters are proposed. Therefore, in this work some aspects of the comparative analysis and tuning rules presented in the referred paper are discussed to show that MSP is a particular case of FSP and that for some particular cases its tuning rule does not allow for a good closed-loop response. Some simulation case studies are used to illustrate these aspects. [Copyright &y& Elsevier]

Abstract: This paper presents a unified approach for the design of dead-time compensators. The approach is based on a modified structure of the Smith predictor that allows to decouple the disturbance and set-point responses in order to tune the controller for a compromise between performance and robustness and is able to cope with unstable processes. The proposed structure is simple to analyse and tune. Moreover a novel robustness analysis of the unstable case is presented to demonstrate the limitations imposed by the unstable dynamics and the dead time. Cases studies are used to illustrate the advantages of the proposed controller. [Copyright &y& Elsevier]

Abstract: This paper presents an analysis of 2DOF discrete dead-time compensators for stable and integrative processes. The proposed structure can be tuned taking into account the performance and robustness of the closed-loop. Also a novel procedure to choose the sampling period is presented considering the robustness and the disturbance rejection response of the closed-loop system. In order to illustrate the results, some simulation examples and experimental results are shown. [Copyright &y& Elsevier]

This paper presents the application of a Model Predictive Controller to the temperature control in a solar air conditioning plant. The controller uses a Smith Predictor and includes a feed-forward control action to reject disturbances caused by solar radiation and the auxiliary gas heater. The tuning procedure is simple and allows a good compromise between robustness and performance. The behaviour of the controller is illustrated by experimental results. [Copyright &y& Elsevier]

Dead-time processes are common in industry and represent a challenge for feedback control. The use of predictor structures with the controllers can attenuate this. A predictor is proposed here based on the Kalman filter, the Kalman Predictor (KP), that has only one tuning parameter for SISO systems and can be used with non-minimum phase, open-loop unstable and integrative systems of any order, and is also capable of estimating many types of disturbances. It is shown that the KP implicitly defines a Filtered Smith Predictor (FSP), hence, all tools used to analyze the closed-loop properties of the FSP can be used, including robustness and disturbance rejection. Tuning guidelines are provided and simulation examples with comparisons with other predictor structures are used to illustrate the advantages of the proposed KF. • A predictor based on the Kalman Filter is proposed, the Kalman Predictor. • It implicitly defines a Filtered Smith Predictor and has the same properties. • The predictor has a single tuning parameter, independent of system order or dynamics. • Can cope with systems of any order or dynamics (non-minimum phase, unstable, etc.). • Can be designed to properly estimate different disturbance types (step, sinusoid, etc.). [ABSTRACT FROM AUTHOR]

In the above-mentioned paper [J Process Control 12 (2002) 391], the authors claimed that the proposed method is a simple and new controller for plants with an integrator and long deadtime that solves some of the draw-backs of previous works. Although the presented results are correct, they do not take into account that several papers have been previously published given the same (and in some cases better) solution for the stated problem. Furthermore, the proposed control structure is internally unstable and cannot be implemented using the suggested block diagram. This note points out that the controller proposed in the commented paper is a particular case of the one presented in IEEE T.A.C. 44(8) (1999) 1597. [Copyright &y& Elsevier]

A constrained latent variable model predictive control (LV-MPC) technique is proposed for trajectory tracking and economic optimization in batch processes. The controller allows the incorporation of constraints on the process variables and is designed on the basis of multi-way principal component analysis (MPCA) of a batch data array rearranged by means of a regularized batch-wise unfolding. The main advantages of LV-MPC over other MPC techniques are: (i) requirements for the dataset are rather modest (only around 10–20 batch runs are necessary), (ii) nonlinear processes can efficiently be handled algebraically through MPCA models, and (iii) the tuning procedure is simple. The LV-MPC for tracking is tested through a benchmark process used in previous LV-MPC formulations. The extension to economic LV-MPC includes an economic cost and it is based on model and trajectory updating from batch to batch to drive the process to the economic optimal region. A data-driven model validity indicator is used to ensure the prediction’s validity while the economic cost drives the process to regions with higher profit. This technique is validated through simulations in a case study. [ABSTRACT FROM AUTHOR]

Since the seminal work of Smith, predictor structures have been used to control processes with dead-time. Predictors allow the control of this type of process with a delay-free nominal model, which simplifies the control design. In this paper, a nonlinear filtered Smith predictor (NLFSP) structure is proposed for systems with input nonlinearities. It is shown that the NLFSP maintains the characteristics of the linear filtered Smith predictor and that with appropriate tuning, it can increase the robustness of the closed-loop system or improve the disturbance rejection response. The NLFSP is applied to a simulated CSTR process to demonstrate these characteristics. [ABSTRACT FROM AUTHOR]

Highlights: [•] We implemented a hierarchical controller approach in order to control thermal comfort. [•] The upper layer is formed by a non-linear MPC, the lower layer contains a PI. [•] The non-linear model predictive controller uses a non-linear model of a room based on first principles. [•] The proposed control strategy is tested in a real bioclimatic building. [•] The hierarchical controller allows to maintain the user's welfare inside the building's rooms. [ABSTRACT FROM AUTHOR]

Highlights: [•] The filtered Smith predictor (FSP) for MIMO processes is analyzed. [•] Internal stability is guaranteed by using a suitable implementation structure. [•] Well-known primary controller tuning approaches can be directly applied. [ABSTRACT FROM AUTHOR]

This paper proposes a cascade series control structure and design for two series processes represented by first-order plus dead-time FOPDT models. The proposed controller uses two series predictors, one for each process, and can deal with stable, unstable, or integrative processes. The design follows similar principles of the simplified filtered Smith Predictor (SFSP) for a single-loop dead-time system. Initially, the primary controller, composed of a set-point static gain and two feedback controllers, is tuned to achieve the desired set-point tracking. Then, the predictor filters are tuned to ensure stability, robustness, and disturbance attenuation. Different from standard cascaded SFSP, one of the feedback controllers, instead of only a static gain, includes a finite time integral. The main advantage of this approach is that disturbances generated in the primary or secondary process can be handled independently by the predictor filters, simplifying the tuning procedure and enhancing the overall control performance. Additionally, for the nominal case, the proposed cascaded controller allows obtaining an ideal set-point tracking and disturbance rejection. After the dead-time effect, the proposed cascade controller achieves the set point exponentially and rejects exponentially step-like disturbances where the user defines the time constants of the exponentials. Simulation results demonstrate the advantages of the proposed controller compared to other recently published approaches, mainly in the inner loop disturbance rejection, which is precisely what is expected from a series cascade controller. • A new time-delay compensator applied to cascade series delayed process is proposed. • The proposed controller can be applied to cascaded open-loop stable, unstable, and integrative processes with time delay. • One can independently tune the disturbance attenuation of the primary and secondary processes without affecting the entire system. • The tuning rules are as simple as in a traditional filtered Smith predictor. • The proposed controller allows the achievement of the ideal setpoint tracking and step-like disturbance rejection. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the measurable disturbance rejection problem in Generalized Predictive Control (GPC). First, it is analyzed how the unconstrained GPC algorithm with implicit disturbance compensation can be interpreted as a typical feedback plus feedforward control scheme, where the main feature is that the feedforward action includes future estimations of the measurable disturbances. Then, it is shown that classical GPC cannot always eliminate the effect of measurable disturbances even using perfect disturbance models and having exact disturbance estimations along the prediction horizon. To overcome this problem a particular GPC tuning condition is proposed, which allows the improved GPC controller to eliminate the disturbance effect even in those cases where causality and instability problems can appear in the relation between the dynamics of the load disturbance and the process output with the dynamics of the control signal and the process output. Since the new tuning condition for disturbance compensation in GPC leads to a high bandwidth in the feedback loop, a two degrees of freedom control scheme within the Filtered Smith Predictor (FSP)-based GPC framework has been implemented to improve the robustness capabilities of the control law. Simulation examples are presented to show the main advantages of the proposed control scheme, including a realistic simulation based on a greenhouse climate control problem where estimators for the main process disturbances are also designed. [Copyright &y& Elsevier]

Abstract: Computational simplicity is one of the most important aspects to take into account in robust model predictive control (MPC). In dead-time processes, it is common to use an augmented state-space representation in order to apply robust MPC strategies but, this procedure may affect computational aspects. In this paper, explicit dead-time compensation will be used to avoid augmented representation. This technique will be analyzed in terms of robust stability and constraint satisfaction for discrete-time linear systems. The results of this discussion will be applied to a robust tube-based MPC strategy which is able to guarantee robust stability and constraint satisfaction of a dead-time system by considering a prediction model without dead-time. Moreover, taking advantage of the proposed scheme, the robust MPC will be particularized for first-order plus dead-time models which simplifies significantly controller synthesis. The proposed dead-time compensation method will be applied to different robust MPC strategies in two case studies: (i) a simulated quadruple-tank system, and (ii) an experimental scaled laboratory heater process. [Copyright &y& Elsevier]

Abstract: This paper presents the development of a unified dead-time compensator for multiple-input and multiple-output (MIMO) n × n processes with multiple delays. The proposed structure is a generalization of the single-input and single-output (SISO) filtered Smith predictor (FSP) controller, therefore, as in the SISO case it can be used to control stable, integrating, and unstable dead-time processes. MIMO-FSP compensates the minimal output dead time and its tuning is simple and can be performed considering a trade-off between performance and robustness. Several simulation case studies are used in the paper to illustrate the use of the unified approach and also to compare the obtained solutions with some recent published results. Practical experiments using a neonatal intensive care unit prototype are also presented. [Copyright &y& Elsevier]

Abstract: The analysis of noise attenuation in dead-time compensators (DTCs) for unstable processes is presented in this paper. The Filtered Smith Predictor, that is a DTC which can be used to control stable and unstable dead-time processes, is used for this study. A new filter design procedure is proposed in order to include noise attenuation specifications. By using this procedure, it is possible to obtain better results than other solutions in literature. A simulation example is used to compare the proposed approach with a recently published results and an experimental case of study is used to illustrate its usefulness. [Copyright &y& Elsevier]

Hydrogen production systems supplied by photovoltaic solar energy have nonlinear dynamics and discontinuities which must be taken into account when a control system is applied. The main purpose of the control system is to maintain the electrolyzer current at the desired operating point and, at the same time, to optimize the grid energy consumption despite the solar energy variability. Classic controllers, like PID ones, are not able to obtain good performance over the whole operation range of these kinds of plants because of the aforementioned characteristics. To overcome these limitations, an optimal control strategy and a linear hybrid model predictive controller (HMPC) are applied to a hydrogen production system in this work. Regarding the optimal control design, a systematic framework is presented in order to obtain the optimal (in the sense of minimal grid energy consumption) trajectory of the states by converting the control problem into a boundary value problem by means of the Pontryagin's Maximum Principle. Interestingly, the resulting control law is explicit and piecewise continuous. Regarding the linear HMPC strategy, a mixed logical dynamical description of the linearized equations of the system is considered in order to obtain the control law by solving an optimization problem in the form of a mixed integer quadratic programming. For this control strategy three cost functions associating the grid energy consumption and the electrolyzer efficiency are presented. The proposed controllers are tested through numerical simulations for both the nominal and uncertain cases and different performance indexes are considered. Finally, a discussion of the main advantages and disadvantages of each controller in real-life applications is presented. • Two controllers are designed for the control problem of a hydrogen production system. • An explicit optimal control law is derived. • The HMPC algorithm approximates the optimal behavior. • Simulation tests with real data are performed for the nominal and uncertain cases. • The advantages and drawbacks of each of the proposed controllers are discussed. [ABSTRACT FROM AUTHOR]

Highlights • The study proposes an advanced control structure in 3 layers aiming the optimal efficiency energy management for Grid-Connected Hybrid Power Plant. • The control structure is based on a Stochastic Model Predictive Control and an optimal finite-state machine to take into account disturbances variations. • Disturbance estimation techniques based on of NAR Neural Networks are applied to have prediction of renewable sources. • The studied control method is compared, through simulation, to a deterministic MPC approach to illustrate an improved performance. Abstract This study presents a complete advanced control structure aimed at the optimal and most efficient energy management for a Grid-Connected Hybrid Power plant. This control scheme is composed of process supervision and process control layers, and it is a possible technology to enable improvements in the energy consumption of industrial systems subject to constraints and process demands. The proposed structure consists of the combination of a Model-Based Predictive Controller, formulated within the Chance Constraints framework to deal with stochastic disturbances (renewable sources, as solar irradiance), an optimal finite-state machine decision system and the use of disturbance estimation techniques for the prediction of renewable sources. The predictive controller uses feedforward compensation of estimated future disturbances, obtained by the use of Nonlinear Auto-Regressive Neural Networks with time delays. The proposed controller aims to perform the management of which energy system to use and to decide where to store energy between multiple storage options. This has to be done while always maximizing the use of renewable energy and optimizing energy generation due to contract rules (maintain maximal economic profit). The proposed method is applied to a case study of energy generation in a sugar cane power plant, with non-dispatchable renewable sources (such as photovoltaic and wind power generation), as well as dispatchable sources (as biomass and biogas). This hybrid power system is subject to operational constraints, as to produce steam in different pressures, sustain internal demands and, imperiously, produce and maintain an amount of electric power throughout each month, defined by strict contract rules with a local Distribution Network Operator (DNO). This paper aims to justify the use of this novel approach to optimal energy generation in hybrid microgrids through simulation, illustrating the performance improvement for different cases. [ABSTRACT FROM AUTHOR]

This paper presents a framework to deal with distributed optimization problems composed by binary and continuous variables. Instead of using a mixed integer quadratic programming (MIQP), the approach proposed here transforms the MIQP into a set of quadratic programming's (QP) that are easier to solve. In this way an instance of the controller related to each feasible combination of binary variables is created. The distributed controller performs an iterative process where the set of agents must agree on the value of continuous interconnection variables, while each agent must decide the values of local binary variables. During the iteration procedure the instances are rated according to a performance index and the instances with best performance are selected until the best one is obtained. The proposed methodology is applied to economic optimization of networked microgrids. [ABSTRACT FROM AUTHOR]

Highlights: [•] A distributed MPC approach for energy management in efficient buildings. [•] Real-world operation of a solar plant for control of thermal comfort. [•] Distributed MPC achieves performance comparable to centralized MPC with a suitable tuning of the parameters. [ABSTRACT FROM AUTHOR]