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In this paper, we evaluate the robustness qualities of Model Predictive Control (MPC) algorithms applied for Linear Parameter Varying (LPV) systems. Specifically, we analyze gain‐scheduled LPV MPC schemes, that is, those that use model predictions based on the LPV scheduling variables available at each sampling instant. Accordingly, we extend previous results on finite‐horizon robustness analysis of linear time‐variant (LTV) systems, employing Integral Quadratic Constraints (IQCs) to describe the input‐output behavior of prediction uncertainties. We provide two main novelties in our formulation: (i) we propose a parameter‐dependent Karush–Kuhn–Tucker (KKT) inequality to describe the existence and feasibility of the LPV MPC control inputs; and (ii) we model the uncertainties that arise due to the unavailability of the scheduling trajectory along the prediction horizon as a bounded interconnection in the form of a Linear Fractional Transformation (LFT). Accordingly, we use dissipativity arguments (p$$ p $$‐hard IQCs) in order to compute robust induced gains of the closed‐loop system (specifically, the ℒ2$$ {\mathcal{L}}_2 $$ and ℒ2$$ {\mathcal{L}}_2 $$‐to‐Euclidean metrics), taking into account the MPC prediction uncertainties. We also generate the set of reachable states from a given initial condition. A benchmark example is used to illustrate the proposed analysis procedure. [ABSTRACT FROM AUTHOR]

In this article, a robust model predictive control (MPC) procedure for quasi‐linear parameter varying (qLPV) systems is proposed. The novelty resides in considering a recursive extrapolation algorithm to estimate the values of the scheduling parameters along the prediction horizon Np, which fastens the sluggish performances achieved with the robust qLPV MPCs from the literature. The bounds on the estimation errors of the scheduling parameters through Np are taken into account by the robust MPC, which solves an online min‐max problem: first, a constrained convex program is resolved in order to determine the worst‐case bound on the cost function and, subsequently, a second constrained quadratic program is solved to minimize this worst‐case cost function with respect to a control sequence vector. Since the bounds on the estimation error for the scheduling parameters are usually much smaller than the bounds on the actual scheduling parameter, the conservativeness of the solution is quite reduced. Recursive feasibility and stability of the proposed algorithm are demonstrated with dissipativity arguments given in the form of a linear matrix inequality remedy, which determines the zone of attraction for which input‐to‐state stability is ensured. The nonlinear temperature regulation problem of a flat solar collector is considered as a case study. Using a realistic simulation benchmark, the proposed technique is compared to other robust min‐max LPV MPC algorithms from the literature, proving itself efficient while achieving good performances. [ABSTRACT FROM AUTHOR]

Motivated by the fact that many nonlinear plants can be represented through Linear Parameter Varying (LPV) embedding, and being this framework very popular for control design, this paper investigates the available Model Predictive Control (MPC) policies that can be applied for such systems. This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined. A simulation example is given comparing some of the important techniques. Finally, some suggestions are given for future investigation threads, seeking further applicability of these methods. [ABSTRACT FROM AUTHOR]

This paper presents a new methodology to design multivariable Smith predictor forn×nprocesses with multiple time delays based on the centralised inverted decoupling structure. The controller elements are calculated in order to achieve good reference tracking and decoupling response. Independent of the system size, very simple general expressions for the controller elements are obtained. The realisability conditions are provided and the particular case of processes with all of its elements as first-order plus time delay systems is discussed in more detail. A diagonal filter is added to the proposed control structure in order to improve the disturbance rejection without modifying the nominal set-point response and to obtain a stable output prediction in unstable plants. The effectiveness of the method is illustrated through different simulation examples in comparison with other works. [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]

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 proposes a feedback linearization strategy for a solar collector field, which is a constrained non-linear processes. The benefits of input–output feedback linearization are improved by a filtered Smith predictor-based model predictive control algorithm with embedded variable constraint mapping to take advantage of: (i) linear control without losing the intrinsic non-linearities typical of thermal power plants; (ii) including input amplitude constraint handling capabilities due to control signal saturations induced, for example, by strong irradiance disturbances or plant start-up; and (iii) avoiding unstable or highly oscillatory responses caused by plant-model mismatch. Simulation studies are first presented to analyze robustness and constraint-mapping features, and real experimental tests of this technique in the AQUASOL desalination plant solar field have been included to demonstrate the advantages of its implementation, especially for reference tracking despite disturbances. [Copyright &y& Elsevier]

This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ -analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability. • A generalized model is presented for Off-Grid Microgrids with multiple carriers. • The Philippine Off-grid islands are considered, with diesel, solar, and wind energy. • A Robust Model Predictive Control scheme is proposed to manage these Microgrids. • Demand compliance and operation reliability are assessed through robustness analysis. • Numerical simulation illustrate reliability under multiple scenarios. [ABSTRACT FROM AUTHOR]

• A framework to model faulty microgrids is proposed. • A Fault Estimation layer, based on LPV observers, is developed. • A Model Predictive Controller is designed as Fault-Tolerant Energy Management Systems. • Realistic numerical simulation results are presented. This paper presents a solution for the Fault-Tolerant Energy Management problem of renewable energy microgrids. This solution is a Energy Management System (EMS) derived from a Model Predictive Controller (MPC) synthesized upon a Linear Parameter Varying (LPV) prediction model. This model describes the energy-generation process in both healthy and faulty operation conditions. The MPC is tuned to adequately coordinate the operation of the microgrid, aiming to optimally use its energetic resources, enlarge the renewable generation share and guarantee maximal efficiency and profit, despite the presence of faults (or even failures) in its subsystems. The quantification of the level of faults in the energy system is provided by an extended-state LPV fault estimation observer that works in parallel to the MPC. The proposed EMS , that acts at an hourly rate, finds time-varying control policies, that are passed as energy-generation set-points for the lower-layer subsystems, with respect to operational constraints, internal demands and taking into account the future (estimation) behaviour of the renewables. To validate the proposed fault-tolerant control scheme, a realistic, high-fidelity case study from the Brazilian sugarcane industry is considered. The achieved simulation results assess the effectiveness and qualities of the proposed energy management strategy; an overall good behaviour is exhibited in both faulty and healthy energy-generation conditions. [ABSTRACT FROM AUTHOR]

Highlights • A temperature delay is observed in solar fields based on flat-plate collectors. • The effect of the apparent delay can be demonstrated with first-order models. • Dynamic model validation is improved through the inclusion of the apparent delay in a bilinear model. Abstract This study presents an analysis of the effect of the transport delay which occurs in solar flat-plate collector fields and how to include its behavior in dynamic models suitable for control purposes. This investigation has been carried out using simplified models based on dynamic energy balances and models based on step response methods and experimental tests. The solar flat-plate collector field encompasses parallel absorber pipes intertwined with pipelines which act as several first-order plus dead time systems in parallel. The effect is an apparent delay observed at the outlet temperature that must be included in the dynamic model in order to reduce the error between the real measurement and the model prediction. The main contribution of this paper is the procedure to evaluate the apparent delay to obtain adequate dynamic models aimed to be used for control purposes. [ABSTRACT FROM AUTHOR]

• Three cooperative sugarcane-based microgrids are considered. • They should work together to produce energy. • A two-layer RTO plus MGCCs strategy is proposed. • The RTO coordinates biomass transport and long-term energy generation goals. • The MGCCs are MPC that guarantee reference tracking. • Realistic numerical simulation scenarios are presented. This paper presents a solution for the energy magement and resource sharing problem of cooperative renewable-energy-based microgrids. Such solution is based on a two-level hierarchical optimization procedure to coordinate the operation of networked microgrids and optimally share their combined energetic resources, aiming to enlarge the renewable generation share while guaranteeing maximal efficiency and profit. The supervision layer is a Real-Time Optimization scheme, designed to determine energy-generation set-points for the distributed plants as well as resource sharing rules, taking into account their models, operational constraints and future predicted behaviour of the renewable sources. The lower layer is a Model Predictive Control loop, designed to follow energy generation set-points passed top-down by the upper layer and abide by demands, while respecting operational constraints. This work is focused on cooperative microgrids based on Brazilian sugarcane industries. Such plants are usually located in various sites, while being owned by the same company that has a fixed energy contract with the Distribution Network Operator. The biomass sources may be transported from one plant to another, if deemed convenient. This case study is taken into account and high-fidelity numerical simulations are presented to assess the effectiveness and qualities of the proposed energy management strategy, which yields overall good results. [ABSTRACT FROM AUTHOR]

Thermal energy storage tanks are habitually combined with solar thermal fields to improve the dispatchability of these facilities. From the dynamical point of view, the start-up phase is relevant since if the storage device is unloaded in terms of energy or widely stratified, the transitory regime can take long time until reaching the operating point. In this paper, an optimal real-time procedure based on a hierarchical controller for improving the start-up phase is proposed. The hierarchical controller is composed of two layers based on a Model Predictive Control (MPC) technique and Proportional Integer Derivative (PID) controllers. Real experimental tests were performed in a pilot facility located at Plataforma Solar de Almería (Almería, Spain). In addition, a comparison in simulation with the typical manual procedure and with two techniques proposed previously in the literature for the same plant is provided. The results demonstrate the benefits obtained by using the proposed method; since it reduces the start-up phase in 34 [min] in comparison with the manual operation, and in 26 and 6 [min] with respect to the two previous techniques. • This paper proposes an optimal start-up policy for solar thermal fields. • The procedure is based on a hierarchical controller that uses a real time optimizer. • Experimental tests were conducted in a real facility with direct storage. • A simulated comparative analysis with other start-up techniques is also provided. • The start-up stage time can be reduced up to 10.5% with the proposed methodology. [ABSTRACT FROM AUTHOR]