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EN
The fulfilment of the condition for the simultaneous achievement of the desired chemical composition and temperature of the metal is ensured by controlling the oxygen consumption and the position of the oxygen impeller lance. The method for solving Model Predictive Control with quadratic functionality in the presence of constraints is given. Implementation of the described solutions will contribute to increasing the proportion of scrap and reducing the melting period without changing of technological process.
PL
Spełnienie warunku jednoczesnego osiągnięcia pożądanego składu chemicznego i temperatury metalu jest zapewnione poprzez kontrolę zużycia tlenu i położenia palnika tlenowego. Zaprezentowano metodę rozwiązania Modelu Sterowania Predykcyjnego z funkcjonalnością kwadratową w obecności ograniczeń. Wdrożenie opisanego rozwiązania przyczyni się do zwiększenia udziału złomu i skrócenia czasu topnienia bez zmiany procesu technologicznego.
EN
This paper presents simulation and laboratory test results of an implementation of an infinite control set model predictive control into a three-phase AC/DC converter. The connection between the converter and electric grid is made through an LCL filter, which is characterized by a better reduction of grid current distortions and smaller (cheaper) components in comparison to an L-type filter. On the other hand, this type of filter can cause strong resonance at specific current harmonics, which is efficiently suppressed by the control strategy focusing on the strict control input filter capacitors voltage vector. The presented method links the benefits of using linear control methods based on a space vector modulator and the nonlinear ones, which result in excellent control performance in a steady state as well as in a transient state.
EN
The model predictive control (MPC) technique has been widely applied in a large number of industrial plants. Optimal input design should guarantee acceptable model parameter estimates while still providing for low experimental effort. The goal of this work is to investigate an application-oriented identification experiment that satisfies the performance objectives of the implementation of the model. A- and D-optimal input signal design methods for a non-linear liquid two-tank model are presented in this paper. The excitation signal is obtained using a finite impulse response filter (FIR) with respect to the accepted application degradation and the power constraint. The MPC controller is then used to control the liquid levels of the double tank system subject to the reference trajectory. The MPC scheme is built based on the linearized and discretized model of the system to predict the system’s succeeding outputs with reference to the future input signal. The novelty of this model-based method consists in including the experiment cost in input design through the objective function. The proposed framework is illustrated by means of numerical examples, and simulation results are discussed.
EN
Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non-minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.
EN
This paper proposes an improved Model Predictive Control (MPC) approach including a fuzzy compensator in order to track desired trajectories of autonomous Underwater Vehicle Manipulator Systems (UVMS). The tracking performance can be affected by robot dynamical model uncertainties and applied external disturbances. Nevertheless, the MPC as a known proficient nonlinear control approach should be improved by the uncertainty estimator and disturbance compensator particularly in high nonlinear circumstances such as underwater environment in which operation of the UVMS is extremely impressed by added nonlinear terms to its model. In this research, a new methodology is proposed to promote robustness virtue of MPC that is done by designing a fuzzy compensator based on the uncertainty and disturbance estimation in order to reduce or even omit undesired effects of these perturbations. The proposed control design is compared with conventional MPC control approach to confirm the superiority of the proposed approach in terms of robustness against uncertainties, guaranteed stability and precision.
EN
This paper investigates the application of a novel Model Predictive Control struc- ture for the drive system with an induction motor. The proposed controller has a cascade-free structure that consists of a vector of electromagnetics (torque, flux) and mechanical (speed) states of the system. The long-horizon version of the MPC is investigated in the paper. In order to reduce the computational complexity of the algorithm, an explicit version is applied. The influence of different factors (length of the control and predictive horizon, values of weights) on the performance of the drive system is investigated. The effectiveness of the proposed approach is validated by some experimental tests.
EN
Chatter is a series of unwanted and extreme vibrations which frequently happens during different machining processes and impose variety of adverse effects on the machine-tool and surface finish. Chatter has two main types namely forced-chatter and self-existed chatter. The forced-chatter has an external cause; however, self-exited chatter has no external stimuli, rather it is created due to the phase difference between the previous and current waves on the surface of the workpiece. Due to the self-generative nature of this type of chatter, its recognition and prevention is much more difficult. For preventing self-exited chatter its model should be available first. The chatter is usually simulated as a one degree of freedom mass-spring-damper model with unknown parameters that they should be determined somehow. In this paper, the parameters of the tool equation of motion i.e. mass, damping, and stiffness coefficients of the system are predicted through a wavelet-based method online, and then based on the achieved parameters, the system is controlled via Model Predictive Control (MPC) approach. For the validation, the algorithm is applied to 25 different experimental tests in which the acceleration of the tool and cutting force are measured via an accelerometer and a dynamometer. By investigation of the SLDs generated by the predicted parameters, the presented system identification method is validated. Also, it is shown that the chatter vibration is completely restrained by means of MPC. For investigation of the MPC performance, MPC algorithm is compared with PID controller and simulations has indicated a much stronger performance of MPC rather than PID controller in terms of vibration attenuation and control effort.
EN
This paper presents a control method for a four-leg three-level flying capacitor converter (FCC) operating as a shunt active power filter (SAPF), based on model predictive control with finite number of control states (FS-MPC). Current control and capacitor voltage balancing are described. Influence of the mismatch of the inductive filter model parameters on the current control precision is analysed. Results are supported by the experimental waveforms obtained with a 10kVA set-up.
EN
Nowadays, the increasing number of non-linear loads influences the grid, causing grid voltage disturbances. These disturbances may be very dangerous for the equipment and can create faults in converter behaviour. However, the right control algorithm can improve the reliability of the work. For a current source rectifier, the finite control set model predictive control has been proposed. This method is very flexible because of the variety of the possible cost function forms. It has been examined under grid voltage disturbed by the higher harmonics and the voltage drop. Simulation results prove the ability to damp the distortions and to ensure the unity power factor. Summing up, the algorithm is a very good solution for use in applications such as battery charging, active power filtering and low-voltage direct current load feeding.
EN
This paper proposes two different health-aware economic predictive control strategies that aim at minimizing the damage of components in a pasteurization plant. The damage is assessed with a rainflow-counting algorithm that allows estimating the components' fatigue. By using the results obtained from this algorithm, a simplified model that characterizes the health of the system is developed and integrated into the predictive controller. The overall control objective is modified by adding an extra criterion that takes into account the accumulated damage. The first strategy is a single-layer predictive controller with an integral action to eliminate the steady-state error that appears when adding the extra criterion. In order to achieve the best minimal accumulated damage and operational costs, the single-layer approach is improved with a multi-layer control scheme, where the solution of the dynamic optimization problem is obtained from the model in two different time scales. Finally, to achieve the advisable trade-off between minimal accumulated damage and operational costs, both control strategies are compared in simulation over a utility-scale pasteurization plant.
EN
In this paper, a fault-tolerant control (FTC) scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC) as well as set-based fault detection and isolation (FDI). In the class of MPC techniques, tube-based MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD) is passive by using invariant sets, while fault isolation (FI) is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs. After the system has been detected to become faulty, the input-constraint set of the MPC controller is adjusted to actively excite the system for achieving FI guarantees on-line, where an active FI-oriented input set is designed off-line. In this way, the system can be excited in order to obtain more extra system-operating information for FI than passive FI approaches. Overall, the objective of this paper is to propose an actuator MPC scheme with as little as possible of FI conservatism and computational complexity by combining tube-based MPC and set theory within the framework of MPC, respectively. Finally, a case study is used to show the effectiveness of the proposed FTC scheme.
EN
This paper presents model predictive control (MPC) with a finite control states set (FS) for three-level four-leg flying capacitor (FCC) converter. Principles of control method are presented and flying capacitor voltages control method is discussed. Experimental results of converter operating as a shunt active power filter (SAPF) and grid connected inverter (GCI) are shown.
EN
The main goal of this paper is to present a five-level converter with the feature of output voltage boosting capability. Thanks to its modular construction and single DC source usage, 5LCHB converter becomes an important alternative for two-level converters operating with DC-DC converters that use bulky inductors. Furthermore, model predictive control (MPC) method is presented, which allows for boosting output voltage of presented converter while providing three-phase load current control and flying capacitor voltage stabilization. The last section describes a 5kVA laboratory model of five-level hybrid converter interfacing RL load and shows experimental results confirming theoretical analysis derived in previous sections.
EN
This paper considers constrained predictive control with linearized model. The object of control is a chemical reactor with many equilibrium points. The analysis show how the linearization steady-state point and the values in reference signal influence the performance of control algorithm.
PL
W artykule podejmowany jest problem sterowania predykcyjnego z modelem otrzymanym w procesie linearyzacji w punkcie. Obiektem regulacji jest reaktor chemiczny z wieloma punktami równowagi. Analiza algorytmu pokazuje jak punkt równowagi użyty do linearyzacji oraz wartości sygnału zadanego, zmienianego skokowo, wpływają na jakość algorytmu sterowania.
EN
This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalized flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators’ availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.
EN
This paper presents a multilevel cascaded H-bridge 5-level converter with boosting capability. The standard solution for boosting voltage in power electronic devices is based on a DC-DC converter with a bulky inductor. However, inductor is a problematic component of a power electronic converter because usually it has to be individually designed and produced for every device and also because its size and weight do not allow for compact construction. This paper presents model predictive control (MPC) method that gives boosting capability for the presented converter. A novel contribution of this paper is the development of a predictive model of the converter and cost function enabling output current control and capacitor voltage balancing.
EN
Model Predictive Control (MPC) is a model-based control method based on a receding horizon approach and online optimization. A key advantage of MPC is that it can accommodate constraints on the inputs and outputs. This paper proposes a max-plus general modeling framework adapted to the robust optimal control of air traffic flow in the airspace. It is shown that the problem can be posed as the control of queues with safety separation-dependent service rate. We extend MPC to a class of discrete-event system that can be described by models that are linear in the max-plus algebra with noise or modeling errors. Regarding the single aircraft as a batch, the relationships between input variables, state variables and output variable are established. We discuss some key properties of the system model and indicate how these properties can be used to analyze the behavior of air traffic flow. The model predictive control design problems are defined for this type of discrete event system to help prepare the airspace for various robust regulation needs and we give some extensions of the air traffic max-plus linear systems.
EN
In this paper, a multivariable model based predictive control (MPC) is proposed for the solution of load frequency control (LFC) in a multi-area interconnected power system. The proposed controller is designed to consider time delay, generation rate constraint and multivariable nature of the LFC system, simultaneously. A new formulation of the MPC is presented to compensate time delay. The generation rate constraint is considered by employing a constrained MPC and economic allocation of the generation is further guaranteed by an innovative modification in the predictive control objective function. The effectiveness of proposed scheme is verified through time-based simulations on the standard 39-bus test system and the responses are then compared with the proportional-integral controller. The evaluation of the results reveals that the proposed control scheme offers satisfactory performance with fast responses.
EN
The objective of this paper is to present a modified structure and a training algorithm of the recurrent Elman neural network which makes it possible to explicitly take into account the time-delay of the process and a Model Predictive Control (MPC) algorithm for such a network. In MPC the predicted output trajectory is repeatedly linearized on-line along the future input trajectory, which leads to a quadratic optimization problem, nonlinear optimization is not necessary. A strongly nonlinear benchmark process (a simulated neutralization reactor) is considered to show advantages of the modified Elman neural network and the discussed MPC algorithm. The modified neural model is more precise and has a lower number of parameters in comparison with the classical Elman structure. The discussed MPC algorithm with on-line linearization gives similar trajectories as MPC with nonlinear optimization repeated at each sampling instant.
PL
Artykuł przedstawia wyniki wstępnej analizy możliwości zastosowania sterowania predykcyjnego MPC turbiną parową elektrowni jądrowej. Tradycyjnie przyjmuje się, że turbina pracuje w jednym punkcie pracy odpowiadającym jej mocy nominalnej, co pozwala na stosowanie klasycznych regulatorów PID. Synteza sterowania dla warunków zmiennego punktu pracy wymaga uwzględnienia nieliniowego charakteru procesów turbiny oraz możliwości naruszania przez generowane sterowanie ograniczeń dopuszczalnego działania. W algorytmie MPC wykorzystany został opracowany wcześniej nieliniowy model turbiny 4CKC465. Opracowany algorytm MPC został porównany z regulatorem PID strojonym dla trajektorii zadanej. Proponowane sterowanie MPC umożliwia sterowanie turbiną z zadowalającymi skutkami. W artykule dokonano krótkiej analizy wpływu parametrów algorytmu MPC na jakość sterowania turbiną parową.
EN
The results of a preliminary analysis of the model predictive control (MPC) in steam turbine of nuclear power plant applicability was presented. Traditionally it is assumed that the turbine works in single operating point corresponding to its nominal power, what allows the usage of classic PID controllers. Synthesis of control for varying operating point conditions requires taking under consideration a nonlinear character of processes taking place within steam turbine as well as possibility of control constraints violation. In MPC algorithm, previously developed nonlinear model of 4CK465 steam turbine was used. Designed MPC algorithm was compared with PID controller tuned for a given trajectory. MPC control proposed in this paper gives satisfactory results of steam turbine control. In this article a brief analysis of the MPC algorithms parameters impact into control quality of steam turbine was presented.
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