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Conventional power generation and usage are undergoing significant changes due to the integration of renewable energy sources into the power distribution network of a microgrid system. This system integrates renewable and conventional distributed generation, storage systems, and loads into a single entity that operates in isolated and grid-connected modes. First, however, energy management strategies are required. In this paper, a robust model predictive control (MPC) technique is proposed to optimize the energy management system of a standalone hybrid microgrid consisting of an energy storage system (ESS) and a diesel generator (DG) structure for tracking a reference load. The weights of the MPC are optimized using a genetic algorithm, and the modes of the ESS are optimized using convex logic. The simulation results demonstrated a reliable tracking of the reference load by the hybrid power generators. At the same time, the MPC maximized the output power of the energy storage system and minimized the use of diesel generator to eliminate emissions associated with diesel engine generators. [ABSTRACT FROM AUTHOR]

This paper presents a decentralised model predictive control (DMPC) for two-input and two-output (TITO) processes. To reduce the computational load, shifted input sequence is used to cater for loop interactions. The proposed scheme is applied to a coupled system to demonstrate the performance the DMPC. Model predictive control (MPC) and decentralised PID (PI) were also applied for comparison purposes. [ABSTRACT FROM AUTHOR]

This paper proposes a simple model predictive inner-loop current controller for regulating the output voltage of a three-phase voltage-source AC/DC converter under the two-loop strategy. The solution of the MPC can be given analytically by minimizing an energy function of the tracking error, and its global stability is guaranteed in the presence of the input constraint. For the outer-loop, a PI controller is utilized to regulate the output voltage without offset error. The simulation results show that the current and the output voltage regulation are achieved in a satisfactory manner despite the load and the parameter changes. [ABSTRACT FROM PUBLISHER]

This paper presents a control scheme in order to manage a laboratory-scale microgrid in a economic way. The main aim of this paper is the comparison between different control strategies. This microgrid is located in Seville (Spain) and incorporates an electrolyzer, metal hybrid storage, fuel cell and a battery set, as well as electronic load and generator as main components. This allows us to study smartgrids with different renewable sources (emulated by the electronic generator) and energy storage based on hydrogen under realistic scenarios. The control objective is to regulate the plant fulfilling the energy demand. This is posed as a tracking problem to be solved. A first approach is a two layer controller composed by a scheduler layer and an offset free LQR layer with output feedforward. This controller provides nice results and although its control layer does not take into account the operational limits of the plant, it gets the best performance from a optimal point of view. A one layer model predictive control for tracking has also been designed and tested in order to increase the performance of the first approach. The obtained results show that this last approach is a good control technique for this class of plants. [ABSTRACT FROM PUBLISHER]

Model Predictive Control (MPC) schemes are now widely used in process industries for the control of key unit operations. Linear model predictive control schemes which make use of linear dynamic model for prediction, limit their applicability to systems which exhibit mildly nonlinear dynamics. In this paper, a state estimation based model predictive controller for nonlinear system has been proposed. The model predictive controller is designed by considering a state space model and an extended Kalman filter to predict the future behavior of the system. The efficacy of the proposed MPC scheme has been demonstrated by conducting simulation studies on the level process of a Continuously Stirred Tank Reactor (CSTR) — a MIMO system, and the real time implementation has been done in the CSTR plant to illustrate the online optimization constraint and also the advantage of MPC over conventional controller by comparison of servo-regulatory responses through ISE values. [ABSTRACT FROM PUBLISHER]

This paper presents a model-based fault-tolerant approach for designing a control strategy in order to integrate a diesel engine generator (DEG) as master generation unit, voltage and frequency leader, in an islanded microgrid configuration. The microgrid design is mainly composed of a hybrid wind-diesel-photovoltaic power system with a battery storage system (BSS). A model predictive control (MPC) scheme has been selected for this task, due to its flexibility and capability for handling constraints. Fault-tolerance is achieved in the DEG control system with the addition of a fault detection and diagnosis (FDD) module to the MPC structure, in order to reconfigure the control strategy when actuator faults in the DEG are present. Different operating conditions of the microgrid were simulated in order to test control robustness. Improved performance over a baseline controller, IEEE type 1 exciter, is achieved. Dynamic models of the microgrids components are presented and simulation results of the microgrid behavior in Matlab/Simulink®. [ABSTRACT FROM PUBLISHER]

Sample/Hold (S/H) time plays an important role in the implementation of Model Predictive Control (MPC). The purpose of this paper is to investigate how the S/H time effects the size of initial feasible set in MPC design. Many factors influence S/H time choice, e.g. the process dynamics, the computational power of the implementation platform, etc. An illustrated example is given to show how to select S/H time in MPC designs, especially from the perspective of trade-off between their on-line computational complexity, initial feasible set, and closed-loop performance. [ABSTRACT FROM PUBLISHER]

Multi-class emission models aim at capturing the heterogenous nature of traffic flows. These models are helpful for improving the efficiency of the reduction of emissions. We develop a macroscopic emission model that takes into account the multi-class nature of traffic. This results in a integrated macroscopic multi-class traffic flow and emission model called the multi-class VT-Macro model. Model predictive control is adopted to reduce the total time spent and the total emissions. End point penalties are included in the objective function, taking into account the different distances to the destination of different vehicles. A case study based on a benchmark network is implemented. The simulation results show that better performance is obtained by using the multi-class VT-macro model and end point penalties. [ABSTRACT FROM PUBLISHER]

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies have a growing presence and are frequently being connected at distribution level. Unfortunately, a renewable energy source is frequently intermittent and any kW provided by these technologies should not be wasted. Currently, optimization of energy consumption is a world target and it is no longer feasible to design a system without considering it. In this paper, the methodologies applied to the thermal house comfort in an environment with limited energy sources are based on a predictive control structure associated to a PI controller. Thus, the available and necessary power to cool and heat the house within the desire "comfort zone" is partially available and, consequently constrained. The system also uses the house thermal inertia to "accommodate thermal energy" within the selected temperature range, maintaining the comfort when no energy is available and reducing the energy consumption without loss of the thermal comfort. The simulation results obtained with traditional PI and with Model-based Predictive Control (MPC) are presented and it can be seen that the system is able to provide high-energy conservation and reliable comfort. [ABSTRACT FROM AUTHOR]

The use of fuel cell as future energy for vehicle application is very promising due to its environmentally friendly, efficient, and flexible properties. When applied to the vehicle, one drawback of fuel cells is that it is unable to supply sudden load changes since the dynamics of fuel cells is slow. However, this could be compensated by adding energy storage systems (batteries and/or supercapacitor). To supply the load, fuel cell and energy storage system will be controlled by a DC-DC converter. The constancy of DC output voltage (DC bus) in hybrid vehicle is very important even with large and rapid load changes. In addition, the increase in electrical current in fuel cells and batteries must be limited to extend their lifetime. In this study a control system was designed and implemented to regulate fuel cell as a primary energy source as well as batteries and supercapacitor as energy storage systems in order to obtain a constant DC output voltage and to limit current slope of fuel cell and batteries. There were three DC-DC converters deployed to regulate the output voltage of the three energy sources namely the boost converter, used to adjust the voltage/current output of the fuel cell, and two bidirectional converters, used to adjust the voltage/current output of energy storage systems (batteries and supercapacitor). To determine the reference current of each converter Model Predictive Control (MPC) was employed, and hysteresis control was functioned to track the reference current. MPC and hysteresis control were implemented on a dSPACE DS1104 Controller Board. For the experiment, small energy sources were operated, which were 50 W 10 A fuel cell, 6 V 4.5 Ah battery, and 7.5 V 120 F supercapacitor. Simulation and experiment results showed that the MPC can be designed to limit the current slope in fuel cells and batteries and to keep the bus voltage within the reference value. [ABSTRACT FROM PUBLISHER]

The paper focuses on maximizing the economic performance (throughput) of industrial printers under changing operating conditions. An interesting problem for these printers is the cold start, when printing components have to be heated up as soon as possible to have minimum delay for the first print. To maximize the throughput of these machines, it is important to determine an optimal heating strategy, as temperature considerably influences the print quality. Also when switching between different media types, the print quality has to be guaranteed. A input dependent model of the system can catch the dynamics for the entire process. A control approach is proposed to maximize the throughput while guaranteeing quality of the system. It is based on a nonlinear dynamic input-dependent predictive controller. MPC is a good choice to control the behavior of printers, because it can deal in an optimal way with hard constraints and available information of the predicted print queue. Simulation examples show the effectiveness of the proposed approach in the presence of variation in the constraints and in the print queue. [ABSTRACT FROM PUBLISHER]

Model Predict Control (MPC) algorithm is limited in practical due to its high online computational requirement, especially for real-time control systems. This paper introduces a novel one bit processing based MPC (OBMPC) controller, which can significantly reduce online computational burden. Additionally, the one bit nature of the OBMPC make the controller particular suitable to Wireless Sensor and Actor Networks (WSANs). The simulation on a virtual satellite system has provided performance and SNR analysis for OBMPC controller and showed the satisfying result of OBMPC controller. [ABSTRACT FROM PUBLISHER]

The vector control method is often applied to speed regulating system of Permanent Magnet Synchronous Motor (PMSM). Control period of speed loop is usually longer than that of current loop. Different regulating algorithms can be applied to speed and current loops. The difference between the control periods of two control-loops in PMSM servo is becoming smaller. Two control-loops structure can be replaced by single control-loop structure. In such a case, model predictive controller (MPC) in the single control-loop based on the second-order relationship between voltage and speed of PMSM is developed in this paper. Moreover, considering the case of large range inertia variation, adaptive model prediction control based on inertia identification is designed for PMSM speed regulation system. Simulation results show that model predictive controller designed in this paper supplies better performance of dynamic behavior and load disturbance rejection compared with model predictive controller in speed loop of two control-loops structure. Adaptive model prediction control method can restrain influence of large range inertia variation in speed regulation system. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on the dual-axis solar-tracker control problem, study the algorithm of solar position, and design a predictive controller to make the location of solar panels track the sun in a smooth and accurate way, which is on the premise of the constraint conditions. Use MATLAB / SIMULINK software to complete the location of solar panels predictive control simulation. The simulation results show that the predictive controller designed in this paper turn out to be a good tracking performance under the premise. Additionally, use the configuration software as system monitoring software. [ABSTRACT FROM PUBLISHER]

In this paper, a novel model-based treatment control system that manipulates the focal zone trajectory and intensity of ultrasound phased arrays to safely deliver prescribed thermal dose to the target in the minimum-time was developed. The system is characterized by its time-optimal performance, direct incorporation of normal tissue constraints, and computationally efficient implementation even in the presence of massive amount of magnetic resonance (MR) thermometry measurement data. A set of empirical basis functions is identified during pre-treatment planning using proper orthogonal decomposition (POD). Using the same reduced-order basis, thermal images are compressed and used in the feedback of the treatment controller. The controller has a cascaded structure, with a nonlinear thermal dose controller in the outer loop and a low-dimensional model predictive controller (MPC) in the inner loop. The controller solves on-line optimization problem, which allows for robust delivery of the desired thermal dose despite modeling errors and disturbances. The feasibility and performance of the developed control system were tested in a computer simulated treatment of a three-dimensional (3D) inhomogeneous tissue with ultrasound transducer arrays. [ABSTRACT FROM PUBLISHER]

For virtual satellites, the main challenge for the attitude determination and control module is to maintain the performance with weak wireless inter-satellite links (ISL). Such weak ISL leads to large data loss, high bit error rate, and long transmission delay during the real-time control process, which beyond the competence of traditional linear control algorithms. In this paper a constrained model predictive controller with learning observer is developed. The simulation results provided performance comparison between MPC and PD controller and showed the advantage of MPC controller for this situation. [ABSTRACT FROM PUBLISHER]

The main objective of this paper is to address the tracking control problem of a ship moving with a constant velocity along a desired path. To this end, a combination of mathematical model (MM) and statistical model, namely the expanded exponential weight wavelet network based ARX (E-EW-WNN-ARX) model is proposed as internal predictor of a model predictive controller (MPC). Firstly, the EW-WNN-ARX model is used to describe the ship motion between the difference of heading angle deviation and the rudder angle of the ship. To represent the ship's motion nonlinearity, the rolling angle is used as the EW-WNN-ARX model index to allow the model parameters to vary with the ship moving state. The EW-WNN-ARX model is identified off-line by using previously observed real data. Then The difference of heading angle deviation are expanded and integrated into mathematical model (MM) characterizing the position tracking error of the ship to form the E-EW-WNN-ARX-MM model which is used to develop a state space model that represent the ship tracking motion behavior. Finally, the identified ship's state-space type tracking motion model is used as predictor of a model predictive controller to steer ship moving forward with constant velocity along a predefined reference path. The effectiveness of the proposed modeling and control methods are demonstrated by the tracking control simulation in which the modeling data was obtained from the Shioji-maru experimental ship of Tokyo University of Marine Science and Technology of Japan. [ABSTRACT FROM PUBLISHER]

In order to reduce the fatal damage of tire blowout during vehicle operation, a differential flatness based model predictive controller is proposed. A 3 degrees of freedom (DOF) vehicle dynamics model with tire blowout is established, and then the flat outputs are given. The flatness property of the vehicle dynamics is discussed accordingly, and the MPC for the vehicle with a tire blowout stability control is formulated, where the lateral acceleration is considered as safety constraint. Simulations of vehicle with a tire blowout driving on the different road are carried out to verify the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

The paper introduces a hierarchical structure for the finite time constrained optimal control of a system with a fuel cell and a supercapacitor storage unit. The supercapacitor storage unit is employed to improve life time, reduce hydrogen consumption of the fuel cell, increase peak power and allow for some regeneration from the load. The three control layers regulate the supercapacitor storage unit charge, the DC bus voltage level and the currents of the fuel cell and supercapacitor storage unit. They are all based on explicit linear offset-free model predictive control, where the optimal control law is computed off-line and stored in a look-up table. [ABSTRACT FROM PUBLISHER]

B-Melter is a process which is very difficult to control by classical means. The output (cane juice) from the crusher is send to the pan house having sections namely A, B and C pans. The high quality sugar are get from only A-pan house and the juice from the B-Pan house normally called as B seeds (sugar), consists of B seeds and B-heavy molasses, here the viscous of Massecuite is normally high. So, in order to maintain the viscous of Massecuite Melter process is normally employed in sugar industry. Here the viscous of Massecuite get deduced up to 60 to 65 brix by adding the steam and hot water with the Massecuite and this temperature control will ensure the melting ratio of the Massecuite. The Mathematical model for the above process and the tuning parameters for PID controller are designed using the conventional technique. In order to reduce the process time and other unwanted disturbances, the Model Reference Adaptive Control (MRAC) and Model Predictive Control [7] has been designed and the controller response are verified with the Matlab Simulation results and it can be adaptively adjusted online for varying state of the system and changing operating conditions. This paper focuses a method for developing control algorithm for the control of such a process. [ABSTRACT FROM PUBLISHER]

In this paper, a multi-step simulation tool for the evaluation of operation strategies with predictive concepts for controllable distributed generators is proposed and applied exemplarily to perform an annual simulation of a sewage gas CHP trading at a power exchange. The analysis quantifies the disturbing effect of prediction errors as compared to a simulation without predictive elements. The simulation recognizes the prediction error of the thermal load, the gas production and the price at the power exchange as an unavoidable disturbing effect. This results in a less profitable schedule than the optimum and requires compensation by utilizing balancing energy. The comparison of the simulation without prediction error and the one with prediction error shows the significance of the disturbing effect. The results show that it is necessary to include prediction errors for the analysis of the economic potential of distributed generators taking part at day-ahead markets. [ABSTRACT FROM PUBLISHER]

This paper studies PID control for non-square systems. A non-square system has unequal numbers of inputs and outputs and there exists coupling among its PID controllers. The coupling can't be simply eliminated using conventional decoupling methods for square systems, thus it is hard to tune PID controllers for non-square systems. In this paper, we adopt predictive control for non-square systems since predictive control method doesn't assume that the dimensions of inputs and outputs are equal and it is suitable for the control of non-square systems. To implement the final controller in practice, the paper proposes to reduce model predictive control(MPC) into PID control. The method is applied to a voltage model of Proton Exchange Membrane Fuel Cell (PEMFC). Simulation results show that the method can achieve better load rejection ability and robustness compared with traditional methods. [ABSTRACT FROM PUBLISHER]

The application of fence methodology on the optimal operation of heavy haul trains through model predictive control (MPC) approach is investigated in this paper. This is mainly motivated by the fact that without fencing, the large number of cars in a long heavy haul train present difficulties in solving the train optimal operation problem under MPC framework. Firstly, the large car number leads to a problem with a large dimension, which slows down the optimization procedure in MPC. Secondly, the signal channels in a train is limited physically so that individual manipulation of each car is physically infeasible. The fixed fence method is employed and its influence on the optimization problem and performances of the train is analyzed. Simulation is carried out to demonstrate the advantage of fence technology. It is shown that with a large fence factor, the complexity of the optimization is largely reduced as well as the computation time and memory demand in the real time implementation. Simulation verifies that it is possible to use fence method to facilitate the implementation of MPC approach for heavy haul trains with little sacrifice of the train's performances. In addition, it is analyzed and demonstrated by simulations that the fence factor, however, should be chosen carefully. [ABSTRACT FROM PUBLISHER]

This paper deals with the Mixed Logical Dynamical (MLD) approach. It allows to model the hybrid systems involved continuous, discrete dynamics and constraints. The resultant system is well-posed for Model Predictive Control (MPC) that is used as control strategy for the system. An active Fault Tolerant control is proposed. This approach is illustrated by a tank system. Simulations are performed using the Hysdel compiler to show the efficiently of this formalism and the fault tolerant MPC control. [ABSTRACT FROM PUBLISHER]

In this paper the Model Predictive control (MPC) for the improvement of the power system stability is presented. Particularly, the paper focus on the application of the MPC technique to regulate the power output of the generators when the system is subject to fault and disturbances. The application on the Single machine infinite bus equipped with Thyristor Controlled Series Capacitor device is carried out to show the advantage of the proposed technique. [ABSTRACT FROM PUBLISHER]

With continuing miniaturization and more prevalent use of Ball Grid Array (BGA) packages in the microelectronics industry, the mechanical strength of Printed Circuit Board Assemblies (PCBAs) with BGA packages is becoming very critical. In a variety of applications ranging from smart phones to high end switches and routers, the mechanical robustness of PCBAs is becoming the key differentiator in product reliability. To enable a standard way to benchmark mechanical reliability, the IPC/JEDEC-9702 test method was developed several years ago. The test method has helped compare different PCBAs to optimize overall mechanical reliability. However, a key challenge with mechanical testing is that by the time the testing is performed, it is too time consuming and expensive to make changes to the package if it's mechanical reliability needs improvement. In this study, bending tests for various BGA packages have been conducted based on the IPC/JEDEC-9702 standard, under different applied deflections to obtain the required data for studying the failure modes. Three-dimensional Finite Element Models (FEM) were developed to understand the relationship between PCB strain and solder joint strain. The strain values at different locations in the PCBA were estimated from the model, and compared with experimental data. Good agreement between model predictions and experimental data has been obtained. Then, a series of FEM analyses were performed with different design parameters. High performance cluster computing was used to reduce computation time without compromising accuracy. Finally, based on the results presented in this study, optimization analysis was performed to improve BGA mechanical reliability for any given geometry. [ABSTRACT FROM PUBLISHER]

The paper discusses economic performance design under nonlinear dynamic behavior and changing operating conditions. Two methods to control plants with input-induced nonlinearities in the presence of hard constraints are presented. A nonlinear predictive controller is designed based on a nonlinear dynamic parameter-dependent model of the system for tracking control. Next a decomposition of the model of the plant is made which allows nonlinear optimal static feedforward and linear dynamic feedback. Model predictive control (MPC) was selected to control the transients while satisfying all constraints without loss of performance. The control scheme has been applied on an industrial printing system. Simulation examples show the effectiveness of the proposed approaches in the presence of variation in the input print queue and constraints. [ABSTRACT FROM PUBLISHER]

In a virtual satellite, communications between any two spacecraft modules may be established on weak wireless inter-satellite using limited resources. The challenges for the attitude determination and control module include maintaining the performance with large data loss and time delay, limited on-board computational resources and hard constraints due to the limited torques that can be provided by actuators on slave satellite sides. In this paper, a modified constrained model predictive controller is developed for the virtual satellite attitude mission. The performance and stability of the MPC controller has been analyzed in this work and the simulation results showed good adoptability of MPC controller for the proposed situation. [ABSTRACT FROM PUBLISHER]

As for MPC controllers, hard-constraint such as constraints on input magnitude or/and rate are generally regarded as an inviolable rule that has to be satisfied strictly before the cost function is optimized at each sampling instant. Such strategy is to result in control conservatism of MPC, as well as infeasibility problem. In this paper, constraint-softening technique is proposed, in which hard-constraint are relaxed appropriately and hence the region of hard-constraint is enlarged directly in optimizer of MPC. As a result, transient performance of the resulting system is significantly improved. Meanwhile, the feasibility problem is solved via this approach. A simulation result demonstrates the effectiveness of the proposed constraint-softening technique. [ABSTRACT FROM PUBLISHER]

In this paper the use of proportional-integral-derivative (PID) switching controllers is proposed for the control of a magnetically actuated mass-spring-damper system which is composed of two masses M1 and M2; each mass is jointed to its own spring. Two different modes occur during the system motion; a PID controller is designed for each mode and a switching logic is applied in order to recognize the system's position to switch to the proper controller. Finally, simulation results are employed to show the performance of the proposed switched PID controller. Also, comparison results with the previously used model predictive controller (MPC) are provided. [ABSTRACT FROM AUTHOR]

In this paper control of the xy pedestal axes has been studied which is a two degrees of freedom earth station antenna of pedestal HDF family group (High Dynamic Full Motion Leo Satellite Tracking Pedestals). This system model is achieved by using the Dymola software that according to the comparisons which have been done is very close to the actual system model and has very high accuracy. Purpose: is to track a LEO orbit satellite that of passing satellites and angles related to the antenna have been extracted path from KNTUSAT software. In carried out simulation, the operation of PI controller has been optimized, MPC and GPC has been studied. The results of comparison between simulation methods show that predictive controller has had fewer errors in satellite tracking and has been shown less control effort and also has had good behavior in disturbance rejection. [ABSTRACT FROM PUBLISHER]