Model Predictive Control (MPC) Research Papers (original) (raw)

- by Silvia Siri and +1
- •
- Model Predictive Control (MPC), Traffic control, Accounting and Auditing, Traffic Flow

Keywords: MIMO antenna UWB antenna Model predictive Control Non-uniform microstrip line Printed monopole antenna a b s t r a c t A novel ultra wideband (UWB) printed monopole multiple-input multiple-output (MIMO) antenna with non-uniform transmission line using nonlinear model predictive control (NMPC) is presented. The proposed antenna is superior to conventional antennas in terms of dimensions, gain, and efficiency while maintaining the impedance bandwidth. In order to improve the results, a non-uniform transmission line has been used for impedance matching between the radiated patch element and the coaxial cable. For designing the non-uniform transmission line, it has been expanded using cosine terms. Regarding the presence of differential equation for the variation in the impedance of the transmission line and its transformation to the state-space equation, NMPC has been employed to design the transmission line and determine the cosine expansion coefficients. Two base antennas, as MIMO, were simulated configuration and fabricated. The surface area of the proposed MIMO antenna is 0.99 k 2 g , the wavelength has been obtained for the center frequency of the 3.16 GHz to 10.6 GHz range, and its mutual coupling, peak gain, channel capacity loss (CCL), total active reflection coefficient (TARC), mean effective gain (MEG) and diversity gain (DG), envelope correlation (ECC) are acceptable. The simulation and measurement results are in good agreement, and the proposed antenna is suitable for MIMO applications. Ó 2020 The ''Authors". Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

The paper proposes an adoption of slope, elevation, speed and route distance preview to achieve optimal energy management of plug-in hybrid electric vehicles (PHEVs). The approach is to identify route features from historical and real-time traffic data, in which information fusion model and traffic prediction model are used to improve the information accuracy. Then, dynamic programming combined with equivalent consumption minimization strategy is used to compute an optimal solution for real-time energy management. The solution is the reference for PHEV energy management control along the route. To improve the system's ability of handling changing situation, the study further explores predictive control model in the real-time control of the energy. A simulation is performed to model PHEV under above energy control strategy with route preview. The results show that the average fuel consumption of PHEV along the previewed route with model predictive control (MPC) strategy can be reduced compared with optimal strategy and base control strategy.

The present study deals with the analysis of a Lotka-Volterra model describing competition between tumor and immune cells. The model consists of differential equations with piecewise constant arguments and based on metamodel constructed by Stepanova. Using the method of reduction to discrete equations, it is obtained a system of difference equations from the system of differential equations. In order to get local and global stability conditions of the positive equilibrium point of the system, we use Schur-Cohn criterion and Lyapunov function that is constructed. Moreover, it is shown that periodic solutions occur as a consequence of Neimark-Sacker bifurcation.

- by Tobias Geyer and +1
- •
- Power Electronics, Model Predictive Control of Hybrid Systems, Model Predictive Control (MPC)

This paper deals with the design of a hierarchical control scheme for complex islanded Alternate Current (AC) microgrids. The proposed solution relies on the combined use of Model Predictive Control (MPC) and Sliding Mode Control (SMC). The model of the microgrid includes several Distributed Generation Units (DGus), affected by unknown load dynamics and modelling uncertainties. Moreover, they are connected according to an arbitrary complex and meshed topology, taking into account the interconnecting line dynamics. The proposed control scheme consists of two control loops. A centralized MPC supervisor generates the voltage reference values for each DGu, while fulfilling input and state constraints on the basis of a reduced order model of the plant. A Suboptimal Second Order Sliding Mode (SSOSM) control is locally designed for each DGu to track, in a decentralized way, the voltage references generated by the supervisor. Simulation results confirm the effectiveness of the proposed control scheme.

- by Gian Paolo Incremona and +2
- •
- Model Predictive Control (MPC), Sliding mode control, Microgrids
- by Lorenzo Maggi and +2
- •
- Model Predictive Control (MPC), Traffic control

Automated planning and scheduling are increasingly utilised in solving evsery day planning task. Planning in domains with continuous numeric changes present certain limitations and tremendous challenges to advanced planning algorithms. There are many pertinent examples to the engineering community; however, a case study is provided through the urban traffic controller domain. This paper introduce a novel hybrid approach to state-space planning systems involving a continuous process which can be utilised in several applications. We explore Model Predictive Control (MPC) and explain how it can be introduce into planning with domains containing mixed discrete and continuous state variables. This preserves the numerous benefits of AI Planning approach by the use of explicit reasoning and declarative modelling. It also leverages on the capability of MPC to manage numeric computation and control of continuous processes. The hybrid approach was tested on an urban traffic control network to ascertain it practicability on a continuous domain; the results show its potential to control and optimise heavy volumes of traffic.

Abstract: We investigate another point of view to perform a new controller named Relax
controller (RelaxC). It allows to control nearly all kinds of problems: pure integrator, constraints
on the control and state of the system, non minimal phase, small and large dead-time, SISO or
MIMO systems and unstable processes. All this, without knowing exactly the structure of the
system to be controlled: just with one classic and physical parameter. This new methodology
seems to federate or explain a numerous controller design methods. When RelaxC works in
degraded operating mode we obtain a well-balanced RelaxCPID. RelaxC is also a mathematics
methodology that allows to compute all the family of PIDn that we want.

Outline the synthetic pathway for the formation of: a. 1, 2 – dibromocyclopentane from cyclohexane. b. chlorocyclohexane from cyclohexanol. SOLUTION A synthetic pathway means a series of proposed steps to go from a defined set of reactants to a specified product, stating the conditions as well as the mechanism involved. a. Cyclopentane + bromine = 1, 2-dibromocyclopentane. Note: this is a radical substitution b. Cyclohexanol (dehydration) = cyclohexene + HCl = chlorocyclohexane. 12. A = CH 3 CH 2 CH 2 Br B = CH 3 CHCH 2 C = CH 3 CHO D = CH2O You can get the necessary reaction equations from a Chemistry textbook.

- by Rasheed O Kelani
- •
- Model Predictive Control (MPC)

The variability of photovoltaic (PV) energy because of atmospheric conditions dependency necessitates employing a maximum power point tracking (MPPT) technique in the installed PV systems. So in this paper, finite control set model predictive control (FCS-MPC) is used to extract the maximum power from the PV source using a boost converter as interfacing circuit. The proposed technique combines FCS-MPC with an extended Kalman filter (EKF) to reduce the number of required sensors. The EKF is used to estimate both of the PV current and the capacitor voltage. This eliminates two sensors circuits from the PV system, which decreases the system cost. The proposed technique is validated by simulation results under different operating conditions.

Conventional predictive direct torque control (PDTC) of permanent magnet synchronous generators (PMSGs) with weighting factors suffers from extensive calculation burden and weighting factor tuning work. This paper proposes three predictive direct torque control techniques for PMSGs without weighting factors. The first proposed control strategy is based on the torque and reactive torque. Reactive torque has the same order of magnitude as torque, which eliminates the need of weighting factor in the cost function. The second proposed control strategy is based on estimating the q-axis reference current from the demanded torque and setting the d-axis reference current to zero, which achieves the maximum torque per ampere (MTPA) operation. Then, according to the deadbeat principle the reference voltage vector (VV) is directly computed from the reference current vector. The cost function design for this strategy includes only the reference voltage vector and the candidates one. The third proposed method is based on two separate cost functions, one is for the torque and another is for d-axis current. The proposed three control strategies eliminates the need of weighting factors and therefore, no tuning work is required.

- by Prodyut Das
- •
- Motor Control, Model Predictive Control (MPC), direct torque control (DTC), Inverter

It has never been accomplished to describe our behavior mathematically. Due to the fact that human behavior is highly erratic even the understanding of its causes are still sketchy. Assuming that we are all equal in our regulation of thought and behavior there are simply too many differences and partially inconsistencies, the attempts stopped in its onset. For having defined the five major groups of mankind, the sociopath, the artist, the median, the, and the psychopath［1］each group is related to each other but his decision making is hardwired differently and thou probably more easy to grasp than sticking to the consistency of all appearance for the regulation of behavior is quite similar in the five groups but different in its limits. Using the more formalistic tools of mathematics, this can open the review of the equations in verifying or falsifying the predictions on future behavior in an individual, at least after defining the group affiliation. Therefore a self-test has been established［2］to predetermine the group. In a hybrid-species ［3］, the eight main neuro-receptors in each group to have two optional origins. Measuring each by its own dominating patterns not only the amalgamation in each group can be defined but also the native patterns of the non-hybrid ancestry ［4］. Not only the variance of possible combination can distribute to the limits of brain-equations but also the time-axis of our memory, being rather different, illuminating the highly different decision making among offspring of hunters and farmers. A phenomenon probably explaining the variance in processing memory by peripheral distributed groups of ADHD and autism for ADHD memorizes in combining data with importance and such is been given an emotional response to the recall, while autism is mainly been given the exact time reference stored in a continuous frame of time-preference. The latter therefore have problems to distinguish between important and not important and the former lacks a passing timeframe, mirroring the primary form of acquiring resource, farming or hunting. With the boundaries set the graphs of the equation on resource-projection looks highly different by only changing its limits. Having not only focussed on the designation of human groups but also in behavioral shifts over time (from social to non-social) and the exact denomination of similar behavior, some rather simple equation could be defined to predict and consequently proof the predicate. Not only the proper use of words is necessary but also awareness that non-social individuals will often not answer truthfully. A topic that also can be mathematically predicted. The outcome will revolutionize our perception of mankind and ourselves, dawning more than one academic discipline, probably enabling us to go virtual and back again.

This paper investigates the model predictive control (MPC) for an autonomous underwater vehicle (AUV). We aim to develop a tracking control algorithm integrated with a dynamic path planning for the AUV. Considering that the effective range of onboard sensors cannot be large, we formulate the path planning problem into a receding horizon optimization framework with spline path templates. Once the local optimal path is constructed for the current time, it is viewed as a reference trajectory of the vehicle. In order to control the depth of AUV simultaneously and to have a friendly interaction with the dynamic path planning method, a nonlinear model predictive control (MPC) scheme is adopted. The simulation results demonstrate the effectiveness of the proposed tracking control algorithm.

- by Chao Shen
- •
- Model Predictive Control (MPC), Auv Control

In this paper, A new load frequency control (LFC) using the model predictive control MPC technique is presented. The MPC technique has been designed such that the effect of the uncertainty due to governor and turbine parameters variation and load disturbance is reduced. A simplified frequency response model is introduced, and physical constraints of the governor and turbine are considered in this model. The model was employed in the MPC structure. Digital simulations for a single control area are provided to validate the effectiveness of the proposed scheme. The results show that, with the proposed MPC technique, the system performance has a good robustness in the face of uncertainties due to governor and turbine parameters variation and load disturbance. A performance comparison between the proposed controller and a conventional integral control scheme is carried out confirming the superiority of the proposed MPC technique.

- by SMGRC UOK and +1
- •
- Model Predictive Control (MPC)

This paper deals with the control laws
recon guration of nonlinear systems, by using a
Fuzzy-Model-based Predictive Control (FMPC). It
should be noted that the studied systems are writ-
ten in the quasi-linear parametric varying (quasi-
LPV) form. This FMPC strategy is developed to
preserve closed-loop stability in the nominal and
actuator faulty case. Fault accommodation by per-
turbations rejection is presented. This step is done
by interpolation-based control to cover the entire
area of operation. To allow the process to maintain
current performances closed to desired performances,
a dynamic optimizer is used. Our contribution comes
from the combination of several aspects: fuzzy model,
quadratic programming and faults decoupling princi-
ple. The linearization around a family of equilibrium
points is also studied. The operating points are appro-
priately con gured by a set of variables called premise.

Las estrategias de control predictivo han cobrado gran interés en los últimos años por parte de la comunidad científica, dado que permiten incorporar índices de funcionamiento de los procesos, tales como de la calidad de su respuesta y del esfuerzo de control, y restricciones operacionales, que incluyen por ejemplo los rangos admisibles de operación de los componentes del sistema. Los índices de funcionamento son muy útiles pues permiten privilegiar distintos aspectos referentes a la dinámica del sistema y además, el uso de restricciones permite tener una descripción mucho más realista del proceso. Para encontrar las variables manipuladas con un controlador predictivo, se debe resolver un problema de optimización que consiste en la minimización del índice de funcionamiento sujeto a las restricciones del proceso. Para sistemas con un cierto grado de complejidad, la resolución del problema de optimización puede ser difícil debido a su alto grado de no linealidad, alta dimensionalidad o a la presencia de variables enteras y continuas (sistemas híbridos). Como una alternativa a los metodos clásicos, para aquellos casos en los que con estos métodos no se puede manejar satisfactoriamente estas dificultades, en este trabajo se propone utilizar algoritmos evolutivos para resolver los problemas de optimización entera mixta que aparecen en el control predictivo de sistemas híbridos no lineales. Basado en las cualidades de los algoritmos evolutivos, en este trabajo se propone métodos genéricos para el diseño y evaluación de estrategias de control predictivo para sistemas híbridos no lineales basado en algoritmos evolutivos para la optimización de la función objetivo en tiempo real. Las estrategias propuestas se evalúan en tres sistemas híbridos: un reactor batch con entradas discretas; el mismo reactor batch, pero con entradas discretas y continuas (mixtas); y un sistema de ruteo dinámico de vehículos. Los estudios presentados justifican la aplicación de algoritmos evolutivos en términos de tiempo computacional y calidad de las soluciones en la mayoría de los casos, consiguiendo obtener controladores más eficientes que otros ya existentes para estos procesos. Además, se estudia diversos aspectos relevantes para la aplicación de algoritmos evolutivos en la resolución del problema de optimización en control predictivo híbrido no lineal, tales como representación de soluciones, manejo de restricciones, y selección de los parámetros del algoritmo. Para este último aspecto, se utiliza un análisis multi-objetivo que permite escoger el tamaño óptimo de población y el número óptimo de iteraciones de acuerdo a criterios basados en el tiempo computacional y la calidad de las soluciones. Este análisis se puede interpretar además como un indicador gráfico de la bondad de una estrategia basada en algoritmos evolutivos, y por lo tanto se utiliza para comparar distintas estrategias dentro de los aspectos mencionados. Finalmente, se propone algunas líneas de investigación para la aplicación de algoritmos evolutivos en control predictivo híbrido no lineal. Dentro de estos temas se encuentran aspectos que debido a la amplitud del tema no han sido analizados, tales como argumentos de estabilidad o el uso de mezclas entre métodos convencionales y algoritmos evolutivos, y otros que si bien han sido analizados, aún pueden ser mejorados, tales como el análisis multi-objetivo para la sintonía de parámetros y comparación de estrategias.

Electric propulsion is currently seen as a key enabling technology for space debris removal missions aimed at deorbiting multiple debris targets. This paper develops an autonomous onboard orbit control strategy tailored to these missions. The control problem is divided into four stages, involving a sequence of low-thrust orbital transfer and rendezvous maneuvers. A feedback control law is derived for each maneuvering stage, by exploiting Lyapunov-based and model predictive control techniques. The proposed design is able to account for mission-specific performance and safety requirements, while satisfying on-off constraints inherent to the propulsion technology. Simulation case studies of a multidebris removal mission demonstrate the effectiveness of the proposed control strategy, and support the viability of electric propulsion for such type of missions.

— Implementing linear model predictive controllers in embedded systems with limited computational resources is still challenging. Recently, several code generation tools have been developed that produce highly efficient library-free optimization algorithms. We present a tool that focuses on controller performance and hardware with low computational resources. The underlying optimization algorithm has been explicitly developed for real-time embedded applications, and is based on an augmented Lagrangian method together with Nesterov's gradient method. The tool provides offline methods that allow the generation of online controllers that have low computational requirements and quickly reach good performance. We demonstrate the capabilities of the software, and the performance of the generated controllers with two examples.

- by P. Zometa
- •
- Embedded Systems, Model Predictive Control (MPC)

This paper proposes a multivariable model predictive control scheme for discharge pressure regulation in centrifugal compressors. The main novelty of the proposed approach is that three control inputs are considered: the rotational speed of the compressor, an anti-surge valve for gas recycle and the inlet guide vane, whose variations allow one to significantly enlarge the operating region of the compressor and hence to enhance the authority of the control system. Surge prevention is achieved by including in the model an output variable accounting for the distance of the operating point from the surge limit. Such distance is defined on a compressor performance map which is invariant to changes in the inlet conditions, and thus its computation requires only standard pressure measurements available from the plant. Numerical simulations show that the proposed control system is able to meet the desired specifications, in the presence of different types of disturbances occurring along the pipeline.

- by Toufik Bentaleb
- •
- Process Control, Model Predictive Control (MPC), GAS TURBINE, Oil and gas

Solar energy is so far the most promising and sustainable alternative energy source to fossil fuels. The new solar technology proposed in this research, building-integrated
photovoltaic-thermal (BIPV/T) systems, can be attached to the façade or replace conventional cladding, enabling on-site generation of solar electricity and heat, which can fulfill a significant portion of the building energy requirements. The overall objective of this research is to develop (a) prototype BIPV/T systems coupled with open-loop corrugated Unglazed Transpired Solar Collectors (UTC); (b) new modeling
representations for design, analysis, and control of BIPV/T integrated in the operation of building Heating, Ventilation and Air Conditioning (HVAC) systems; (c) an innovative
energy management framework, over a future planning horizon, based on model-predictive control algorithms that can anticipate the variability of solar irradiance and building load, thus enabling the optimal operation of high performance buildings with distributed solar energy resources and active thermal energy storage.
To this end, high-resolution, three-dimensional Computational Fluid Dynamics (CFD) models are developed to investigate the complex airflow and heat transfer mechanisms in BIPV/T systems and provide a solid foundation that supports the
formulation of thermal analysis models. The CFD models are validated using data from an experimental set-up in a state-of-the-art solar simulator facility, in terms of the cavity
exit air temperature (the error less than 1°C), the stream-wise development of plate surface temperature (the error less than 1°C), and vertical profiles of stream-wise velocity (average error within 10 %) and turbulent kinetic energy (average error within 20 %).
Energy prediction models for both corrugated UTCs and UTCs integrated with BIPV/T systems are established to evaluate their performance (electrical and thermal energy output, outlet air temperature, etc.) for different weather (incident solar radiation
and wind speed) and system design parameters (corrugation geometry, PV module coverage ratio, suction velocity, etc.). Comprehensive Nusselt number and effectiveness correlations, representing both the exterior and interior convective heat transfer processes in BIPV/T systems, are obtained from the CFD simulations and subsequently used in the energy models. Experimental data for prototype BIPV/T collectors installed at Purdue’s Architectural Engineering Lab are used to validate the energy models. Comparison between the model predictions and the experimental data verifies the dynamic response
of the collectors to weather and operating conditions, with the root mean square error within 1 °C in terms of cavity exit air temperature for the UTC configuration and within 2 °C (PV surface temperature) for the model of UTC with PV modules. The methodology for the analysis of the thermal boundary layer development and convective heat transfer process can be generalized to uniform approaching flow over corrugated plates with discrete suction, while the Nusselt number and effectiveness correlations and the physical modeling approach can be adopted to other BIPV/T systems.
Then the energy models are implemented in building simulation platforms to enable integration of BIPV/T with building HVAC systems (air handling unit and radiant floor heating) and active thermal storage systems. Finally, a deterministic model-predictive control algorithm is formulated for the integrated solar system. This includes building up a detailed dynamic system model in TRNSYS, presenting a system
identification approach to obtain simplified gray and black-box models that capture the relevant system dynamics and are computationally efficient for implementation in real controllers, formulating the cost function and setting up the constraints and the optimization environment, and examining the potential impacts associated with the prediction accuracy of the solar irradiance, which is the most significant disturbance acting on the system. The energy saving potential of the integrated system and the predictive controller is investigated in comparison with baseline operation strategies used in commercial buildings, using the Hydronic Laboratory at Purdue’s Living Laboratories as a simulation test-bed. The investigation shows that efficient integration concepts and
optimal control strategies are necessary to predict and plan the energy cost for the integrated solar system, resulting in total energy savings for the integrated solar system that can be up to 45 %. The modeling representations and approaches developed in this study can be generalized and extended to other commercial buildings with different integrated solar systems, HVAC systems and energy storage.
In summary, the solar technology (prototype BIPV/T collectors), systems representation, validated models, and the numerical prototypes of predictive-control algorithms developed in this dissertation found to be an efficient approach for buildingscale
renewable energy generation and utilization in high performance commercial buildings. The research presented herein is a necessary precursor for future investigation and expansion of smart buildings or net-zero energy buildings and the adoption of innovative energy management concepts in engineering practice. With large-scale deployment, this could be an effective pathway to reduce greenhouse gas emissions and
the need to build new fossil fuel power plants.

- by Xiaoran Feng
- •
- Model Predictive Control (MPC)

This thesis deals with modeling, Real-Time simulation and model-based control strategies for flexible-link mechanisms. The dynamic model under consideration is based on Finite Element Method (FEM) and Equivalent Rigid Link System (ERLS). Such model is used for the simulation and for the design of closed-loop controls systems for a single-link mechanism, a four-bar linkage and a five-link mechanism. The resulting nonlinear model is used to perform both off-line and Hardware-In-the-Loop (HIL) simulations.
A whole chapter of this work is devoted to the development and experimental validation of a HIL testbed of a single-link planar mechanism with high flexibility and affected by gravity. The experimental validation of the proposed
HIL simulator is performed by comparing the response of the real and of the simulated system using the same real-time controller. The comparison shows a good agreement of results.
Moreover two MPC (Model Predictive Control) strategies are investigated as
an effective strategy to control both the position and the vibration in flexiblelink
manipulators. The investigation involves the single-link mechanism, the
4-bar linkage and the five-link manipulator. For the latter, also the ability to
track a prescribed trajectory for the end-effector in investigated. Again, for
the five-link mechanism, the effect of the choice of different primitives for the
trajectory planning algorithm is evaluated trough simulations, as well as the
optimal choice for control’s tuning parameters. Robustness of the proposed
control system is investigated trough several numerical experiments.
In the last part of this work experimental results are proposed to show the superior capabilities of MPC to perform high-speed movements with limited
vibration trough the comparison with traditional control strategies.

This paper proposes a modification in the
maximum power point tracking (MPPT) by using model
predictive control (MPC). The modification scheme of the
MPPT control is based on the perturb and observe algorithm
(P&O). This modified control is implemented on the dc-dc
multilevel boost converter (MLBC) to increase the response of
the controller to extract the maximum power from the
photovoltaic (PV) module and to boost a small dc voltage of it.
The total system consisting of a PV model, a MLBC and the
modified MPPT has been analyzed and then simulated with
changing the solar radiation and the temperature. The
proposed control scheme is implemented under program
MA TLAB/SIMULINK and the obtained results are validated
with real time simulation using dSPACE 1103 ControlDesk.
The real time simulation results have been provided for
principle validation.

In this study, the hardware and software design and implementation of an autonomous electric vehicle are addressed. We aimed to develop an autonomous electric vehicle for path tracking. Control and navigation algorithms are developed and implemented. The vehicle is able to perform
path-tracking maneuvers under environments in which the positioning signals from the Global Navigation Satellite System (GNSS) are not accessible. The proposed control approach uses a modified
constrained input-output nonlinear model predictive controller (NMPC) for path-tracking control. The proposed localization algorithm used in this study guarantees almost accurate position estimation under GNSS-denied environments. We discuss the procedure for designing the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system’s full state is estimated by fusing visual inertial odometry (VIO) measurements with
wheel odometry data using an extended Kalman filter (EKF). Simulation and real-time experiments are performed. The obtained results demonstrate that our designed autonomous vehicle is capable of
performing path-tracking maneuvers without using Global Navigation Satellite System positioning data. The designed vehicle can perform challenging path tracking maneuvers with a speed of up to
1 m per second.

This paper reviews occupancy based model predictive control (MPC) for building indoor climate control. Occupancy behavior in buildings is stochastic and complex in nature. With better understanding of occupancy presence in rooms and spaces, advanced controls, such as MPC, can be designed to achieve a more energy efficient operation, compared to more traditional control methods, while comfort is maintained. This paper starts with an overview of traditional controls implemented in buildings, and importance of occupancy based controls. Various control-oriented building modeling methods including physics-based and data-driven models are reviewed. Later on, a comprehensive review of MPC in terms of control theory, objective functions, constrains, optimization methods, system characteristics and various types of MPC is presented conducted. In principle, MPC finds an optimal sequence of control commands to optimize an objective function, considering system model, disturbances, predictions and actuation constraints. Lastly, occupancy based controls including commonly used rule-based and latest model-based controls are reviewed. In addition, a few experimental studies are presented and discussed. The paper presents a holistic overview of occupancy-based MPC for building heating, ventilation, and air conditioning (HVAC) systems, and discusses current status and future challenges. The purpose of this paper is to provide a guideline forresearchers who would like to conduct similar studies to have a better understanding of established research methods.

- by Amin Mirakhorli and +1
- •
- Sustainable Building Design, Building Energy Simulation, Model Predictive Control (MPC), Smart Grid

The wind turbine (WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms. This paper focuses on wind turbine control design strategies. The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.

Renewable energy sector is undergoing rapid expansion as the global focus is shifting towards cleaner, reliable and sustainable resources. As the new installation of these resources are well underway, there is tremendous potential for exploring these to more advanced control algorithms. Model predictive control is gaining immense popularity because of its flexible controllability, its ability to be used in any of application irrespective of its field as well as the availability of fast processors. This paper presents a systematic review on Photo-voltaic (PV) and wind energy systems controlled by Model predictive control approach. The work presented here will help the researchers to further explore the flexibility of this controller for design, analysis and implementation in renewable energy systems.

Sistem kontrol adaptif adalah sistem kontrol dimana parameter- parameternya dapat diatur dan juga memiliki mekanisme untuk mengatur parameter-parameter tersebut.Model Reference Adaptive Control (MRAC)merupakan salah satu skema kendali... more

Sistem kontrol adaptif adalah sistem kontrol dimana parameter- parameternya dapat diatur dan juga
memiliki mekanisme untuk mengatur parameter-parameter tersebut.Model Reference Adaptive
Control (MRAC)merupakan salah satu skema kendali adaptif dimana performansi keluaran
sistem(proses) mengikuti performansi keluaran model referensinya.

This paper presents a method that overcomes the problem of the confusion during fast irradiance change in the classical MPPTs as well as in model predictive control-MPPTs available in the literature. The previously introduced MPC-MPPTs take into account the model of the converter only, which make them drift during fast environmental conditions. Therefore, the model of the PV array is also considered in the proposed algorithm, which allows it prompt during rapid environmental condition changes. It takes into account multiple previous samples of power, and based on that is able to take the correct tracking decision when the predicted and measured power differ. After the decision is taken, it will be sent to a second part of the algorithm as a reference. The second part is used for following the reference provided by the first part, where the pulses are sent directly to the converter, without a modulator or a linear controller. The proposed technique is validated experimentally by using a buck converter, fed by a PV simulator. The tracking efficiency is evaluated according to EN50530 standard in static and dynamic conditions. The experimental results show that the proposed MPC-MPPT is a quick and accurate tracker even under very fast changing irradiance.

- by Abderezak Lashab and +1
- •
- Model Predictive Control (MPC), Drift, Maximum Power Point Tracking for Solar, Buck Converter

This thesis presents the application of Model Predictive Controller (MPC) on
Automatic Transmission (AT) system clutch to clutch transients. This research describes
the advance predictive controller applied to optimize the clutch to clutch shift transient
during torque phase and speed phase in order to control the shifting dynamics for a better
shift quality and engagement performance.
In this research, a high order dynamic model of transmission system which includes the
hydraulic dynamics of actuator has been analyzed in order to design multiple Model
Predictive Controller (MPC).
Simulation results obtained considering upshift maneuvers with engine torque
management applied and shows the effectiveness of the proposed control strategy. The
analytical solution presented permits to tune the torque phase and speed phase of shifting
transient in an optimized way that is easier than previous strategy based on look-up tables
and PID controller.

This paper examines the design concept and mobile control strategy of the human assistant robot I-PENTAR (inverted pendulum type assistant robot). The motion equation is derived considering the non-holonomic constraint of the twowheeled mobile robot. Different optimal control approaches are applied to a linearized model of I-PENTAR. These include linear quadratic regulator (LQR), linear quadratic Gaussian control (LQG), H2 control and H∞ control. Simulation is performed for all the approaches yielding good performance results.

Model predictive control (MPC) is an advanced control that can be used for dynamic optimization of HVAC equipment. Although the benefits of this technology have been shown in numerous research papers, currently there is no commercially or publicly available software that allows the analysis of building systems that employ MPC. The lack of detailed and robust tools is preventing more accurate analysis of this technology and the identification of factors that influence its energy saving potential. The modeling environment (ME) presented here is a simulation tool for buildings that employ MPC. It enables a systematic study of primary factors influencing dynamic controls and the savings potential for a given building. The ME is highly modular to enable easy future expansion, and sufficiently fast and robust for implementation in a real building. It uses two commercially available computer programs, with no need for source code modifications or complex connections between programs. A simplified building model is used during the optimization, whereas a more complex building model is used after the optimization. It is shown that a simplified building model can adequately replace a more complex model, resulting in significantly shorter computational times for optimization than those found in the literature.

- by Tea Zakula and +2
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- Model Predictive Control (MPC), Energy efficiency

The subject of this paper is a comparison of two control strategies of an inverted pendulum on a cart. The first one is a linear-quadratic regulator (LQR), while the second is a state space model pre-dictive controller (SSMPC). The study was performed on the simulation model of an inverted pendulum, determined on the basis of the actual physical parameters collected from the laboratory stand AMIRA LIP100. It has been shown that the LQR algorithm works better for fixed-value control and disturbance rejection, while the SSMPC controller is more suitable for the trajectory tracking task. Furthermore, the system with SSMPC controller has smoother changes in the control signal, that can be beneficial for an actuator, while LQR controller may generate adverse, rapid changes in the control signal.

— Community-based microgrid (C-µGrid) systems are gaining increasing importance nowadays because of the lack of µGrid public investment and management policies. Technoeconomic analysis shows that C-µGrid based on a cluster of microgenerators could be an effective solution when individual systems are not feasible. In this paper, the controlling capability of the central controller of the C-µGrid is improved through an economic model predictive control (EMPC) approach operating at the pricing level that can fulfill the goal of the operational control of the cluster. With a central controller, it is capable of satisfying the demand at prosumer (active energy producer and simultaneous consumer) sides and, at the same time, optimizing the various µGrid contrasting constraints. Emphasis here has been given to the operational constraints related to the battery lifetime, so that the maintenance and replacement costs would be reduced. A comparative analysis has been carried out between the performance of two systems, one based on an IF-THEN-ELSE heuristic supervision logic (S-LOGIC) and the other based on the proposed EMPC strategy. The analysis has been undertaken in a location in Dublin, Ireland, on the basis of available measured data. Simulation shows the effectiveness of implementing the EMPC approach to optimally manage the system.

- by Francesco Tedesco and +2
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- Energy Economics, Renewable Energy, Model Predictive Control (MPC), Renewable energy resources

The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

- by Róbert Marc
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- Robotics, Humanoid Robotics, Model Predictive Control (MPC)

El Control Predictivo de Modelo tiene la característica principal de adelantarse a la aparición de perturbaciones, por lo que es de especial interés en el seguimiento de trayectorias de vehículos aéreos. Actualmente y gracias a que las computadoras a bordo de los drones han aumentado su capacidad de computo es posible implementar controladores de bajo y alto nivel.

- by Eduardo Salazar Hidalgo
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- Model Predictive Control (MPC), Drones, Odometria

Design of an improved Permanent Magnet Synchronous Generator (PMSG) wind turbine power based Current Fed Inverter (CFI) using Model Predictive Controller (MPC) is proposed in this paper. Optimum torque control is proposed in wind energy conversion system, MPC is used to adjust the dynamic response time based on the application need. This model deals with torque control strategy of PMSG in the machine side controller. Impact of normal mode of operation by the copper losses and torque ripples are minimized by maximizing the average torque. Synthetization of adequate stator phase current are obtained naturally. Uncertainty of the steady state errors of the plant and parameter error are rectified in the system model. The designed CFI with MPC was implemented using medium range wind turbine in MATLAB /Simulink. The simulation output shows the better efficiency in over modulation region by the proposed CFI with controller. Constant switching frequency is maintained and efficient required dynamic response (DR) value is attained.

- by FOREX Publication and +1
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- Model Predictive Control (MPC), Wind Turbines, Permanent Magnet Synchronous Generator, Over modulation

The objective of this paper is to provide safe landing of an UAV on any demanding conditions and can be very useful during the period of floods, rescuing the affected people. It can be also be useful in military operations. This system provides stable method for landing of an aircraft. Image processing technique is used to capture the image and find the angle of inclinations. MATLAB produces four types of image through Image processing technique. They are Gray image, Edge Detection image, Histogram image, object detection image. MATLAB sends the histogram signal to microcontroller using UART. It is used to convert the received parallel data into serial data for transmitting the data to longer distance. Relay drives the DC motor. Angle of inclinations is measured according to the detected image of the surface. MATLAB produces the total inclination value of the surface. DC motor automatically adjusts the landing gear according to the obtained input inclination value of the surface. An UAV is programmed to adjust the landing gear according to angle of inclination. When the landing surface is detected for landing the aircraft, it automatically adjusts the landing gear and lands the aircraft according to the inclined surface of the landing area. I. INTRODUCTION Presently a-days, late progressions in handling capacities have empowered the likelihood of utilizing new computationally immoderate control systems like the Model Predictive Control (MPC) on continuous applications. On this paper, a structure is proposed for the configuration of a managing MPC to be actualized more than an easier, more solid inside circle PID Autopilot without making any changes to its structure. The proposed framework had the capacity add new capacities to the inner circle by making little remedies to its control requests, guaranteeing the agreeability with limitations. UAVs are basically air ship without a human pilot inside. The heaviness of these vehicles can run from a couple of grams to 12 tons having 35 meters of wingspan. We depict the utilization of model predictive control (MPC) utilizing state mapping to the programmed arriving arrangement of a space plane. The controller must be intended to empower the vehicle to take after reference signals as for direction and speed, which are communicated as capacities of the downrange. To track the reference flags, a control data is by and large outlined so one or more perfect transient reactions are figured it out. We additionally have added to a flight control framework in light of this idea. Nonetheless, it was not tasteful for utilization as the controller of a genuine vehicle because of the absence of thought of the data and state requirements. A controller that considers these requirements can be composed by applying MPC. Then again, it is hard to predefine the transient reactions important to track the reference signals, which shift with the current downrange. In this paper, another methodology utilizing state mapping and criticism linearization is proposed to enhance the control execution of following the reference signals. State mapping is inferred to portray a framework in which the reference signs can be taken care of as a steady set point, and the portrayed framework is changed utilizing state input. An unmanned elevated vehicle (UAV) is a flying machine furnished with an on-load up flight controller, information preparing units, sensors, and correspondence framework, which make it fit for performing self-sufficient flight missions in view of a prearranged flight information or driven remotely from a ground control station. This class of vehicles offers more prominent profits over kept an eye on airplane when utilized for high-danger errands or in missions obliging high secrecy. Quick advancement saw in the innovative work of UAVs can be credited to various components, for example, mechanical progressions, political and financial elements among others. UAVs can be characterized into settled-wing and rotorcraft.

- by Stefanos Manias and +1
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- Optimal Control, Power Electronics, Model Predictive Control of Hybrid Systems, Model Predictive Control