In applications such as multiphase motor drives, classical predictive control strategies are characterized by a variable switching frequency which adds high harmonic content and ripple in the stator currents. This paper proposes a model predictive current control adding a modulation stage based on a switching pattern with the aim of generating a fixed switching frequency. Hence, the proposed controller takes into account the prediction of the two adjacent active vectors and null vector in the ( α - β ) frame defined by space vector modulation in order to reduce the (x-y) currents according to a defined cost function at each sampling period. Both simulation and experimental tests for a six-phase induction motor drive are provided and compared to the classical predictive control to validate the feasibility of the proposed control strategy.

The urgent need to reduce greenhouse gas emissions to achieve a decarbonized society has led to the active introduction of electric vehicles worldwide. Renewable energy sources that do not emit greenhouse gases during charging must also be used. However, the uncertainty in the supply of renewable energy is an issue that needs to be considered in practical applications. Therefore, in this study, we predicted the amount of electricity generated by renewable energy using model predictive control, and we considered the operation of a complete island-operated park and ride EV parking station that does not depend on commercial electricity. To perform appropriate model predictive control, we performed comparative simulations for several different forecast interval cases. Based on the obtained results, we determined the forecast horizon and we simulated the economic impact of implementing EV demand response on the electricity demand side. We found that without demand response, large amounts of... [more]

As a bi-directional converter, the Buck-Boost converter, which has the advantages of simple structure and taking the SOC of the battery as the balance variable, is adopted as the balance topology in this paper. In view of the shortcomings of traditional balance topology, which can only balance two adjacent batteries, resulting in a long balance time and insufficient balance accuracy, a cascade active balance charging topology that can balance in intra-group and inter-group situations simultaneously is proposed. At the same time, the fuzzy control algorithm and model predictive control are used as the balance control strategies, respectively, to control whether the MOSFET is on or off in the balance topology circuit. The duty cycle is dynamically adjusted to the size of the balance current to achieve the balance of the battery pack. The results show that the cascade Buck-Boost balance topology based on model prediction control can accurately control the balancing current and improve the... [more]

This work considers the DC part of a hybrid AC/DC microgrid with a meshed topology. We address cost minimization, battery scheduling and the power loss minimization within the power distribution network through constrained optimization. The novelty comes from applying differential flatness properties to the microgrid components and formulating the cost and constraints in terms of the associated B-splines parametrization of the flat outputs (the voltages and currents of the system). This allows us to obtain optimal power profiles to minimize the power dissipation and the cost of the electricity purchase from the external grid. These profiles are tracked by a model predictive controller at the higher level, while at a a lower level a controller deals with the operation of the switches within the DC/DC converters. Extensive simulations under nominal and fault-affected scenarios using realistic data validate the proposed approach.

This paper introduces a novel metaheuristic approach of sooty terns optimization algorithm (STOA) to determine the optimum parameters of model predictive control (MPC)-based deregulated load frequency control (LFC). The system structure consists of three interconnected plants with nonlinear multisources comprising wind turbine, photovoltaic model with maximum power point tracker, and superconducting magnetic energy storage under deregulated environment. The proposed objective function is the integral time absolute error (ITAE) of the deviations in frequencies and powers in tie-lines. The analysis aims at determining the optimum parameters of MPC via STOA such that ITAE is minimized. Moreover, the proposed STOA-MPC is examined under variation of the system parameters and random load disturbance. The time responses and performance specifications of the proposed STOA-MPC are compared to those obtained with MPC optimized via differential evolution, intelligent water drops algorithm, stain... [more]

Efficient management of energy resources is crucial in smart buildings. In this work, model predictive control (MPC) is used to minimize the economic costs of prosumers equipped with production units, energy storage systems, and electric vehicles. To this purpose, the predictive control manages the available energy resources by exploiting future information about energy prices, absorption and production power profiles, and electric vehicle (EV) usage, such as times of departure and arrival and predicted energy consumption. The predictive control is compared with a rule-based technique, herein referred to as a heuristic approach, that acts in an instant-by-instant fashion without considering any future information. The reported results show that the studied predictive approach allows one to achieve charging profiles that adapt to variable operating conditions, aiming at optimal performances in terms of economic cost minimization in time-varying price scenarios, reduction of rms current... [more]

Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass transfer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the las... [more]

In this paper, a model predictive controller based on a generator model for prediction purposes is proposed to replace a standard generator controller with a stabilizer of a power system. Such a local controller utilizes an input-output model of the system taking into consideration not only a generator voltage Ug but also an additional, auxiliary signal (e.g., α, Pg, or ωg). This additional piece of information allows for taking oscillations into account that occur in the system and minimizing their impact on the overall system performance. Parameters of models used by the controller are obtained on the basis of the introduced black-box models both for a turbine and a synchronous generator, parameters of which are estimated in an on line fashion using a RLS method. The aim of this paper is to compare the behavior of the classical generator control system with a power system stabilizer and a model predictive control with an additional feedback signal. The novelty of the paper is related... [more]

This paper proposes a multi-virtual-vector model predictive control (MPC) for a dual three-phase permanent magnet synchronous machine (DTP-PMSM), which aims to regulate the currents in both fundamental and harmonic subspace. Apart from the fundamental α-β subspace, the harmonic subspace termed x-y is decoupled in multiphase PMSM according to vector space decomposition (VSD). Hence, the regulation of x-y currents is of paramount importance to improve control performance. In order to take into account both fundamental and harmonic subspaces, this paper presents a multi-virtual-vector model predictive control (MVV-MPC) scheme to significantly improve the steady performance without affecting the dynamic response. In this way, virtual vectors are pre-synthesized to eliminate the components in the x-y subspace and then a vector with adjustable phase and amplitude is composed of two effective virtual vectors and a zero vector. As a result, an enhanced current tracking ability is acquired due... [more]

In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good... [more]

Multiphase electrical machines are advantageous for many industrial applications that require a high power rating, smooth torque, power/torque sharing capability, and fault-tolerant capability, compared with conventional single three-phase electrical machines. Consequently, a significant number of studies of multiphase machines has been published in recent years. This paper presents an overview of the recent advances in multiphase permanent magnet synchronous machines (PMSMs) and drive control techniques, with a focus on dual-three-phase PMSMs. It includes an extensive overview of the machine topologies, as well as their modelling methods, pulse-width-modulation techniques, field-oriented control, direct torque control, model predictive control, sensorless control, and fault-tolerant control, together with the newest control strategies for suppressing current harmonics and torque ripples, as well as carrier phase shift techniques, all with worked examples.

This paper aims to provide the smart grid research community with an open and accessible general mathematical framework to develop and implement optimal flexibility mechanisms in large-scale network applications. The motivation of this paper is twofold. On the one hand, flexibility mechanisms are currently a hot topic of research, which is aimed to mitigate variation and uncertainty of electricity demand and supply in decentralised grids with a high aggregated share of renewables. On the other hand, a large part of such related research is performed by heuristic methods, which are generally inefficient (such methods do not guarantee optimality) and difficult to extrapolate for different use cases. Alternatively, this paper presents an MPC-based (model predictive control) framework explicitly including a generic flexibility mechanism, which is easy to particularise to specific strategies such as demand response, flexible production and energy efficiency services. The proposed framework... [more]

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and... [more]

It is considered necessary to implement advanced controllers such as model predictive control (MPC) to utilize the technical flexibility of a building polygeneration system to support the rapidly expanding renewable electricity grid. These can handle multiple inputs and outputs, uncertainties in forecast data, and plant constraints, amongst other features. One of the main issues identified in the literature regarding deploying these controllers is the lack of experimental demonstrations using standard components and communication protocols. In this original work, the economic-MPC-based optimal scheduling of a real-world heat pump-based building energy plant is demonstrated, and its performance is evaluated against two conventional controllers. The demonstration includes the steps to integrate an optimization-based supervisory controller into a typical building automation and control system with off-the-shelf HVAC components and usage of state-of-art algorithms to solve a mixed integer... [more]

Offshore produced water treatment (PWT) accounts for cleaning the largest waste stream in the offshore oil and gas industry. If this separation process is not properly executed, large amounts of oil are often directly discharged into the ocean. This work extends two grey-box models of a three-phase gravity separator and a deoiling hydrocyclone, and combines them into a single plant-wide model for testing PWT control solutions in a typical process configuration. In simulations, three known control solutions—proportional-integral-derivative (PID) control, H∞ control, and model predictive control (MPC)—are compared on the combined model to evaluate the separation performance. The results of the simulations clearly show what performance metrics each controller excels at, such as valve wear, oil discharge, oil-in-water (OiW) concentration variance, and constraint violations. The work incentivizes future control to be based on operational policy, such as defining boundary constraints and wei... [more]

In this paper, an improved finite control set model predictive current control (FCS-MPCC) is proposed for a two-phase hybrid stepper motor fed by a three-phase voltage source inverter (VSI). The conventional FCS-MPCC selects an optimal voltage vector (VV) from six active and one null VVs by evaluating a simple cost function and then applies the optimal VV directly to the VSI. Though the implementation is simple, it features a large current ripple and total harmonic distortion (THD). The proposed improved FCS-MPCC builds an extended control set consisting of 37 VVs to replace the original control set with only seven VVs. The increase in the amount of VVs helps to regulate the current more accurately. In each control period, the improved FCS-MPCC takes advantage of deadbeat control to calculate a reference VV, and only the three VVs adjacent to the reference VV are predicted and evaluated, which decrease the computational workload significantly. Build waveform patterns for all VVs in the... [more]

In this paper, we investigate the secondary control problems of AC microgrids with physical states (i.e., voltage, frequency and power, etc.) constrained in the process of actual control, namely, under the condition of state constraint. On the basis of the primary control (i.e., droop control), the control signals generated by distributed secondary control algorithm are used to solve the problems of voltage and frequency recovery and power allocation for each distributed generators (DGs). Therefore, the model predictive control (MPC) with the mechanism of rolling optimization is adopted in the second control layer to achieve the above control objectives and solve the physical state constraint problem at the same time. Meanwhile, in order to reduce the communication cost, we designed the self-triggered control based on the prediction mechanism of MPC. In addition, the proposed algorithm of self-triggered MPC does not need sampling and detection at any time, thus avoiding the design of o... [more]

This paper presents a model predictive control (MPC)-based algorithm for rollover prevention of an autonomous electric road sweeper (AERS). For AERS, the basic function of autonomous driving is a path- and velocity-tracking control needed to make a vehicle follow given path and velocity profiles. On the other, the AERS adopts an articulated frame steering (AFS) mechanism which can make cornering behavior agile. Moreover, the tread of the AERS is narrow, and the height of the mass center is high. As a result, it is prone to roll over. For this reason, it is necessary to design a controller for path and velocity tracking and rollover prevention in order to improve maneuverability and roll safety of the AERS. A kinematic model was adopted as a vehicle one for the AERS. With the vehicle model, reference states of position and velocity were determined that are needed to make the AERS track the reference path and prevent rollover. With the vehicle model and reference states, an MPC-based mot... [more]

Optimization of economic aspects of microgrid operation in both grid-connected and islanded mode leads to contradictive definitions of optimality for both modes. There is no general agreement on how to cope with this duality. To address this issue, as well as modern energy market requirements and a better renewable energy utilization necessity in the case of large facilities, a comprehensive control solution utilizing the appropriate model is needed. In response, the authors propose a hybrid microgrid model covering fundamental features and designed to work in conjunction with two switched receding horizon control laws. A relevant controller is chosen according to the current microgrid operation mode and its cost function tailored to specific demands of the islanded or grid-connected operation. Performed research led to a new switched hybrid model predictive control approach focused on microgrid economic optimization. This approach utilizes an appropriate hybrid microgrid model also co... [more]

Regarding the microgrid with large-scale electric vehicle (EV) energy storage systems working at the vehicle-to-grid (V2G) mode, uncertain factors (e.g., the number of EVs feeding the microgrid shifts frequently) make the system unfixed, leading to the fact that it is difficult to precisely determine the real-time droop coefficients of the system, thereby degrading the performance of the traditional inverter control strategies that rely on the droop coefficients. To solve the problem, this paper proposes an errorless-control-targeted double control loop (DCL) technique based on robust MPC to control the microgrid with EV energy storage systems without using droop coefficients. Firstly, the structure of the DCL method is developed, with each component in the structure detailed. Compared to the traditional control strategies, the novel one regards the frequency, voltage, and currents as the control objectives instead of active/inactive power. It deserves to be mentioned that the frequenc... [more]

Increasing demand for faster and more agile Unmanned Aerial Vehicles (UAVs, drones) is observed in many scenarios, including but not limited to medical supply or Search-and-Rescue (SAR) missions. Exceptional maneuverability is critical for robust obstacle avoidance during autonomous flights. A novel modification to the Model Predictive Controller (MPC) is proposed, which drastically improves the speed of the attitude controller of our quadcopter drone. The modified MPC is suitable for the onboard microcontroller and the 400 Hz main control loop. The peak and total energy consumption and the performance of the attitude controllers are assessed: the modified MPC and the default Proportional-Integral-Derivative (PID). The tests were conducted in a custom-implemented Flight Mode in the ArduCopter software stack, securing the drone in a test harness, which guarantees the experiments are repetitive. The ultimate MPC greatly increases maneuverability of the drone and may inspire more research... [more]

The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed, the most recent works are focusing on optimizing hydrogen consumption, without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMS is normalized in order to address its multi-objective optimization. Then, weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques, this proposed approach does not require any information about the speed profile, which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces th... [more]

This article presents the results of the development of a hybrid research platform for fundamental and empirical modeling and analysis of energy management of shared electric vehicles. The article describes the hybrid model and its specific features in detail. Within the model architecture, a part of the fundamental model, empirical model and data collection tools were interconnected. The uniqueness lies in the models of electric cars created for a specific vehicle using cost-optimal parameterizations, as well as the implementation of a cloud solution, which is based on custom data communication, custom data logger and cost-optimized parameterization of machine learning algorithms. Experimental verification was performed on a real electric car in public traffic. The car is part of casharing platform.

This work aims to develop a novel system, including software and hardware, to perform independent control tasks in a genuine parallel manner. Currently, to control processes with various sampling periods, distributed control systems are most commonly utilized. The main goal of this system is to propose an alternative solution, which allows simultaneous control of both fast and slow processes. The presented approach utilizes FPGA (Field Programmable Gate Array) with Nios II processor (Intel Soft Processor Series) to implement and maintain instances of independent controllers. Instances can implement FDMC (Fast Dynamic Matrix Control) and PID (Proportional-Integral-Derivative) control algorithms with various sampling times. The FPGA-based design allows for true independence of controllers’ execution both from one another and the managing processor. Also, pure parallel execution allows for implementing slow and fast controllers in the same device. The complete flexible system with a matri... [more]

Heat pumps coupled with thermal energy storage (TES) systems are seen as a promising technology for load management that can be used to shift peak loads to off-peak hours. Most of the existing model predictive control (MPC) studies on tariff-based load shifting deploying hot water tanks use simplified tank models. In this study, an MPC framework that accounts for transient thermal behavior (i.e., mixing and stratification) by applying energy (EMPC) and exergy (XMPC) analysis is proposed. A case study for an office building equipped with an air handling unit (AHU) revealed that the MPC strategy had a high load-shifting capacity: over 80% of the energy consumption took place during off-peak hours when there was an electricity surplus in the grid. An analysis of a typical day showed that the XMPC method was able to provide more appropriate stratification within the TES for all load characteristics. An annual exergy analysis demonstrated that, during cold months, energy degradation in the... [more]