Model Predictive Control Using Thermal Inputs for Crystal Growth Dynamics
Recently, crystal growth technologies have made
progress by the requirement for the high quality of crystal materials.
To control the crystal growth dynamics actively by external forces
is useuful for reducing composition non-uniformity. In this study,
a control method based on model predictive control using thermal
inputs is proposed for crystal growth dynamics of semiconductor
materials. The control system of crystal growth dynamics considered
here is governed by the continuity, momentum, energy, and mass
transport equations. To establish the control method for such thermal
fluid systems, we adopt model predictive control known as a kind
of optimal feedback control in which the control performance over
a finite future is optimized with a performance index that has a
moving initial time and terminal time. The objective of this study
is to establish a model predictive control method for crystal growth
dynamics of semiconductor materials.
Improved Blood Glucose-Insulin Monitoring with Dual-Layer Predictive Control Design
In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.
Model Predictive Control with Unscented Kalman Filter for Nonlinear Implicit Systems
A class of implicit systems is known as a more
generalized class of systems than a class of explicit systems. To
establish a control method for such a generalized class of systems, we
adopt model predictive control method which is a kind of optimal
feedback control with a performance index that has a moving
initial time and terminal time. However, model predictive control
method is inapplicable to systems whose all state variables are not
exactly known. In other words, model predictive control method is
inapplicable to systems with limited measurable states. In fact, it
is usual that the state variables of systems are measured through
outputs, hence, only limited parts of them can be used directly. It is
also usual that output signals are disturbed by process and sensor
noises. Hence, it is important to establish a state estimation method
for nonlinear implicit systems with taking the process noise and
sensor noise into consideration. To this purpose, we apply the model
predictive control method and unscented Kalman filter for solving
the optimization and estimation problems of nonlinear implicit
systems, respectively. The objective of this study is to establish a
model predictive control with unscented Kalman filter for nonlinear
Numerical Simulations on Feasibility of Stochastic Model Predictive Control for Linear Discrete-Time Systems with Random Dither Quantization
The random dither quantization method enables us
to achieve much better performance than the simple uniform
quantization method for the design of quantized control systems.
Motivated by this fact, the stochastic model predictive control
method in which a performance index is minimized subject to
probabilistic constraints imposed on the state variables of systems
has been proposed for linear feedback control systems with random
dither quantization. In other words, a method for solving optimal
control problems subject to probabilistic state constraints for linear
discrete-time control systems with random dither quantization has
been already established. To our best knowledge, however, the
feasibility of such a kind of optimal control problems has not
yet been studied. Our objective in this paper is to investigate the
feasibility of stochastic model predictive control problems for linear
discrete-time control systems with random dither quantization. To
this end, we provide the results of numerical simulations that verify
the feasibility of stochastic model predictive control problems for
linear discrete-time control systems with random dither quantization.
A Model Predictive Control Based Virtual Active Power Filter Using V2G Technology
This paper presents a virtual active power filter (VAPF) using vehicle to grid (V2G) technology to maintain power quality requirements. The optimal discrete operation of the power converter of electric vehicle (EV) is based on recognizing desired switching states using the model predictive control (MPC) algorithm. A fast dynamic response, lower total harmonic distortion (THD) and good reference tracking performance are realized through the presented control strategy. The simulation results using MATLAB/Simulink validate the effectiveness of the scheme in improving power quality as well as good dynamic response in power transferring capability.
Online Robust Model Predictive Control for Linear Fractional Transformation Systems Using Linear Matrix Inequalities
In this paper, the problem of robust model predictive control (MPC) for discrete-time linear systems in linear fractional transformation form with structured uncertainty and norm-bounded disturbance is investigated. The problem of minimization of the cost function for MPC design is converted to minimization of the worst case of the cost function. Then, this problem is reduced to minimization of an upper bound of the cost function subject to a terminal inequality satisfying the l2-norm of the closed loop system. The characteristic of the linear fractional transformation system is taken into account, and by using some mathematical tools, the robust predictive controller design problem is turned into a linear matrix inequality minimization problem. Afterwards, a formulation which includes an integrator to improve the performance of the proposed robust model predictive controller in steady state condition is studied. The validity of the approaches is illustrated through a robust control benchmark problem.
Sampled-Data Model Predictive Tracking Control for Mobile Robot
In this paper, a sampled-data model predictive tracking
control method is presented for mobile robots which is modeled as
constrained continuous-time linear parameter varying (LPV) systems.
The presented sampled-data predictive controller is designed by linear
matrix inequality approach. Based on the input delay approach, a
controller design condition is derived by constructing a new Lyapunov
function. Finally, a numerical example is given to demonstrate the
effectiveness of the presented method.
Design and Development of Real-Time Optimal Energy Management System for Hybrid Electric Vehicles
This paper describes a strategy to develop an energy
management system (EMS) for a charge-sustaining power-split hybrid
electric vehicle. This kind of hybrid electric vehicles (HEVs) benefit
from the advantages of both parallel and series architecture. However,
it gets relatively more complicated to manage power flow between the
battery and the engine optimally. The applied strategy in this paper is
based on nonlinear model predictive control approach. First of all, an
appropriate control-oriented model which was accurate enough and
simple was derived. Towards utilization of this controller in real-time,
the problem was solved off-line for a vast area of reference signals
and initial conditions and stored the computed manipulated variables
inside look-up tables. Look-up tables take a little amount of memory.
Also, the computational load dramatically decreased, because to find
required manipulated variables the controller just needed a simple
interpolation between tables.
Application of Fractional Model Predictive Control to Thermal System
The article presents an application of Fractional Model Predictive Control (FMPC) to a fractional order thermal system using Controlled Auto Regressive Integrated Moving Average (CARIMA) model obtained by discretization of a continuous fractional differential equation. Moreover, the output deviation approach is exploited to design the K -step ahead output predictor, and the corresponding control law is obtained by solving a quadratic cost function. Experiment results onto a thermal system are presented to emphasize the performances and the effectiveness of the proposed predictive controller.
Distributed Coordination of Connected and Automated Vehicles at Multiple Interconnected Intersections
In connected vehicle systems where wireless communication is available among the involved vehicles and intersection controllers, it is possible to design an intersection coordination strategy that leads the connected and automated vehicles (CAVs) travel through the road intersections without the conventional traffic light control. In this paper, we present a distributed coordination strategy for the CAVs at multiple interconnected intersections that aims at improving system fuel efficiency and system mobility. We present a distributed control solution where in the higher level, the intersection controllers calculate the road desired average velocity and optimally assign reference velocities of each vehicle. In the lower level, every vehicle is considered to use model predictive control (MPC) to track their reference velocity obtained from the higher level controller. The proposed method has been implemented on a simulation-based case with two-interconnected intersection network. Additionally, the effects of mixed vehicle types on the coordination strategy has been explored. Simulation results indicate the improvement on vehicle fuel efficiency and traffic mobility of the proposed method.
DFIG-Based Wind Turbine with Shunt Active Power Filter Controlled by Double Nonlinear Predictive Controller
This paper presents a wind turbine based on the doubly fed induction generator (DFIG) connected to the utility grid through a shunt active power filter (SAPF). The whole system is controlled by a double nonlinear predictive controller (DNPC). A Taylor series expansion is used to predict the outputs of the system. The control law is calculated by optimization of the cost function. The first nonlinear predictive controller (NPC) is designed to ensure the high performance tracking of the rotor speed and regulate the rotor current of the DFIG, while the second one is designed to control the SAPF in order to compensate the harmonic produces by the three-phase diode bridge supplied by a passive circuit (rd, Ld). As a result, we obtain sinusoidal waveforms of the stator voltage and stator current. The proposed nonlinear predictive controllers (NPCs) are validated via simulation on a 1.5 MW DFIG-based wind turbine connected to an SAPF. The results obtained appear to be satisfactory and promising.
Model Predictive Control of Turbocharged Diesel Engine with Exhaust Gas Recirculation
Control of diesel engine’s air path has drawn a lot of attention due to its multi input-multi output, closed coupled, non-linear relation. Today, precise control of amount of air to be combusted is a must in order to meet with tight emission limits and performance targets. In this study, passenger car size diesel engine is modeled by AVL Boost RT, and then simulated with standard, industry level PID controllers. Finally, linear model predictive control is designed and simulated. This study shows the importance of modeling and control of diesel engines with flexible algorithm development in computer based systems.
Model Predictive Control of Three Phase Inverter for PV Systems
This paper presents a model predictive control (MPC)
of a utility interactive three phase inverter (TPI) for a photovoltaic
(PV) system at commercial level. The proposed model uses phase
locked loop (PLL) to synchronize the TPI with the power electric
grid (PEG) and performs MPC control in a dq reference frame. TPI
model consists of a boost converter (BC), maximum power point
tracking (MPPT) control, and a three-leg voltage source inverter
(VSI). The operational model of VSI is used to synthesize the
sinusoidal current and track the reference. The model is validated
using a 35.7 kW PV system in Matlab/Simulink. Implementation
results show simplicity and accuracy, as well as reliability of the
Computer-Assisted Management of Building Climate and Microgrid with Model Predictive Control
With 40% of total world energy consumption,
building systems are developing into technically complex large
energy consumers suitable for application of sophisticated power
management approaches to largely increase the energy efficiency
and even make them active energy market participants. Centralized
control system of building heating and cooling managed by
economically-optimal model predictive control shows promising
results with estimated 30% of energy efficiency increase. The research
is focused on implementation of such a method on a case study
performed on two floors of our faculty building with corresponding
sensors wireless data acquisition, remote heating/cooling units and
central climate controller. Building walls are mathematically modeled
with corresponding material types, surface shapes and sizes. Models
are then exploited to predict thermal characteristics and changes in
different building zones. Exterior influences such as environmental
conditions and weather forecast, people behavior and comfort
demands are all taken into account for deriving price-optimal climate
control. Finally, a DC microgrid with photovoltaics, wind turbine,
supercapacitor, batteries and fuel cell stacks is added to make the
building a unit capable of active participation in a price-varying
energy market. Computational burden of applying model predictive
control on such a complex system is relaxed through a hierarchical
decomposition of the microgrid and climate control, where the
former is designed as higher hierarchical level with pre-calculated
price-optimal power flows control, and latter is designed as lower
level control responsible to ensure thermal comfort and exploit
the optimal supply conditions enabled by microgrid energy flows
management. Such an approach is expected to enable the inclusion
of more complex building subsystems into consideration in order to
further increase the energy efficiency.
MP-SMC-I Method for Slip Suppression of Electric Vehicles under Braking
In this paper, a new SMC (Sliding Mode Control)
method with MP (Model Predictive Control) integral action for the
slip suppression of EV (Electric Vehicle) under braking is proposed.
The proposed method introduce the integral term with standard SMC
gain , where the integral gain is optimized for each control period by
the MPC algorithms. The aim of this method is to improve the safety
and the stability of EVs under braking by controlling the wheel slip
ratio. There also include numerical simulation results to demonstrate
the effectiveness of the method.
Nonlinear Model Predictive Control of Water Quality in Drinking Water Distribution Systems with DBPs Objectives
The paper develops a Non-Linear Model Predictive
Control (NMPC) of water quality in Drinking Water Distribution
Systems (DWDS) based on the advanced non-linear quality dynamics
model including disinfections by-products (DBPs). A special attention
is paid to the analysis of an impact of the flow trajectories prescribed
by an upper control level of the recently developed two-time scale
architecture of an integrated quality and quantity control in DWDS.
The new quality controller is to operate within this architecture in the
fast time scale as the lower level quality controller. The controller
performance is validated by a comprehensive simulation study based
on an example case study DWDS.
Experimental Implementation of Model Predictive Control for Permanent Magnet Synchronous Motor
Fast speed drives for Permanent Magnet Synchronous
Motor (PMSM) is a crucial performance for the electric traction
systems. In this paper, PMSM is derived with a Model-based
Predictive Control (MPC) technique. Fast speed tracking is achieved
through optimization of the DC source utilization using MPC. The
technique is based on predicting the optimum voltage vector applied
to the driver. Control technique is investigated by comparing to the
cascaded PI control based on Space Vector Pulse Width Modulation
(SVPWM). MPC and SVPWM-based FOC are implemented with the
TMS320F2812 DSP and its power driver circuits. The designed MPC
for a PMSM drive is experimentally validated on a laboratory test
bench. The performances are compared with those obtained by a
conventional PI-based system in order to highlight the improvements,
especially regarding speed tracking response.
MPC of Single Phase Inverter for PV System
This paper presents a model predictive control (MPC)
of a utility interactive (UI) single phase inverter (SPI) for a
photovoltaic (PV) system at residential/distribution level. The
proposed model uses single-phase phase locked loop (PLL) to
synchronize SPI with the grid and performs MPC control in a dq
reference frame. SPI model consists of boost converter (BC),
maximum power point tracking (MPPT) control, and a full bridge
(FB) voltage source inverter (VSI). No PI regulators to tune and
carrier and modulating waves are required to produce switching
sequence. Instead, the operational model of VSI is used to synthesize
sinusoidal current and track the reference. Model is validated using a
three kW PV system at the input of UI-SPI in Matlab/Simulink.
Implementation and results demonstrate simplicity and accuracy, as
well as reliability of the model.
Space Vector PWM and Model Predictive Control for Voltage Source Inverter Control
In this paper, we present a comparative assessment of
Space Vector Pulse Width Modulation (SVPWM) and Model
Predictive Control (MPC) for two-level three phase (2L-3P) Voltage
Source Inverter (VSI). VSI with associated system is subjected to
both control techniques and the results are compared.
Matlab/Simulink was used to model, simulate and validate the
control schemes. Findings of this study show that MPC is superior to
SVPWM in terms of total harmonic distortion (THD) and
Model Predictive 2DOF PID Slip Suppression Control of Electric Vehicle under Braking
In this paper, a 2DOF (two degrees of freedom) PID (Proportional-Integral-Derivative) controller based on MPC (Model predictive control) algorithm fo slip suppression of EV (Electric Vehicle) under braking is proposed. The proposed method aims to improve the safety and the stability of EVs under braking by controlling the wheel slip ration. There also include numerical simulation results to demonstrate the effectiveness of the method.
Modeling and Control Design of a Centralized Adaptive Cruise Control System
A vehicle driving with an Adaptive Cruise Control
System (ACC) is usually controlled decentrally, based on the
information of radar systems and in some publications based on
C2X-Communication (CACC) to guarantee stable platoons. In this
paper we present a Model Predictive Control (MPC) design of a
centralized, server-based ACC-System, whereby the vehicular platoon
is modeled and controlled as a whole. It is then proven that the
proposed MPC design guarantees asymptotic stability and hence
string stability of the platoon. The Networked MPC design is
chosen to be able to integrate system constraints optimally as well
as to reduce the effects of communication delay and packet loss.
The performance of the proposed controller is then simulated and
analyzed in an LTE communication scenario using the LTE/EPC
Network Simulator LENA, which is based on the ns-3 network
RBF Modelling and Optimization Control for Semi-Batch Reactors
This paper presents a neural network based model predictive control (MPC) strategy to control a strongly exothermic reaction with complicated nonlinear kinetics given by Chylla-Haase polymerization reactor that requires a very precise temperature control to maintain product uniformity. In the benchmark scenario, the operation of the reactor must be guaranteed under various disturbing influences, e.g., changing ambient temperatures or impurity of the monomer. Such a process usually controlled by conventional cascade control, it provides a robust operation, but often lacks accuracy concerning the required strict temperature tolerances. The predictive control strategy based on the RBF neural model is applied to solve this problem to achieve set-point tracking of the reactor temperature against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the desired reaction temperature.
Synchronization of a Perturbed Satellite Attitude Motion
In the paper, the predictive control method is proposed to control the synchronization of two perturbed satellites attitude motion. Based on delayed feedback control of continuous-time systems combines with the prediction-based method of discrete-time systems, this approach only needs a single controller to realize synchronization, which has considerable significance in reducing the cost and complexity for controller implementation.
Investigations into Effect of Neural Network Predictive Control of UPFC for Improving Transient Stability Performance of Multimachine Power System
The paper presents an investigation in to the effect of neural network predictive control of UPFC on the transient stability performance of a multimachine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers, and an improved damping of the power oscillations as compared to the conventional PI controller.
Profit Optimization for Solar Plant Electricity Production
In this paper a stochastic scenario-based model predictive control applied to molten salt storage systems in concentrated solar tower power plant is presented. The main goal of this study is to build up a tool to analyze current and expected future resources for evaluating the weekly power to be advertised on electricity secondary market. This tool will allow plant operator to maximize profits while hedging the impact on the system of stochastic variables such as resources or sunlight shortage.
Solving the problem first requires a mixed logic dynamic modeling of the plant. The two stochastic variables, respectively the sunlight incoming energy and electricity demands from secondary market, are modeled by least square regression. Robustness is achieved by drawing a certain number of random variables realizations and applying the most restrictive one to the system. This scenario approach control technique provides the plant operator a confidence interval containing a given percentage of possible stochastic variable realizations in such a way that robust control is always achieved within its bounds. The results obtained from many trajectory simulations show the existence of a ‘’reliable’’ interval, which experimentally confirms the algorithm robustness.
Flight Control of Vectored Thrust Aerial Vehicle by Neural Network Predictive Controller for Enhanced Situational Awareness
This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a flight control procedure to address the dynamics variation and performance requirement difference of flight trajectory for an unmanned helicopter model with vectored thrust configuration. This control strategy for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.
Periodic Control of a Wastewater Treatment Process to Improve Productivity
In this paper, periodic force operation of a wastewater treatment process has been studied for the improved process performance. A previously developed dynamic model for the process is used to conduct the performance analysis. The static version of the model was utilized first to determine the optimal productivity conditions for the process. Then, feed flow rate in terms of dilution rate i.e. (D) is transformed into sinusoidal function. Nonlinear model predictive control algorithm is utilized to regulate the amplitude and period of the sinusoidal function. The parameters of the feed cyclic functions are determined which resulted in improved productivity than the optimal productivity under steady state conditions. The improvement in productivity is found to be marginal and is satisfactory in substrate conversion compared to that of the optimal condition and to the steady state condition, which corresponds to the average value of the periodic function. Successful results were also obtained in the presence of modeling errors and external disturbances.
Periodic Control of a Reverse Osmosis Water Desalination Unit
Enhancement of the performance of a reverse osmosis
(RO) unit through periodic control is studied. The periodic control
manipulates the feed pressure and flow rate of the RO unit. To ensure
the periodic behavior of the inputs, the manipulated variables (MV)
are transformed into the form of sinusoidal functions. In this case, the
amplitude and period of the sinusoidal functions become the
surrogate MV and are thus regulated via nonlinear model predictive
control algorithm. The simulation results indicated that the control
system can generate cyclic inputs necessary to enhance the closedloop
performance in the sense of increasing the permeate production
and lowering the salt concentration. The proposed control system can
attain its objective with arbitrary set point for the controlled outputs.
Successful results were also obtained in the presence of modeling
Model Predictive Fuzzy Control of Air-ratio for Automotive Engines
Automotive engine air-ratio plays an important role of
emissions and fuel consumption reduction while maintains
satisfactory engine power among all of the engine control variables. In
order to effectively control the air-ratio, this paper presents a model
predictive fuzzy control algorithm based on online least-squares
support vector machines prediction model and fuzzy logic optimizer.
The proposed control algorithm was also implemented on a real car for
testing and the results are highly satisfactory. Experimental results
show that the proposed control algorithm can regulate the engine
air-ratio to the stoichiometric value, 1.0, under external disturbance
with less than 5% tolerance.
Slip Suppression of Electric Vehicles using Model Predictive PID Controller
In this paper, a new model predictive PID controller
design method for the slip suppression control of EVs (electric
vehicles) is proposed. The proposed method aims to improve the
maneuverability and the stability of EVs by controlling the wheel
slip ratio. The optimal control gains of PID framework are derived
by the model predictive control (MPC) algorithm. There also include
numerical simulation results to demonstrate the effectiveness of the