Abstract: 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.
Abstract: 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
implementation.
Abstract: 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.
Abstract: 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.
Abstract: 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
simulator.
Abstract: 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.
Abstract: This paper describes the application of a model
predictive controller to the problem of batch reactor temperature
control. Although a great deal of work has been done to improve
reactor throughput using batch sequence control, the control of the
actual reactor temperature remains a difficult problem for many
operators of these processes. Temperature control is important as
many chemical reactions are sensitive to temperature for formation of
desired products. This controller consist of two part (1) a nonlinear
control method GLC (Global Linearizing Control) to create a linear
model of system and (2) a Model predictive controller used to obtain
optimal input control sequence. The temperature of reactor is tuned
to track a predetermined temperature trajectory that applied to the
batch reactor. To do so two input signals, electrical powers and the
flow of coolant in the coil are used. Simulation results show that the
proposed controller has a remarkable performance for tracking
reference trajectory while at the same time it is robust against noise
imposed to system output.
Abstract: 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
method.
Abstract: This paper presents a new adaptive DMC controller
that improves the controller performance in case of plant-model
mismatch. The new controller monitors the plant measured output,
compares it with the model output and calculates weights applied to
the controller move. Simulations show that the new controller can
help improve control performance and avoid instability in case of
severe model mismatches.
Abstract: This paper is focused on issues of process modeling
and two model based control strategies of a fed-batch sugar
crystallization process applying the concept of artificial neural
networks (ANNs). The control objective is to force the operation into
following optimal supersaturation trajectory. It is achieved by
manipulating the feed flow rate of sugar liquor/syrup, considered as
the control input. The control task is rather challenging due to the
strong nonlinearity of the process dynamics and variations in the
crystallization kinetics. Two control alternatives are considered –
model predictive control (MPC) and feedback linearizing control
(FLC). Adequate ANN process models are first built as part of the
controller structures. MPC algorithm outperforms the FLC approach
with respect to satisfactory reference tracking and smooth control
action. However, the MPC is computationally much more involved
since it requires an online numerical optimization, while for the FLC
an analytical control solution was determined.
Abstract: This paper is focused on issues of nonlinear dynamic process modeling and model-based predictive control of a fed-batch sugar crystallization process applying the concept of artificial neural networks as computational tools. The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. A feed forward neural network (FFNN) model of the process is first built as part of the controller structure to predict the process response over a specified (prediction) horizon. The predictions are supplied to an optimization procedure to determine the values of the control action over a specified (control) horizon that minimizes a predefined performance index. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. However, the simulation results demonstrated smooth behavior of the control actions and satisfactory reference tracking.
Abstract: This paper describes the application of a model predictive controller to the problem of batch reactor temperature control. Although a great deal of work has been done to improve reactor throughput using batch sequence control, the control of the actual reactor temperature remains a difficult problem for many operators of these processes. Temperature control is important as many chemical reactions are sensitive to temperature for formation of desired products. This controller consist of two part (1) a nonlinear control method GLC (Global Linearizing Control) to create a linear model of system and (2) a Model predictive controller used to obtain optimal input control sequence. The temperature of reactor is tuned to track a predetermined temperature trajectory that applied to the batch reactor. To do so two input signals, electrical powers and the flow of coolant in the coil are used. Simulation results show that the proposed controller has a remarkable performance for tracking reference trajectory while at the same time it is robust against noise imposed to system output.
Abstract: 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.
Abstract: Model Predictive Control (MPC) is increasingly being
proposed for real time applications and embedded systems. However
comparing to PID controller, the implementation of the MPC in
miniaturized devices like Field Programmable Gate Arrays (FPGA)
and microcontrollers has historically been very small scale due to its
complexity in implementation and its computation time requirement.
At the same time, such embedded technologies have become an
enabler for future manufacturing enterprises as well as a transformer
of organizations and markets. Recently, advances in microelectronics
and software allow such technique to be implemented in embedded
systems. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and
applied control technique in the industrial engineering. In fact in
this paper, we propose an efficient framework for implementation
of Generalized Predictive Control (GPC) in the performed STM32
microcontroller. The STM32 keil starter kit based on a JTAG interface
and the STM32 board was used to implement the proposed GPC
firmware. Besides the GPC, the PID anti windup algorithm was
also implemented using Keil development tools designed for ARM
processor-based microcontroller devices and working with C/Cµ
langage. A performances comparison study was done between both
firmwares. This performances study show good execution speed and
low computational burden. These results encourage to develop simple
predictive algorithms to be programmed in industrial standard hardware.
The main features of the proposed framework are illustrated
through two examples and compared with the anti windup PID
controller.
Abstract: Model Predictive Control has been previously applied
to supply chain problems with promising results; however hitherto
proposed systems possessed no information on future demand. A
forecasting methodology will surely promote the efficiency of
control actions by providing insight on the future. A complete supply
chain management framework that is based on Model Predictive
Control (MPC) and Time Series Forecasting will be presented in this
paper. The proposed framework will be tested on industrial data in
order to assess the efficiency of the method and the impact of
forecast accuracy on overall control performance of the supply chain.
To this end, forecasting methodologies with different characteristics
will be implemented on test data to generate forecasts that will serve
as input to the Model Predictive Control module.
Abstract: In this paper, we consider Wiener nonlinear model for solid oxide fuel cell (SOFC). The Wiener model of the SOFC consists of a linear dynamic block and a static output non-linearity followed by the block, in which linear part is approximated by state-space model and the nonlinear part is identified by a polynomial form. To control the SOFC system, we have to consider various view points such as operating conditions, another constraint conditions, change of load current and so on. A change of load current is the significant one of these for good performance of the SOFC system. In order to keep the constant stack terminal voltage by changing load current, the nonlinear model predictive control (MPC) is proposed in this paper. After primary control method is designed to guarantee the fuel utilization as a proper constant, a nonlinear model predictive control based on the Wiener model is developed to control the stack terminal voltage of the SOFC system. Simulation results verify the possibility of the proposed Wiener model and MPC method to control of SOFC system.
Abstract: A model predictive controller based on recursive learning is proposed. In this SISO adaptive controller, a model is automatically updated using simple recursive equations. The identified models are then stored in the memory to be re-used in the future. The decision for model update is taken based on a new control performance index. The new controller allows the use of simple linear model predictive controllers in the control of nonlinear time varying processes.
Abstract: This paper proposed a nonlinear model predictive
control (MPC) method for the control of gantry crane. One of the main
motivations to apply MPC to control gantry crane is based on its
ability to handle control constraints for multivariable systems. A
pre-compensator is constructed to compensate the input nonlinearity
(nonsymmetric dead zone with saturation) by using its inverse
function. By well tuning the weighting function matrices, the control
system can properly compromise the control between crane position
and swing angle. The proposed control algorithm was implemented for
the control of gantry crane system in System Control Lab of University
of Technology, Sydney (UTS), and achieved desired experimental
results.
Abstract: 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.
Abstract: Model Predictive Control (MPC) is an established control
technique in a wide range of process industries. The reason for
this success is its ability to handle multivariable systems and systems
having input, output or state constraints. Neverthless comparing to
PID controller, the implementation of the MPC in miniaturized
devices like Field Programmable Gate Arrays (FPGA) and microcontrollers
has historically been very small scale due to its complexity in
implementation and its computation time requirement. At the same
time, such embedded technologies have become an enabler for future
manufacturing enterprisers as well as a transformer of organizations
and markets. In this work, we take advantage of these recent advances
in this area in the deployment of one of the most studied and applied
control technique in the industrial engineering. In this paper, we
propose an efficient firmware for the implementation of constrained
MPC in the performed STM32 microcontroller using interior point
method. Indeed, performances study shows good execution speed
and low computational burden. These results encourage to develop
predictive control algorithms to be programmed in industrial standard
processes. The PID anti windup controller was also implemented in
the STM32 in order to make a performance comparison with the
MPC. The main features of the proposed constrained MPC framework
are illustrated through two examples.