Analysis Of Model Predictive Control Research Articles

Improving Power Quality in Grid-Connected Photovoltaic Systems: A Comparative Analysis of Model Predictive Control in Three-Level and Two-Level Inverters.

The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.

Analysis of Model Predictive Intersection Control for Autonomous Vehicles

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios.

Optimization-based control strategy for a large-scale polyhydroxyalkanoates production in a fed-batch bioreactor using a coupled PDE–ODE system

Abstract Control strategy development for fed-batch bioreactor (FBBR) plays an important role in the improvement of polyhydroxyalkanoate (PHA) production. To develop a feeding strategy for PHA production in a large-scale FBBR, an optimization-based control scheme that considers nutrient dispersion is proposed in this work. A coupled partial differential equations and ordinary differential equation model is proposed to describe the axial-dispersed nutrient and well-dispersed microbial dynamics with process constraints. An analytical model predictive control (AMPC) method that applies integrated variables of nutrients is employed to develop the real-time control system. The control objective is to regulate the PHA concentration at the updated set points by adjusting the nutrient feed rates; a process disturbance is introduced to evaluate the control robustness. Simulation experiments of a fed-batch operation are conducted to investigate the performance of the developed controller; the controlled output is designed to track the updated set points corresponding to the biomass concentration. Results of closed-loop and regulatory systems showed that the proposed control strategy could provide more productivity (33–38%) compared to the applied PI controller. The performance test demonstrates that the developed control system could apply the biomass concentration for updating set points, provide the optimal control actions that promote PHB accumulation and handle the disturbance effectively.

Analysis of model predictive control in numbered-up modular facilities

Modular production units are a key enabling technology for distributed manufacturing, an important emerging paradigm which helps promote sustainability, resource independence, and robustness in critical chemical production and distribution infrastructure. In modular systems, desired material throughput is achieved by “numbering up” rather than “scaling up”. Previous studies have quantified the benefits of introducing modular units when making decisions at a supply chain or process design level. However, modular units provide additional potential benefits at the level of operation and control which heretofore has not been as thoroughly studied. In this paper, we analyze the structure of the optimal modular system control problem and identify three qualities unique to problems with numbered-up units: the ability to control outputs locally or globally, the presence of symmetry in the underlying problem, and the possibility of reconfiguring the connectivity of modular units. We provide analysis and insights on ways to mitigate challenges and exploit beneficial properties resulting from the above qualities through a benchmark modular reactor case study.

A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

A Comprehensive Analysis of Model Predictive Control for Lane Keeping Assist System

Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning

Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning

Comparison and Performance Analysis of Model Predictive Control Developed by Transfer Function Based Model and State Space Based Model for Brushless Doubly Fed Induction Generator

Comparison and Performance Analysis of Model Predictive Control Developed by Transfer Function Based Model and State Space Based Model for Brushless Doubly Fed Induction Generator

Nonlinear Optimization-Based Robust Control Approach for a Two-Stage Anaerobic Digestion Process

A two-stage anaerobic digestion (AD) process has been applied to improve the efficiency of methane production from various organic materials. However, the performance of traditional process controllers may be limited by differences in the rate of biochemical reactions, process uncertainties, and the consequences of interconnection between the two bioreactors. In this work, a nonlinear optimization-based control strategy that applies an analytical model predictive control (AMPC) scheme with an adaptive optimal set-point is proposed for the control of the two-stage AD system. The objectives of the proposed control system are to stabilize the system under uncertain operating conditions and maximize biomethane production. The optimal set-points for the controller are adapted in real-time operation, and then the control system is performed to manipulate the controlled output to the optimal trajectories. Compensators and nonlinear state observers are applied to handle the process/model mismatch and estimate unmeasured variables. The proposed control system is applied to the process with disturbances, fluctuations of inlet stream concentrations, and changes in the bacterial growth rate, and the control performance is investigated. Simulation results show that the developed control scheme automatically adjusts the optimal set-points and provides adequate control actions to maintain the maximum rate of methane production. The results of this investigation demonstrate that the control strategy promotes different biochemical reactions, avoids the inhibition effect, and handles the mutual effects between acidogenic and methanogenic bioreactors for methane production effectively.

Field demonstration and implementation analysis of model predictive control in an office HVAC system

Model Predictive Control (MPC) is a promising technique to address growing needs for heating, ventilation, and air-conditioning (HVAC) systems to operate more efficiently and with greater flexibility. However, due to a number of factors, including the required implementation expertise, lack of high quality data, and a risk-adverse industry, MPC has yet to gain widespread adoption. While many previous studies have shown the advantages of MPC, few analyzed the implementation effort and associated practical challenges. In addition, previous work has developed an open-source, Modelica-based tool-chain that automatically generates optimal control, parameter estimation, and state estimation problems aimed at facilitating MPC implementation. Therefore, this study demonstrates usage of this tool-chain to implement MPC in a real office building, discusses practical challenges of implementing MPC, and estimates the implementation effort associated with various tasks in order to inform the development of future workflows and serve as an initial benchmark for their impact on reducing implementation effort. This study finds that the implemented MPC saves approximately 40% of HVAC energy over the existing control during a two-month trial period and that tasks related to data collection and controller deployment activities can each require as much effort as model generation.

Stability and performance in MPC using a finite-tail cost

In this paper, we provide a stability and performance analysis of model predictive control (MPC) schemes based on finite-tail costs. We study the MPC formulation originally proposed by Magni et al. (2001) wherein the standard terminal penalty is replaced by a finite-horizon cost of some stabilizing control law. In order to analyse the closed loop, we leverage the more recent technical machinery developed for MPC without terminal ingredients. For a specified set of initial conditions, we obtain sufficient conditions for stability and a performance bound in dependence of the prediction horizon and the extended horizon used for the terminal penalty. The main practical benefit of the considered finite-tail cost MPC formulation is the simpler offline design in combination with typically significantly less restrictive bounds on the prediction horizon to ensure stability. We demonstrate the benefits of the considered MPC formulation using the classical example of a four tank system.

A New Approach to Model Predictive Control Based on Two Degrees of Freedom Control and B-Splines Input Shaping

The purpose of this article is to reduce some technical difficulties related to the complexity of stability and feasibility analysis of Model Predictive Control (MPC) as well as to reduce the complexity of the relative optimization procedure. The new approach is based on a two degrees of freedom control scheme where the output $r(k)$ of a feedforward input estimator is used as input forcing the closed-loop system $\Sigma _f$ . This latter is given by the feedback connection of a Linear Time Invariant (LTI) plant with a dynamic output controller. The task of the controller is to guarantee the stability of $\Sigma _f$ , as well as the fulfillment of hard constraints for any $r(k)$ satisfying an “ a priori ” determined admissibility condition. The input $r(k)$ is computed through the online minimization of a quadratic cost functional and is applied to $\Sigma _f$ according to the usual MPC strategy. To simplify the constrained optimization problem, the input $r(k)$ forcing $\Sigma _f$ is assumed to be given by a B-spline function. This greatly decreases the number of decision variables of the online optimization procedure because B-splines are universal approximators which admit a parsimonious parametric representation. Moreover such parameterization allows us to reformulate the minimization of the cost functional as a box Constrained Least Squares (CLS) problem. It is shown that stability and recursive feasibility of the adopted MPC strategy are guaranteed in advance, regardless the chosen prediction horizon.

Analytical Tuning Method of MPC Controllers for MIMO First-Order Plus Fractional Dead Time Systems

An analytical model predictive control (MPC) tuning method for multivariable first-order plus fractional dead time systems is presented in this paper. First, the decoupling condition of the closed-loop system is derived, based on which the considered multivariable MPC tuning problem is simplified to a pole placement problem. Given such a simplification, an analytical tuning method guaranteeing the closed-loop stability as well as pre-specified time-domain performance is developed. Finally, simulation examples are provided to show the effectiveness of the proposed method.

An analysis of Model Predictive Control with Integral Action applied to Digital Displacement Cylinders

An analysis of Model Predictive Control with Integral Action applied to Digital Displacement Cylinders

Development of a novel 2D single coffee bean model and comparison with a 3D model under varying heating profiles

AbstractCoffee roasting produces several hundreds of aroma compounds that affect its quality. A good roasting model can not only help to control the required energy to reduce the process cost, but also ensure the product quality by cooperating with process analytical technologies and model predictive control strategies. However, current coffee roasting models are limited to constant heating temperature application due to high computational cost. Current research is developing a 2D roasting model of a single bean under varying heating profiles and is comparing it with the commonly used 3D model. The 2D model is designed to achieve minimum discrepancies with experimental data using estimated parameters of the bean's thermal conductivity and moisture diffusivity. The model performance showed that the 2D model was able to solve much faster than a 3D model, while the absolute difference in the bean temperature prediction between these two models was less than 5°C. The proposed 2D model shows potential in monitoring the roasted coffee bean quality, where it can provide a cost efficient and reliable bean temperature prediction.Practical applicationsThe application of varying heating profiles can optimize the flavor of roasted coffee beans. This study had developed a 2D model that can efficiently simulate under varying heating conditions. The 2D model also provides comparable accuracy to the 3D model and therefore, proving of its potential to be used in the modeling of batch coffee roasting.

Design and Analysis of Model Predictive Control based Direct and Indirect Current strategy for Single Phase Shunt Active Power Filter

In this paper presents a comparative analysis of two control method applied to a single phase Shunt Active Power Filter (SAPF). It is about Model Predictive Control (MPC) based Direct Current Control (DCC) and Indirect Current Control (IDCC) strategy. The performances of two current control strategies were verified through a simulation with MATLABSimulink Software. Simulation results confirmed that compared to the DCC strategy, the IDCC strategy using MPC becomes simpler and need less hardware components.

Direct adaptive model predictive control tuning based on the first‐order plus dead time models

A direct adaptive tuning strategy is proposed for model predictive controllers. Parameter tuning is essential for a satisfactory control performance. Various tuning methods are proposed in the literature which can be categorised as heuristic, numerical and analytical methods. The proposed tuning methodology is based on an analytical model predictive control tuning approach for plants described by first-order plus dead time models. For a fixed tuning scheme, the tuning performance deteriorates in dealing with unknown or time varying plants. To overcome this problem, an adaptive tuning strategy is utilised. It is suggested to employ a discrete-time model reference adaptive control with recursive least squares estimations for controller tuning. The proposed method is also extended to multivariable systems. The stability and convergence of the proposed strategy is proved using the Lyapunov approach. Finally, simulation and experimental studies are used to show the effectiveness of the proposed methodology.

Control of Ethylene Dichloride Cracking Furnace Using an Analytical Model Predictive Control Strategy for a Coupled Partial Differential Equation/Ordinary Differential Equation System

A nonlinear optimization-based control system with analytical model predictive control (AMPC) structure is formulated in cascade with an off-line pseudo-steady-state calculator for an ethylene dichloride (EDC) cracking furnace process described by a coupled partial differential equation/ordinary differential equation model. The objective of the proposed control system is to control the EDC cracking rate at the desired set points by manipulating the fuel gas flow rate with constraints to avoid extensive coke formation. To handle the complex behaviors that are affected by radiating walls interacting with spatial dynamics of the reactor coil, the set point calculator is employed to provide an optimal target for the constrained optimization-based controller in calculating the control actions. Simulation results show that the proposed control system is successful to regulate the controlled output at the desired set points. Control performance tests with servo and regulatory problems demonstrate that the develo...

Modelling and Robustness Analysis of Model Predictive Control for Electrical Submersible Pump Lifted Heavy Oil Wells

In this paper we consider the problem of automatic control of oil production wells equipped with Electric Submersible Pumps (ESP). To facilitate robustness analysis of automatic control algorithms for such systems, a high fidelity simulator of ESP lifted well producing heavy viscous crude oil, has been developed. Model Predictive Control strategy proposed by the authors in an earlier publication has been tested on this simulator with the main focus on controller performance and robustness. The results demonstrated sufficient robustness of the controller with respect to system nonlinearities, variations in operating conditions, disturbances and measurement noise.

Analytic Model Predictive Control of Uncertain Nonlinear Systems: A Fuzzy Adaptive Approach

A fuzzy adaptive analytic model predictive control method is proposed in this paper for a class of uncertain nonlinear systems. Specifically, invoking the standard results from the Moore-Penrose inverse of matrix, the unmatched problem which exists commonly in input and output dimensions of systems is firstly solved. Then, recurring to analytic model predictive control law, combined with fuzzy adaptive approach, the fuzzy adaptive predictive controller synthesis for the underlying systems is developed. To further reduce the impact of fuzzy approximation error on the system and improve the robustness of the system, the robust compensation term is introduced. It is shown that by applying the fuzzy adaptive analytic model predictive controller the rudder roll stabilization system is ultimately uniformly bounded stabilized in theH-infinity sense. Finally, simulation results demonstrate the effectiveness of the proposed method.

Analytical non-linear model predictive control for hybrid systems with discrete inputs only

Model predictive control (MPC) is known as an efficient technique for controlling different industrial processes. However, the computation load remains the main challenge facing the real-time application of the MPC technique especially for complex systems, like, for example, hybrid systems with discrete or binary variables. In this study the authors propose an analytical non-linear model predictive control (NMPC) technique for hybrid systems with discrete inputs only. The proposed controller has lower computation complexity compared to other techniques presented in the literature; as a result real-time implementation is turned to be possible for several systems. The proposed analytical NMPC controller can be applied efficiently for different classes of hybrid systems: switching systems, linear hybrid systems, non-linear hybrid systems and constrained systems. The proposed technique is validated through several examples representing different classes of hybrid systems with discrete inputs.