Benefits Of Model Predictive Control Research Articles

Closed loop model predictive control of a hybrid battery-hydrogen energy storage system using mixed-integer linear programming

The derivation of an efficient operational strategy for storing intermittent renewable energies using a hybrid battery-hydrogen energy storage system is a difficult task. One approach for deriving an efficient operational strategy is using mathematical optimization in the context of model predictive control. However, mathematical optimization derives an operational strategy based on a non-exact mathematical system representation for a specified prediction horizon to optimize a specified target. Thus, the resulting operational strategies can vary depending on the optimization settings.This work focuses on evaluating potential improvements in the operational strategy for a hybrid battery-hydrogen energy storage system using mathematical optimization. To investigate the operation, a simulation model of a hybrid energy storage system and a tailor-made mixed integer linear programming optimization model of this specific system are utilized in the context of a model predictive control framework. The resulting operational strategies for different settings of the model predictive control framework are compared to a rule-based controller to show the potential benefits of model predictive control compared to a conventional approach. Furthermore, an in-depth analysis of different factors that impact the effectiveness of the model predictive controller is done. Therefore, a sensitivity analysis of the effect of different electricity demands and resource sizes on the performance relative to a rule-based controller is conducted. The model predictive controller reduced the energy consumption by at least 3.9 % and up to 17.9% compared to a rule-based controller. Finally, Pareto fronts for multi-objective optimizations with different prediction and control horizons are derived and compared to the results of a rule-based controller. A cost reduction of up to 47 % is achieved by a model predictive controller with a prediction horizon of 7 days and perfect foresight.

Optimal Energy Management System Control of Permanent Magnet Direct Drive Linear Generator for Grid-Connected FC-Battery-Wave Energy Conversion

The Wave Energy Conversion System (WECS) control strategy is presented in this study to make sure the system operates at its best under fluctuating wave resource situations. The suggested system consists of a MOPSO based MPC approach, a point absorber WEC oscillating in heave, back-to-back power converter for grid connections, and a linear permanent magnet generator. Despite the benefits of model predictive control, problems including switching frequency variations, steady-state errors, high processing costs, and constrained prediction horizons continue to exist. The article presents a method that incorporates the switching control action into the cost function while maintaining the finite nature of a model predictive control to handle the switching frequency issue. In order to minimise switching frequency variations while also addressing other control goals, such as regulating the direct current linked voltage and controlling the flow of active and reactive power, the switching control weight factors are optimised. In order to increase power quality, a fuel cell-based short-term energy storage system is also included to direct current link between the back-to-back converters.

Two layer control strategy of an island DC microgrid with hydrogen storage system

In this paper, a two-layer hierarchical control strategy for an isolated DC microgrid with a hybrid energy storage system is considered. The DC microgrid studied is composed of a short-term battery energy storage system, long-term hydrogen-based energy storage system and renewable energy resources. The proposed control strategy, combines concepts of Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) as a primary level and Model Predictive Control (MPC) as a supervisory level. The IDA-PBC layer guarantees a correct power balance and ensures global stability according to Lyapunov theory, considering the non-linear behavior of the physical system. On the other hand, the MPC layer calculates the reference signals for the primary level, ensuring null stationary error in the desired variables. As known, it is important to keep the electrolyzer, the fuel cell and the batteries in a safe operating mode to increase their useful life and guarantee the quality of the hydrogen produced. In this sense, the benefits of MPC are exploited by considering system constraints in the controller design. The main objective of the two-layer control strategy is to ensure the supply of electrical energy to the loads and quality in the electrical variables of the network. Representative simulation results under demanding conditions verify the effectiveness of combining IDA-PBC and MPC for this class of problems.

Selective Harmonic Mitigation: Limitations of Classical Control Strategies and Benefits of Model Predictive Control

Selective harmonic mitigation pulsewidth modulation (SHMPWM) combined with model predictive control (MPC) is a promising approach for grid-connected power converters. SHMPWM can guarantee grid code compliance in steady state, e.g. grid harmonic injection, with a reduced output converter filter, while MPC improves dynamic response and allows grid code compliance in the event of grid transients. This paper presents a survey of the MPC strategies already published in the literature developed for their use with SHMPWM. The existing strategies fall into two categories: direct model predictive control with an implicit selective harmonic mitigation modulator, and direct model predictive control based on finite control set (FCS-MPC). One representative control strategy of each group is compared to each other and to the performance of classical proportional- integral (PI) controllers combined with SHMPWM. The goal is to identify the potential benefits of MPC for grid-connected power converters, and determine the main advantages and limitations of the two selected state-of-the-art control strategies. Their performance is assessed through Hardware-in-the-Loop (HIL) experimental results in terms of real-time implementation, harmonic content grid code compliance, dynamic response and performance under grid transients.

Handbook of Model Predictive Control [Bookshelf

This handbook contains 27 chapters that are organized into three parts. Part 1 is on theory and comprises 12 chapters, ranging from basic MPC theory to advanced studies and model predictive control (MPC) formulations. Part 2, on computation, includes eight chapters and covers numerical implementation of MPC-related optimization algorithms. Part 3 discusses applications of MPC in numerous fields, such as automotive, power and energy systems, health care, and finance. The book is designed for a wide audience. It is an excellent reference for graduate students, researchers, and practitioners in the field of control systems and numerical optimization who want to understand the potential, challenges, and benefits of MPC and its applications. Alternately, it is an up-to-date reference for MPC research experts (both in academia and industry). For this audience, the book helps experts address new MPC-related problems and research directions. The book provides a thorough and comprehensive reference of the underlying theory, implementation, and applications of MPC. The content of the book, contributed by various experts in the field, is well written and suitably organized into three parts. Furthermore, this book does an excellent job meeting several competing goals: clarity of communication to a diversified audience, formal rigor, and a self-contained presentation of the topics in each chapter. This handbook enables the reader to gain a panoramic viewpoint of MPC theory and practice as well as provides a state-of-the art overview of new and exciting areas of application at the forefront of MPC research.

A Centralized CB-MPC to Suppress Low-Frequency ZSCC in Modular Parallel Converters

The parallel operation of three-phase converters has become an effective way to achieve modularity. However, zero-sequence circulating current (ZSCC) will appear when the multiparalleled converter modules share a common dc link. In the event that the converters have different output powers and inevitable circuit parameter mismatch, low-frequency (LF) ZSCC can be produced. To address the LF-ZSCC issues, in this article, we propose a centralized carrier-based model predictive control (CB-MPC) scheme for modular parallel converters. This control scheme can be implemented either in master converters or in a dedicate central controller. The CB-MPC can achieve both power control and LF-ZSCC elimination. In addition, with carriers adopted, interleaving and fixed device switching frequency can be easily realized. As such, the benefits of model predictive control and interleaving can be combined. Based on the ZSCC model derived in this article, the elimination of LF-ZSCC can be achieved for more than two paralleled converters when they have different output powers and/or with parameter mismatch. The effectiveness of the proposed CB-MPC has been verified by the simulation and experimental results.

Constrained dynamic compensation with model predictive control for tracking

This article extends the benefits of model predictive control for the linear dynamic compensators commonly designed for aerospace systems. The traditional control structures often used in output tracking are synthesized, and the proposed model predictive control scheme modifies the plant input and reference to be tracked, creating artificial demands and control corrections to guarantee the state and satisfaction of the output constraint. The novel predictive controller, with a related reference governor, is presented with assured closed-loop stability, convergence and recursive feasibility for attainable piecewise constant setpoints along the system operation. The modification of the feasible demands and control inputs enlarges the domain of operation of the constrained closed-loop system. The elimination of the quadratic programming solvers running during real-time operation is also presented to reduce the computational burden. The simulation results using a fighter trainer model are finally shown, illustrating the benefits of the proposed technique.

Model predictive control for pre-compensated power converters: Application to current mode control

Current Mode Control (CMC) is the standard approach to regulate DC-DC power converters in high performance applications, allowing to obtain a faster time-response and better closed-loop stability if compared to Voltage Mode Control (VMC). In the last decade, several algorithms have been proposed to improve standard CMC, most of them requiring to replace the original controller. However, it is common to have either analog or embedded CMC controllers which cannot be replaced easily in commercial power converters. Inspired by very recent results in the topic, this paper proposes a Model Predictive Control (MPC) external loop aimed at optimally modifying the set-point of a CMC loop to improve converter performance. The proposed configuration is directly applicable to any pre-compensated converter as it does not require changes on the already-in-place controller. Moreover, by leveraging a multi-rate implementation, the benefits of MPC are introduced in power conversion without affecting much the computational cost of the over-all control system, contrary to what would happen for a direct MPC implementation. Simulation and experimental results on a synchronous DC-DC buck converter, controlled by a standard CMC algorithm, confirm the benefits of the approach.

Robust model predictive control of a plate heat exchanger

Advanced process control includes optimization-based tools that are recently widely implemented in industry to maximize economical effectiveness and to minimize environmental impact. Robust model predictive control (MPC) is one of these strategies and it combines benefits of model predictive control and robust control approaches. This study investigates improvement of control performance and increase of energy savings using the soft-constrained robust MPC with integral action for a laboratory plate heat exchanger. Soft constraints on control inputs keep the heat exchanger in required operation conditions and enable to use the feasible range of manipulated variable effectively with decreasing of energy cost. Integral action of the predictive controller ensures offset-free reference tracking. Simulation results obtained using the newly designed robust predictive controller with soft constraints and integral action confirm improved control response and increased energy savings in comparison with the results reached using the predictive controller with hard constraints and without active soft constraints.

Benefits of model predictive control for gasoline airpath control

One effective possibility to reduce pollutant emissions and fuel consumption of an internal combustion engine is the use of improved process control. It is made viable by the implementation of additional actuators and sensors which allow to operate the process more flexible. For full exploitation of the setup an appropriate control algorithm is necessary. Classical engine control structures rely on the use of many calibration parameters which result in high demands on the calibration time. Model predictive control (MPC) is an advanced control algorithm which is able to overcome this drawback. It allows to use a mathematical plant model for control synthesis which reduces calibration time and makes reusability possible. The present paper introduces the MPC algorithm and discusses the benefits of MPC for engine control. A special emphasis is put on the application for gasoline airpath control. For clarification, the benefits are demonstrated by numerical simulation studies. For the example of gasoline two stage turbocharging, the advantage concerning control performance are shown as well as the possibility to easily adapt to changed specifications for the closed-loop control dynamics.

A Fixed Switching Frequency Scheme for Finite-Control-Set Model Predictive Control—Concept and Algorithm

A fixed switching frequency scheme for finite-control-set model predictive control is presented to achieve an output waveform quality that compares well to that of a pulse-width-modulator-based linear controller, while retaining the benefits of model predictive control. The controller is divided into two parts: the offline calculation and storage of the coefficients of the prediction equations and the online evaluation of the controller. The controller is experimentally evaluated on a five-level flying-capacitor converter.

Predictive control for embedded systems

Recent years have seen a drive to gain the theoretical guarantees and proven practical benefits of model predictive control (MPC) in a broad range of new application areas. The underlying theory of MPC has become well-established over the last decade, and it is now a simple and powerful paradigm for implementing complex control laws. MPC is currently the only technique available for synthesizing controllers that can explicitly ensure constraint satisfaction by design and easily allows for the incorporation of nonlinear dynamics. Contrary to the classic application areas of MPC involving expensive products, slow sampling rates, and massively complex plants, these new systems are characterized by their aggressive demand for fast sample rates, high reliability, low cost, and/or efficient use of energy. This special issue contains seven papers addressing new control and optimization methods, as well as computational software, for implementing predictive control on embedded platforms for high-speed dynamical systems. Hartley et al. 1 presents a field programmable gate array (FPGA)-based model predictive controller (MPC) for two phases of spacecraft rendezvous. The developed interior-point solver is accelerated by moving the main computational component, solving a system of linear equations, onto FPGA hardware. The measured timing indicates a substantial acceleration in computational time. Herceg et al. 2 presents a review of common active set algorithms that have been deployed for the implementation of fast MPC. The extensive theoretical and simulation-based analysis suggests directions for improvements specific to MPC computation. Liniger et al. 3 develops a nonlinear receding horizon-based controller for the racing of small (1:43 scale) RC cars. The nonlinear optimization problems are linearized at each time step and the resulting quadratic programs (QPs) are solved in embedded hardware in milliseconds, resulting in a 50 Hz sampling rate with cars traveling more than 3 m/s. Necoara et al. 4 presents a novel linear model predictive control scheme for use with quadratic programing software that may return a suboptimal or even infeasible solution. The scheme uses an estimate of infeasibility and suboptimality to adaptively tighten constraints and, thereby, ensures recursive satisfaction of constraints and asymptotic stability of the closed-loop system. Holaza et al. 5 develops a novel technique for synthesizing reduced complexity explicit MPC laws, which provide closed-loop stability and recursive satisfaction of constraints. The process involves the automatic generation of a simpler control law over a reduced partition via the solution of a convex optimization problem. Quirynen et al. 6 presents a tutorial of online optimization algorithms and efficient code implementations for embedded nonlinear model predictive control. The ACADO Toolkit for MATLAB is used to explain and showcase how these methods can solve practically relevant problems in only a few tens of microseconds. Kufoalor et al. 7 demonstrates how an existing PC-based industrial MPC solution can be migrated to an embedded platform. The real-time considerations necessary to achieve a functional high-performance predictive controller on a low-cost embedded hardware, with limited computational resources are detailed. We thank the editors of Optimal Control Applications and Methods for hosting this special issue, especially Martin Wells for his invaluable help and guidance.

Model Predictive Control for Reference Tracking on an Industrial Machine Tool Servo Drive

The benefits of model predictive control (MPC) have been well established; however its application to reference tracking on digital servo drives (DSDs), which typically have very fast update rates, is limited by the computational power of present-day processors. This paper presents a novel MPC formulation, which provides a mechanism to trade-off online computation effort with tracking performance, while maintaining stability. This is achieved by introducing a trajectory horizon, which is distinct from the prediction and control horizons typically encountered in MPC formulations. It is shown that increasing the trajectory horizon inherently leads to improved tracking; however larger horizon lengths also have the unwanted effect of increasing online computation. The proposed MPC formulation is compatible with recently developed explicit MPC solutions, and hence the burden of online optimization is avoided. The new approach is successfully implemented on an industrial machine tool DSD, and in terms of tracking accuracy, is shown to outperform the incumbent approach of cascaded PID control.

Explicit Model Predictive Control for Reference Tracking on an Industrial Machine Tool

AbstractThe benefits of Model Predictive Control (MPC) as a control technique have been well established. However its application to reference tracking on Digital Servo Drives (DSDs) which typically have very fast update rates is limited by the computational power of present-day processors. This paper presents a novel MPC formulation, which provides a mechanism to trade-off online computation effort with tracking performance, while maintaining stability. This is achieved by introducing a trajectory horizon, which is distinct from the prediction and control horizons typically encountered in MPC formulations. It is shown that increasing the trajectory horizon inherently leads to improved tracking, however larger horizon lengths also have the unwanted effect of increasing online computation. The proposed MPC formulation is compatible with recently developed explicit MPC solutions, and hence the burden of online optimisation is avoided. The proposed approach (formulated as an explicit MPC solution) is successfully implemented on an industrial machine tool DSD, and is shown to outperform the incumbent approach of cascaded PID control.

Robust constrained predictive controllers for hot rolling mills: Disturbance uncertainty case

Looper—tension control among various control problems in hot rolling mills (HRMs) has significance, in particular, because it affects both operational stability of the process and dimensional quality of the products. The control target in this control loop is to maintain looper angle and strip tension simultaneously at their desired values. The most difficult challenge in the controller design arises from the interaction, between strip tension and looper angle, and uncertainty coming from disturbances and a model mismatch. Recently, there has been some research to investigate the potential benefits of model predictive control (MPC) for this control loop. However, most of them used constrained optimization based on nominal models, and this often caused constraint violations for the uncertain case. The purpose of this paper is to develop robust MPC algorithms for the hot rolling mill process, in the presence of disturbance uncertainty, in order to improve the constraint handling performance.

Economic evaluation and design of an electric arc furnace controller based on economic objectives

Controllers are often designed to satisfy functional objectives, without a thorough analysis of the resulting economic impact. A model predictive controller, designed based on economic objectives, is used to control electric arc furnace variables that are usually under manual control. To make the controller simulation as realistic as possible, the economic data used are taken from an operating furnace, and measurements are only fed back as and when they typically become available. The economic benefits of model predictive control over manual control are also quantified in a simulation study. Potential savings in excess of 5% are predicted by more efficient feed material utilisation and by preventing unscheduled delays.