Performance Of Model Predictive Control Research Articles

Multimode Model Predictive Control for PMSM Drive System

In order to improve the control performance of the motor model predictive control (MPC) system, a multimode hybrid-vector MPC strategy is proposed in this article, which is developed from the conventional double-vector MPC strategy. The proposed method extends the last voltage vector of the previous control period to the present control period according to the relationship between the last voltage vector of the previous control period and the first voltage vector of the present control period. Then, two voltage vectors or three voltage vectors are included in each control period, which forms the concept of the hybrid vector. Since a voltage vector is inherited from the previous control period, the proposed method can achieve better steady-state control performance at a lower switching frequency, considering the two adjacent control periods as a whole. Finally, comparative experiments demonstrate that the proposed method has better steady-state control performance at the same switching frequency.

Deep optimization of model predictive control performance for wind turbine yaw system based on intelligent fuzzy deduction

Because wind direction has the trait of time varying, yaw is a common state of wind turbine (WT). To improve the energy capture and reduce the yaw actuator usage time, a Model Predictive Control (MPC) method based on the Fuzzy Deduction Weight Coefficient Evaluator (FDWE) is proposed. Specifically, in view of the two contradictory control objectives of energy capture loss ratio and yaw actuator usage ratio in MPC, FDWE is designed to dynamically adjusts the weight coefficient connecting the two objectives according to the predicted wind direction. On this basis, to fully exert the advantage of FDWE, the fuzzy rule and membership function (MF) are tried to be simultaneously optimized. For this complex optimization problem involving different types of multiple design variables, three different solving strategies are proposed: fuzzy rule-MF ordered optimization, mixed integer optimization, and association optimization. Finally, an improved Adaptive Grid Algorithm Multi-Objective Particle Swarm Optimization is presented to solve the formulated optimization problems. The results indicate that the optimized FDWE-MPC using the three strategies not only improves the energy capture of the WT, but also reduces the use of yaw actuator compared to the baseline MPC. Consequently, the proposed method is promising in reducing the production cost for the WTs.

An Advanced Fractional Order Method for Temperature Control

Temperature control in buildings has been a highly studied area of research and interest since it affects the comfort of occupants. Commonly, temperature systems like centralized air conditioning or heating systems work with a fixed set point locally set at the thermostat, but users turn on or turn off the system when they feel it is too hot or too cold. This configuration is clearly not optimal in terms of energy consumption or even thermal comfort for users. Model predictive control (MPC) has been widely used for temperature control systems. In MPC design, the objective function involves the selection of constant weighting factors. In this study, a fractional-order objective function is implemented, so the weighting factors are time-varying. Furthermore, we compared the performance and disturbance rejection of MPC and Fractional-order MPC (FOMPC) controllers. To this end, we have chosen a building model from an EnergyPlus repository. The weather data needed for the EnergyPlus calculations has been obtained as a licensed file from the ASHRAE Handbook. Furthermore, we acquired a mathematical model by employing the Matlab system identification toolbox with the data obtained from the building model simulation in EnergyPlus. Next, we designed several FOMPC controllers, including the classical MPC controllers. Subsequently, we ran co-simulations in Matlab for the FOMPC controllers and EnergyPlus for the building model. Finally, through numerical analysis of several performance indexes, the FOMPC controller showed its superiority against the classical MPC in both reference tracking and disturbance rejection scenarios.

The impact of personal preference-based thermal control on energy use and thermal comfort: Field implementation

Recent research efforts on modeling personal thermal comfort support the integration of personalized preferences in optimal building control and further implementation in real buildings. This paper presents the development and field implementation of personal preference-based thermal control (i.e., a controller that provides thermal conditions to satisfy personal preferences) in real offices, emphasizing the role of model predictive control (MPC) and low-cost local sensing. Probabilistic thermal preference profiles were developed from experiments collected in identical private offices with controllable VAV systems. A low-cost thermal sensing network and a MPC framework were integrated into a centralized building management and control system. Customized, preference-based HVAC control was then experimentally implemented in the offices to (i) quantify the personal comfort penalty when using conventional wall thermostat versus local sensing-based operation for two distinct thermal preference profiles; (ii) evaluate the impact of personalized MPC (dynamic setpoint) on energy use and personal comfort compared with personalized simple feedback control (static setpoint), using local sensing; (iii) compare the personalized MPC performance for two distinct thermal preference profiles under different weather conditions. The results indicate the comfort benefits of monitoring local thermal conditions (vs wall thermostats) for different preference profiles and showed 28–35% energy savings with personalized MPC (vs personalized static setpoint control). The overall personalized MPC performance (and energy consumption) depends on the personal thermal preference characteristics and outdoor conditions.

Robust Model Predictive Current Control of PMSM Based on Nonlinear Extended State Observer

The control performance of traditional model predictive control (MPC) usually deteriorates dramatically with the permanent magnet synchronous motor (PMSM) parameter changes in operation, leading to current harmonics and torque ripple. This article proposes a robust model predictive current control (MPCC) method based on a nonlinear extended state observer (NESO) to resolve this problem. The NESO is used to observe the current disturbance part to avoid the influence of motor parameters on the current model. Because this method is not sensitive to changes in motor parameters, the system is robust and has a low current harmonic. Second, the ESO designed in this article based on the nonlinear function has a lower observation error. In addition, this article uses the fixed coefficient method to simplify the design process. A more accurate parameter design method is proposed based on the bounded analysis of observation error. At the same time, the root locus diagram proves that NESO contributes to the stability of the current loop. Finally, hardware and simulation experiments proved that the robust MPCC based on NESO has lower harmonic content and better dynamic performance.

Dynamic Operability Analysis for Process Design and Control of Modular Natural Gas Utilization Systems.

Process modularization is an alternative process design and construction framework, in which modular units are independent and replaceable blocks of a process system. While modular plants have higher efficiency and are safer to construct than conventional stick-built plants (Roy, S. Chem. Eng. Prog. 2017, 113, 28-31), they are significantly more challenging to operate because of the loss in the control degrees of freedom that comes with process integration and intensification (Bishop, B. A.; Lima, F. V. Processes 2021, 9, 2165). To address this challenge, in this work, operability analyses are performed to consider the design and operation of modular units. Initially, a steady-state operability analysis is employed to find a set of feasible modular designs that are able to operate considering different modular plant conditions. A dynamic operability analysis is then applied to the feasible designs to identify the operable designs that are capable of rejecting the operational disturbances. Lastly, a closed-loop control measure is introduced to compare the performances of the different operable designs. The proposed approach is implemented in a modular membrane reactor to find a set of operable designs considering different natural gas wells, and the respective closed-loop nonlinear model predictive control performance of these units is evaluated.

Optimal Sub-References for Setpoint Tracking: A Multi-level MPC Approach

We propose a novel method to improve the convergence performance of model predictive control (MPC) for setpoint tracking, by introducing sub-references within a multilevel MPC structure. In some cases, MPC is implemented with a short prediction horizon due to limited on-line computation capacity, which could lead to deteriorated dynamic performance. The introduced multi-level optimization method can generate proper sub-references for the MPC setpoint tracking problem, and efficiently improve the dynamic performance. In the higher level a specific performance criterion is taken as the objective, while explicit MPC is utilized in the lower level to represent the control input. The generated sub-references are then used in MPC for the real system with prediction horizon restrictions. Setpoint-tracking MPC for linear systems is used to illustrate the approach throughout the paper. Numerical simulations show that MPC with sub-references significantly improves the convergence performance compared with regular MPC with the same prediction horizon. Thus, it can be concluded that MPC with sub-references has a high potential to tackle more complicated control problems with limited computation capacity.

An Automatic Tuning MPC with Application to Ecological Cruise Control

Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview information. Nevertheless, MPC performance is highly dependent on the choice of cost function tuning parameters. In this work, we demonstrate an approach for online automatic tuning of an MPC controller with an example application to an ecological cruise control system that saves fuel by using a preview of road grade. We solve the global fuel consumption minimization problem ofine using dynamic programming and find the corresponding MPC cost function by solving the inverse optimization problem. A neural network fitted to these offline results is used to generate the desired MPC cost function weight during online operation. The effectiveness of the proposed approach is verified in simulation for different road geometries.

Performance Evaluation of Energy-Optimal Adaptive Cruise Control in Simulation and on a Test Track

This paper presents a comprehensive evaluation of energy-optimal adaptive cruise control (EACC) based on model predictive control (MPC) in comparison to standard adaptive cruise control (ACC) which is equipped for mass-produced cars of a German car maker. First, a simulation study is made in this work to compare the performance of PID-based control and MPC. Afterwards, a prototype of EACC is installed in an electric vehicle (EV), which is tested as the host car and follows a preceding car on a test track. In the test cycles, which represent urban and rural driving scenarios, EACC reduces the energy consumption of the host car by up to 13% in comparison with the standard ACC. The results of the vehicle tests not only demonstrate a significant energy reduction by EACC, but also show that EACC can reduce the risk of rear-end collisions by reacting earlier to rapid deceleration of the vehicle in front. Furthermore, the passengers feel more comfortable as the vehicle speed trajectory is smoothened by EACC. Finally, to investigate the real-time implementation capability of the optimization algorithms, this paper also examines the computation time of the MPC-based EACC function with different lengths of the MPC horizon.

강화학습 기반 화학 공정 제어 성능 향상을 위한 보상 함수 시뮬레이션 연구

Process control is vital for operating chemical process safely and efficiently while maintaining product quality. Process control has been implemented by utilizing traditional techniques such as PID (Proportional-Integrate-Derivative) control and MPC (Model Predictive Control). However, these approaches have disadvantages when the PID tuning rules do not provide optimal tuning parameters for various operating scenarios or when the model fails to match the actual plant. RL (Reinforcement Learning) is one of the most prominent data-driven techniques for addressing such issues and has gained popularity in recent years. While RL has been extensively studied and succeeds in controlling chemical processes, the reward function has a significant impact on its performance. Therefore, it is essential to specify the reward function in order to efficiently control the process. In this study, we propose a reward function to enhance the performance of RL and compare the control performances of MPC and RLs with four distinct reward functions. The controllers track the setpoint of the product in the Van de Vusse process and are evaluated based on the deviation between the setpoint and output. RL with the proposed reward function outperformed the other controllers. Its performance was 12% greater than that of MPC and 6-30% greater than other RL controllers. In chemical processes, the control performance of RL is enhanced by incorporating the time term and positive reward in the reward function, thus outperforming conventional control approaches.

Energy capture optimization of heave oscillating buoy wave energy converter based on model predictive control

The optimal constant damping control based on the peak frequency of irregular wave is commonly adopted to improve the wave energy capture efficiency for the oscillating buoy wave energy converter (WEC) while the influence of the wave frequency change cannot be considered. In this paper, a real-time PTO force control optimization method is proposed based on model predictive control (MPC). Firstly, the dynamic model of the buoy is built based on the Cummins equation. Secondly, the additional penalty terms which may make the optimization problem convex are introduced into the original maximized energy objective, and their effects on the efficiency are investigated. Then, the deterministic sea wave prediction method is used to predict the future wave force aiming to make up the shortage that the classical MPC cannot compensate the external disturbance. Finally, the energy capture performance of MPC for the oscillating buoy WEC was compared with the resistive control. The results show that the MPC method can improve the energy capture by about 40%–60% under the irregular wave conditions.

Development and assessment of a model predictive controller enabling anticipatory control strategies for a heat-pipe system

To support the reliable and resilient operation of modular reactors and microreactors, anticipatory control strategies have been proposed for achieving faster-than-real-time predictions and decision-making capabilities in anticipation of potential anomalies, including setpoint changes and cyber incidents. This work presents how anticipatory control strategies can be implemented via model predictive control (MPC) for a single heat pipe's temperature. Considering the uncertainty in developing and applying MPC, this work evaluates MPC performance given three different model forms: a linear response surface model, an artificial neural network (ANN), and an autoregressive model with exogenous input (ARX). This work also evaluates the impacts of different input biases and variance on MPC performance in order to account for potential sensor reading variations due to cyber incidents. We observe that the ANN and ARX result in more fluctuated control actions compared to the MPC with linear response surface model. However, when the cyber incidents are of large magnitudes, the linear response surface model produces smaller feasible regions than the ANN and ARX models under identical constraints.

Data-Driven Iterative Learning Predictive Control for Power Converters

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method.

Advanced predictive control for GRU and LSTM networks

This article is concerned with Model Predictive Control (MPC) algorithms that use Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for prediction. For two benchmark processes, it is shown that the typical approach to MPC that hinges on successively linearized LSTM or GRU models do not give precise predictions and satisfactory control quality. The presented MPC control schemes utilize online advanced trajectory linearization, which yields simple quadratic optimization programs. It is shown that the discussed approaches give excellent prediction accuracy and control quality, very similar to that possible in MPC with full nonlinear prediction and nonlinear optimization done online. It is also demonstrated that the described MPC algorithms are a few times faster than the MPC method with nonlinear optimization. Moreover, the performance of MPC based on LSTM and GRU networks is compared, and simpler GRU networks are recommended.

QPSO-MPC based tracking algorithm for cable-driven continuum robots.

Cable-driven continuum robots (CDCRs) can flexibly travel through narrow space for complex workspace tasks. However, it is challenging to design the trajectory tracking algorithm for CDCRs due to their nonlinear dynamic behaviors and cable hysteresis characteristics. In this contribution, a model predictive control (MPC) tracking algorithm based on quantum particle swarm optimization (QPSO) is designed for CDCRs to realize effective trajectory tracking under constraints. In order to make kinematic analysis of a CDCR, the forward and inverse mapping among actuation space, joint space and work space is analyzed by using the piecewise constant curvature method and the homogeneous coordinate transformation. To improve the performance of conventional MPC for complex tracking tasks, QPSO is adopted in the rolling optimization of MPC for its global optimization performance, robustness and fast convergence. Both simulation and operational experiment results demonstrate that the designed QPSO-MPC presents high control stability and trajectory tracking precision. Compared with MPC and particle swarm optimization (PSO) based MPC, the tracking error of QPSO-MPC is reduced by at least 43 and 24%, respectively.

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

The paper proposes an adaptive Lyapunov-based nonlinear model predictive control (MPC) to cope with the problems in nonlinear systems subjecting to system constraints and unknown disturbances of the parallel car driving simulator. Commonly, standard nonlinear controllers could guarantee the overall system stability for the parallel structure. However, the constraints tend to impact the control performance and stability adversely. Therefore, MPC plays a vital role in the proposed technique to explicitly consider all the practical constraints and simultaneously enhance the system’s robustness. Nevertheless, the accuracy of the modeling process has a significant effect on the MPC performance, and thus, the convergence cannot be guaranteed in the presence of the model uncertainties. To overcome this problem, by the merit of the fuzzy adaptive law, the control system takes the disturbances and unmodelled parameters into account. Moreover, the feasibility and stability of the approach, which is the fundamental problem of MPC, are ensured according to the Lyapunov-based nonlinear controller, backstepping aggregated with sliding mode control (SMC), and hence inherit advantages of these controls. Simulation results show the efficiency and superior constituted controllers of the proposed method.

Model predictive control for close-proximity maneuvering of spacecraft with adaptive convexification of collision avoidance constraints

This study investigated model predictive control (MPC) for close-proximity maneuvering of spacecraft. It is essential for a designed MPC to effectively handle collision avoidance between the servicer spacecraft and the client spacecraft, especially while the client is rotating. The rotating motion of the client leads to dynamic changes in the collision avoidance constraints, which increases the difficulty of optimizing the control input in the MPC framework. Therefore, this study presents a method to improve the performance and computational efficiency of MPC for rendezvous and docking with a nonrotating or rotating client. An ellipsoid is adopted to model the client’s keep-out zone (KOZ). Given the spherical KOZ of the servicer, an expanded ellipsoid is introduced to describe the KOZ for the center of mass of the servicer and modeled as a nonlinear constraint. The linearization method for reference points located at the expanded ellipsoid is adopted to convexify the nonlinear constraints. The reference points are adaptively determined according to the positions of the servicer, client, and expanded ellipsoidal KOZ. The resulting hyperplanes are then used to describe the collision avoidance constraints. By utilizing the aforementioned strategies, combined with the calculated reference points, an adaptive convex programming algorithm suitable for real-time implementation of MPC is derived. The performance of the proposed method is demonstrated through numerical simulations.

Improved Optimal Duty Model Predictive Current Control Strategy for PMSM

To further improve the steady-state performance of the conventional dual vector model predictive current control (MPCC), an improved optimal duty MPCC strategy for permanent magnet synchronous motor (PMSM) is proposed. This strategy is realized by selecting an optimal voltage vector combination and its duration from the five basic voltage vector combinations, followed by acting on the inverter. The five combinations are: the combination of the optimal voltage vector at the previous moment and basic voltage vector with an angle difference of 60°; the combination of the optimal voltage vector at the previous moment and basic voltage vector with an angle difference of −60°; the combination of the aforementioned three basic voltage vectors with the zero vector. Experimental results indicate that the method effectively reduces the stator current ripple without increasing the calculational burden. Furthermore, it improves the steady-state performance of the system without altering the dynamic performance of the system.

Model predictive control of heating, ventilation, and air conditioning (HVAC) systems: A state-of-the-art review

Due to the fast advancement of communication and information technology, intelligent buildings have garnered great interest. These buildings can forecast weather, ambient temperature, and sun irradiation and can modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on current and previous data. This change is intended to reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. Although substantial research has been conducted on MPC in building HVAC systems, there is a shortage of critical reviews and a lack of a comprehensive framework that formulates and defines the applications. This article provides a comprehensive state-of-the-art overview of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, occupancy behavior, building type, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. The primary objective of this work is to highlight critical design characteristics for the MPC control scheme and to give improved suggestions for future research. Moreover, numerous prospective scenarios have been suggested that might provide future research direction.

On energy-efficient HVAC operation with Model Predictive Control: A multiple climate zone study

This paper aims to quantify the performance of Model Predictive Control (MPC) for a typical commercial building heating, ventilation and air conditioning (HVAC) system across a wide range of climate and weather conditions. The motivation of the study comes from the fact that although there is a large body of work on MPC for HVAC systems, there is a lack of studies that examine the range of possible performance of MPC, in terms of both energy savings and maintaining indoor climate (temperature and humidity) as a function of outdoor weather. A challenge in conducting such a study is developing an MPC controller that can be used in a wide range of weather. The root cause of this challenge is the need for a tractable cooling and dehumidification coil model that can be used by the MPC controller, since the coil may operate in quite distinct modes depending on weather. We present such an MPC controller, and then leverage it to conduct an extensive simulation campaign for fourteen climate zones in the United States and four weather conditions (winter, spring, summer, and fall) in each climate zone. The performance of the proposed controller is compared with not only a rule-based baseline controller but also with a simpler MPC controller that ignores humidity and latent heat considerations. There are several results the arise from this comparative study. One such result is that energy savings from MPC over baseline can vary dramatically based on climate and season. Another is that the effect of ignoring humidity in the MPC formulation can lead to poor indoor humidity control more in milder weather rather than in hot weather. The results from this study can help practitioners and researchers assess costs and benefits of proposed MPC formulations for HVAC control.