Dynamic Matrix Control Research Articles

Current Stress Minimization Based on Particle Swarm Optimization for Dual Active Bridge DC–DC Converter

Under extended-phase-shift (ESP) control, the current stress of the dual active bridge converter (DAB) is relatively high, which reduces the efficiency of the converter. To solve this problem, a particle swarm optimization (PSO) algorithm based on minimizing the current stress is proposed in this paper. The optimal phase-shift ratio of the DAB converter with ESP control is obtained by using the algorithm’s optimization characteristic. This approach ensures that the converter achieves minimal current stress, thereby enhancing the steady-state performance of the DAB converter. Moreover, in terms of dynamic performance, traditional PI control has poor dynamic response ability when there are sudden changes in load and input voltage. To solve this problem, the voltage dynamic matrix control (DMC) algorithm is introduced to combine with the PSO algorithm to minimize the current stress of the DAB converter under EPS control while enhancing the dynamic response capability of the DAB converter. A simulation model was constructed for comparative validation on MATLAB/Simulink 2019, demonstrating the correctness and effectiveness of the improved control method.

Data-driven plant-model mismatch detection for dynamic matrix control systems using sum-of-norms regularization

This article addresses the plant-model mismatch detection problem for linear multiple-input and multiple-output systems operating under the constrained dynamic matrix control (DMC) with the assumption of unknown noise models. An autocovariance-based mismatch detection method that uses sum-of-norms regularization is proposed, aiming to detect parameter jumps and estimate the noise model separately. The intention of introducing regularization is not only to be able to segment the mismatch so that the mismatch is piece-wise constant in time, but also to make the method robust to colored noise. Moreover, a method to alleviate mis-detection caused by unknown operating conditions is proposed. We show that the method can detect significant jumps in parameters and thus provide a priori knowledge for system re-identification and timing of updating the model. Finally, the feasibility of the proposed method under closed-loop conditions is analyzed from a stochastic perspective and demonstrated with illustrative examples.

Nonlinear model-based predictive control of a yeast fermentation bioreactor using new bee colony-WNN modelling structure

This paper concentrates on the application of a newly developed model for the nonlinear control of a yeast fermentation bioreactor. The primary control objectives are the maintenance of temperature and product concentration within the desired range. So, having an accurate model in control strategy is not ignorable and this model influences on close-loop controllability. In the context of online control, the parameters are changed (the system is time varying) and precision of extracted model is more important. Moreover, there is a demand for a computationally efficient model, and the convergence speed during the training of the neural network is significant. To overcome the challenges associated with slow convergence, the risk of being trapped in local minima, and the imperative of achieving computational efficiency, this study introduces a novel combination of a pruned Bee Colony Wavelet Neural Network for the online modeling of the bioreactor. The method's superiority lies in its ability to conduct searches in multiple sections independently, thereby avoiding local minima. The hybrid model controllability is further investigated using Dynamic Matrix Control (DMC) and Generalized Matrix Control (GPC) techniques. Subsequently, the paper explores the application of Nonlinear Model Predictive Control (NMPC) by solving a Gauss-Newton quadratic programming problem online, followed by a comparative analysis of the results. It is demonstrated that the precision of the model closely influences profitability and enhances product yields.

Repetitive dynamic matrix control for systems with periodic specifications

This paper proposes a Repetitive Dynamic Matrix Control (RDMC) for systems with periodic specifications. The new algorithm is able to track periodic references and reject repetitive disturbances with a known period based on a modified prediction error. A repetitive version of the Generalized DMC (GDMC) is also proposed such that it can be applied to control open-loop unstable systems. Only the step-response coefficients are required to describe the dynamical system such that the RDMC preserves the modeling simplicity of the Dynamic Matrix Control (DMC). The proposed solution can be interpreted as an extension of the DMC for repetitive control applications. A data-driven filter design is proposed in order to ensure null prediction steady-state error in the presence of periodic disturbances even for unstable open-loop systems. Two case studies are presented to show the usefulness of the proposed strategy for control systems with periodic specification and to illustrate the typical advantages and drawbacks of the proposed repetitive control extension of the DMC algorithm.

Double-Loop-Optimization-Based Joint Parameter Tuning for Dynamic Matrix Control With Validation in Intelligent Cigarette Drying Process

Double-Loop-Optimization-Based Joint Parameter Tuning for Dynamic Matrix Control With Validation in Intelligent Cigarette Drying Process

An intelligent heating system based on the Internet of Things and STM32 microcontroller

Under the rapid growth of Internet of Things technology, many households are moving towards smart solutions. Addressing the inflexibility of temperature control in traditional heating systems, this research focuses on designing an intelligent heating system. To enhance flexibility and intelligence, an intelligent heating system based on the Internet of Things and STM32 microcontroller is proposed. Furthermore, the study identifies limitations of traditional proportional-integral-derivative control methods and establishes an optimization control model for heating system output temperature based on the Dynamic Matrix Control algorithm. Results indicate that the system's web interface successfully draws temperature curves, displaying clear data on detected temperature and humidity. The output temperature optimization control model shows a temperature rise of 2 °C and a temperature control error index of 0.0543 during the initial heating stage, and a control error index of 0.0353 during the mid-heating stage when the valve relative opening is close to 0. And the temperature control effect is better than traditional PID control, fuzzy PID control, genetic algorithm based PID control, and predictive feedback predictive control, without obvious indoor temperature overshoot phenomenon, which has certain advantages. In conclusion, the proposed system and model exhibit favorable application outcomes, offering technological support for the intelligent management of heating systems.

Study on Multivariable Dynamic Matrix Control for a Novel Solar Hybrid STIGT System

To construct a clean and efficient energy system, advanced solar thermal power generation technology is developed, i.e., a solar hybrid STIGT (Steam Injected Gas Turbine) system with near zero water supply. Such a system is conducive to the efficient use of solar energy and water resources, and to improvement of the performance of the overall system. Given that the strong correlation between multiple-input and multiple-output of the new system, the MDMC (Multivariable Dynamic Matrix Control) method is proposed as an alternative to a PID (Proportional-Integral-Derivative) controller to meet requirements in achieving better control characteristics for a complex power system. First, based on MATLAB/Simulink, a dynamic model of the novel system is established. Then it is validated by both experimental and literature data, yielding an error no more than 5%. Subsequently, simulation results demonstrate that the overshoot of output power on MDMC is 1.2%, lower than the 3.4% observed with the PID controller. This improvement in stability, along with a reduction in settling time and peak time by over 50%, highlights the excellent potential of the MDMC in controlling overshoot and settling time in the novel system, while providing enhanced stability, rapidity, and accuracy in the regulation and control of distribution networks.

Exergy-based control strategy design and dynamic performance enhancement for parabolic trough solar receiver-reactor of methanol decomposition reaction

The parabolic trough solar receiver-reactors of methanol decomposition reaction (PTSRR-MDR) enable efficient hydrogen production with solar energy at users’ end. Solar fluctuations affect the solar-to-chemical efficiency, hydrogen yield and system stability, indicating the necessity for an appropriate control strategy. In this study, dynamic and thermodynamic models of PTSRR-MDR with Cu/ZnO/Al2O3 as catalyst were developed. Results of thermodynamic analysis show that an optimal flow rate exists for methanol to achieve the maximum solar-to-chemical exergy efficiency of the PTSRR-MDR. The temperature increases sharply and exceeds the catalyst sintering temperature when the methanol flow rate decreases under its optimal value. Then, control objectives to achieve the maximum efficiency are obtained with a temperature margin of 3 °C to guarantee the PTSRR-MDR safety. Control strategies based on Dynamic Matrix Control (DMC), Forward-Feedback Control (FFC) and Ramp Control (RC) were evaluated under dynamic simulations. Results show that strategies based on DMC, FFC, and RC increase the solar-to-chemical exergy efficiency from 14.76% to 16.31%, 16.28%, and 15.65%, respectively. The Integral Squared Error (ISE) of methanol conversion rate under three methods are 1.46, 0.32 and 5.28, respectively, indicating that strategy based on FFC is considered most suitable for PTSRR-MDR operation control.

Model and algorithm analysis of MPC control in aerospace engineering

With the continuous development of time and technology, the control system has thus evolved in the direction of great complexity. In the field of path control, once control algorithms cannot absolutely meet the requirements of all people, so this paper introduces an alternative algorithm-MPC. This article focuses on the development of MPC from when it is invented and its application in aerospace today with specified examples, and then lists and analyses the mainstream MPC algorithms, namely LTI, LTV, LPV, PWA and nonlinear systems. Article below also illustrates the derivation of sample of non-parametric model-based algorithms (Dynamic matrix control) and parametric model-based algorithms (Generalized Predictive Control) and the elaboration of mainstream systems and their applications in the form of an overview. Eventually gives a conclusion to the current situation of MPC and relationships among various algorithms. Note that this article is mainly aimed at scholars who are new to MPC algorithms.

Adaptive Tuning of Dynamic Matrix Control for Uncertain Industrial Systems With Deep Reinforcement Learning

Adaptive Tuning of Dynamic Matrix Control for Uncertain Industrial Systems With Deep Reinforcement Learning

Truncation error correction by designing input-adjustment-output coefficients for dynamic matrix control based on cubic spline function

The truncation error is an important problem in the dynamic matrix control (DMC) algorithm. Generally, the dynamic information of a plant for the traditional DMC algorithm is determined by the step response. However, the step response of the plant is limited in practice, which leads to the truncation error of the modeling horizon in the feedback correction of DMC algorithm. A truncation error correction method based on cubic spline function is proposed for DMC algorithm, which is realized by designing coefficients and tracking the dynamic response of the system. In the feedback correction of DMC algorithm, the relationship between the correction parameter and the correction difference is constructed by the fitting of cubic spline functions on the intervals. A truncation error correction procedure is presented to calculate the correction parameters by inputting of the correction difference into the relationship. Then, the control increment and predicted model output are computed based on the correction parameters and updated shift matrix. Finally, numerical experiments demonstrate that the control performance of the proposed DMC algorithm has been much improved compared to that of traditional DMC algorithm.

Development of a three-term MPC and its application to an ultra-supercritical coal fired power plant

Most MPC (Model Predictive Control) algorithms used in industries and studied in the control academia use a two-term QP (quadratic programming), where the first term is the weighted norm of the output errors, and the second term is that of the input increments. In this work, a DMC (Dynamic Matrix Control) algorithm that uses three-term QP is developed and studied, where the third term is the weighted norm of the output increments. A relationship between the three-term DMC and the two-term DMC is established; and based on that, ideal closed-loop response curves are derived. These results are useful for the tuning of the three-term DMC. Then, a method for comparing two control methods is proposed and it is shown that the three-term DMC outperforms the two-term DMC. The findings are verified using simulation studies. Finally, the three-term DMC is successfully applied to a 1030 MW ultra-supercritical coal-fired power plant. According to simulation study using the identified model, in comparison to traditional two-term DMC, there is a roughly 10% reduction in the standard deviation of load tracking error for similar control efforts. In real-life tests on the power plant, the performance of the three-term DMC is compared to that of the existing PID controller. The load rate of change increased from 0.8%/min to 1.5%/min while the standard deviation of steam pressure decreased 38%; the standard deviations of the main steam temperature and reheat steam temperature decreased more than 40% with their mean values increased by more than 2 ∘C and 4 ∘C respectively.

One-Step Ahead Control Using Online Interpolated Transfer Function for Supplementary Control of Air-Fuel Ratio in Thermal Power Plants

Recently, the environmental problem has become a global issue. The air to fuel ratio (AFR) in the combustion of thermal power plants directly influences pollutants and thermal efficiency. A research result was published showing that the AFR control performance of thermal power plants can be improved through supplementary control using dynamic matrix control (DMC). However, online optimization of DMC needs an extra computer server in implementation. This paper proposes a practical AFR control with one-step ahead control which does not use online optimization and can be implemented directly in existing distributed control system (DCS) of thermal power plants. Closed-loop transfer function models at three operating points are independently developed offline. Then, an online transfer function using interpolation of offline models is applied at each sampling step. A simple one-step ahead control with online transfer function is applied as a supplementary control of AFR. Simulations with two different type power plants, a 600 MW oil-fired drum-type power plant and a 1000 MW ultra supercritical (USC) coal-fired once-through type power plant, are performed to show the effectiveness of the proposed control structure. Simulation results show that the proposed supplementary control can effectively improve the conventional AFR control performance of power plants.

Modelling and Quadratic Dynamic Matrix Control of a DC Motor System with Non-linearities Consideration

Abstract: DC motor’s models that have often been used to test and validate control algorithms with, even the most robust ones, have usually been simplistic: limiting the generalization and applicability of the results obtained. This article presents a modeling approach for a DC motor system consisting of a Buck converter and driving a load at the end of the shaft, in which the nonlinearities of each of its sub-parts are taken into account in the model of the overall system. The model was simulated and its step responses were compared to those of the linear model and conventional nonlinear models with a significant difference (Pvalue<0.001). This model was then used to test the Fast Nonlinear Quadratic Dynamic Matrix Control (FNLQDMC). The letter has demonstrated good performance in simulation in terms of setpoint tracking, constraint handling, and computational load savings during online optimization problem solving

Constrained Dynamic Matrix Control under International Electrotechnical Commission Standard 61499 and the Open Platform Communications Unified Architecture.

This paper focuses on the implementation of a constrained Dynamic Matrix Control (DMC) approach within the level processes of the FESTO™ MPS-PA Compact Workstation plant in the context of the Industrial Internet of Things (IIoT) paradigm. The goal is to develop an industrial control application with decentralized logic that optimizes the operation of the plant while adhering to specific constraints. The implementation is carried out using the IEC-61499 standard and the OPC-UA protocol, enabling seamless communication between devices and systems. The authors utilize the 4diac-IDE and 4diac-FORTE as the development and runtime environments, respectively, to enable the execution of the control application on low-cost devices. The Beagle Bone Black (BBB) card is used for data acquisition and actuator control. Three types of constraints are considered: control increment (Δu(k)), output (ym(k)), and control (u(k)) constraints, to prevent unnecessary stress on the actuator and avoid damage to the plant. The QP algorithm is employed to optimize the objective function and address these constraints effectively. By integrating advanced control strategies into industrial processes in the IIoT paradigm and implementing them on low-cost devices, this paper demonstrates the feasibility and effectiveness of improving system performance, resource utilization, and overall productivity while considering system limitations and constraints.

A Decoupling Matrix-Based Learning Control Scheme for the Machine Directional Register of Roll-to-Roll Systems

Roll-to-Roll (R2R) printing systems are complex MIMO systems, which makes it difficult to design a register control scheme for them, much less high-precision learning control scheme. In this paper, a decoupling matrix-based simplification scheme is proposed to transform the complex MIMO system to be a simple one. According to the transformed system and the Lyapunov stability theory, a decoupling matrix-based learning control (DMBLC) scheme is proposed for the register control of R2R printing systems, while a data-based evaluation index is designed to assist DMBLC to achieve high register precision through continuous learning. The effectiveness of DMBLC is verified by simulations and experiments. Comparisons among DMBLC, dynamic matrix control and fully decoupled proportional differential control are carried out to show the superior performance of DMBLC.

An LSTM-PSO-Based Predictive Strategy and Its Application in the Main Steam Temperature Control in Power Plants

We proposed an LSTM-PSO predictive controller strategy to overcome the large delay and strong disturbance during main steam temperature control. First, the long short-term memory (LSTM) network model was developed to forecast the future trend of mainstream temperature, and the deviation between the model output and the expected value after feedback correction was minimized by the particle swarm optimization (PSO) algorithm. Then, the LSTM-PSO predictive controller was taken as the primary controller to maintain the steam temperature of the coal-fired boiler. A comparative simulation with conventional PID cascade control and dynamic matrix controller strategies was given, and the test calculations indicate that the adjustment time was shortened and the overshoot was reduced. Finally, the LSTM – PSO predictive control strategy was deployed into the actual boiler operation process of a 300 MW coal-fired power plant, within 0.5 °C steady-state error and 2 °C dynamic error.

Effectiveness of Dynamic Matrix Control algorithm with Laguerre functions

Effectiveness of Dynamic Matrix Control algorithm with Laguerre functions

Research on Multivariable Fuzzy Dynamic Matrix Control Algorithm

To improve the control quality of distillation tower systems that suffer from issues such as coupling, delay, and nonlinearity, traditional control methods have been ineffective. Consequently, this paper proposes using a variable-domain fuzzy dynamic matrix control algorithm for control system optimization. The traditional dynamic matrix control algorithm is augmented with a fuzzy controller, which is utilized to compensate for predicted output. The concept of variable domain is introduced to address the problems of poor adaptive ability and low control accuracy in the Fuzzy-DMC algorithm. Scaling factors are designed to adjust the fuzzy domain, enabling the fuzzy controller to compensate for predicted output even in the event of a significant model mismatch, resulting in improved control performance. The experimental results indicate that the algorithm proposed in this paper is superior to both traditional DMC and Fuzzy-DMC algorithms in terms of rise time, overshoot, and steady-state rate. In addition, this algorithm possesses a simple structure and does not modify the conventional DMC algorithm structure, making it theoretically applicable in practice.

Adaptive Dynamic Additional Margin With N-Level Hierarchy Optimization for Inner Detector Speed Control

Pipeline transportation, as one of the five major transportation methods, has its unique advantages, and its safety guarantee is particularly important. The inner detector is an important tool for pipeline inspection, and its stable real-time speed is an important guarantee to ensure the accuracy of the inspection results for pipeline safety. We proposed an interval dynamic matrix control with additional margin (AM-IDMC), which achieves remarkable results for the speed control of the inner detector. Firstly, we improved AM-IDMC and designed a new penalty factor with adaptive dynamic trigger conditions. Secondly, we design a heuristic evolutionary algorithm N-Level Hierarchy Optimization (NLHO), and give a complete algorithm flow. Then, for the real-time speed control problem of the inner detector, we propose three evaluation index functions, which can be adaptively combined into a comprehensive objective function according to the motion state of the inner detector. Finally, the experimental verification proves the effectiveness and feasibility of the NLHO algorithm on the real-time speed control problem of the inner detector by adaptive dynamic additional margin (ADAM).