Search Results (10)

The multi-stack fuel cell system (MFCS) has advantages such as a wide range, long life, and high efficiency; however, its multiple heat sources impose higher requirements on the thermal management system, especially for different stacks. In order to control each stack temperature in an MFCS, the model predictive control (MPC) algorithm based on the backpropagation (BP) neural network is proposed. Firstly, dynamic characteristics have been obtained experimentally for selected PEMFC stacks of different powers. Based on experimental data, a parallel multi-stack fuel cell thermal management subsystem with different stack powers model is established and a system prediction model of the BP neural network is trained by applying the MFCS thermal management subsystem model simulation data. Then, the step response matrix of the system prediction model is obtained at typical operating conditions, and a dynamic matrix controller (DMC) is designed. Finally, a test operating condition is designed for simulation analysis. The results show that the DMC based on BP neural network can quickly and accurately control each stack temperature of the MFCS, while having the characteristics of small overshoot and short regulation time. Full article

As a model predictive control (MPC) technique, dynamic matrix control (DMC) has gained widespread industrial adoption due to its straightforward model construction and clear physical interpretation. However, its effectiveness relies on the accuracy of the predictive model, where measurement inaccuracies or excessive noise in step-response coefficients may significantly degrade control performance. This study enhances robustness of DMC by implementing finite impulse response (FIR) filters on measured step-response coefficients while providing theoretical proof of its stability. The improved algorithm is applied to cold helium temperature control of the modular High-Temperature Gas-Cooled Reactor (mHTGR). A cascade control structure is adopted, where the inner loop uses a PID controller to ensure system stability, while the outer loop uses DMC to adjust the setpoint of the hot helium temperature, thereby controlling the cold helium temperature. Numerical simulation results demonstrate significant improvements in temperature control performance and enhanced robustness of the modified DMC method. Full article

Natural gas-to-methanol plants are receiving renewed interest with the significant increase in shale gas availability. Methanol serves as a crucial raw material for producing various industrial and consumer goods as well as key platform chemicals, including acetic acid, methyl tertiary butyl ether, dimethyl ether, and methylamine. In this research, a dynamic model is developed for Natgasoline’s methanol manufacturing plant. A hierarchical control system comprising Dynamic Matrix Control (DMC) and a basic regulatory control loop is constructed using this dynamic model to minimize methanol losses and utility costs under various process upsets. A subspace identification methodology is used to develop rigorous DMCplus controller models. The simulation results in the ASPEN manufacturing software platform show that the DMCplus controller developed in this study can reduce methanol losses by 96% and utility requirements by 40%. The controller is robust to feed flow variations of ±10%. Furthermore, disturbances due to the variation in hydrogen content in the syngas are also successfully rejected by the controller. This hierarchical multivariable control system performs significantly better than the traditional regulatory PID control strategy in optimizing the methanol process under process constraints. Full article

For some classes of control plants (e.g., large time delay or inverse response), the PID controllers may offer unsatisfactory results; on the other hand, a Model Predictive Control (MPC) algorithm based on a linear model may offer insufficient control quality when applied to nonlinear control plants. To improve the MPC algorithm operation, one can use a steady-state process model; this paper describes how to do this skillfully. The obtained algorithm, based on the popular Dynamic Matrix Control (DMC) algorithm, is detailed. The proposed approach consists in modifying the analytical version of the DMC algorithm in such a way that it can still be expressed as the control law. Thus, the algorithm can still be applied to fast control plants, requiring short sampling times. Though the proposed approach does not modify the DMC algorithm much, it offers improvement in the control quality when the algorithm is employed in a nonlinear control plant. Experiments illustrating the efficiency of the proposed approach were conducted in the control system of a nonlinear chemical reactor. The results show improvement in the control quality compared to a case when the classical MPC algorithm is used. Full article

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. Full article

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. Full article

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 ($\Delta u\left(k\right)$), output ($ym\left(k\right)$), and control ($u\left(k\right)$) 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. Full article

This paper proposes a supplementary control for tighter control of the air–fuel ratio (AFR), which directly affects the environmental emissions of thermal power plants. Dynamic matrix control (DMC) is applied to the supplementary control of the existing combustion control loops and the conventional double cross limiting algorithm for combustion safety is formulated as constraints in the proposed DMC. The proposed supplementary control is simulated for a 600-MW drum-type power plant and 1000 MW ultra-supercritical once-through boiler power plant. The results show the tight control of the AFR in both types of thermal power plants to reduce environmental emissions. Full article

The modular high temperature gas-cooled reactor (MHTGR) based nuclear steam supplying system (NSSS) is constituted by an MHTGR, a once-through steam generator (OTSG) and can generate superheated steam for industrial heat or electric power generation. The wide range closed-loop stability is achieved by the recently proposed coordinated control law, in which the neutron flux and the temperatures of both main steam and primary coolant are chosen as controlled variables, and the flowrates of both primary and secondary loop and the control rod speed are chosen as manipulated variables. However, the thermal power is only controlled in open loop manner and hence could be further optimized through feedback. Motivated by this, a dynamic matrix control (DMC) is proposed for optimizing the thermal power of MHTGR based NSSS. A simple step-response model with the thermal power response data is utilized in designing the DMC. The design objective of DMC is to optimize the deviation of the thermal power from its reference under its rate constraint. Then, by the virtue of strong stability of existing control law and optimization ability of DMC, a cascade control structure is implemented for the thermal power optimization, with the coordinated control law in the inner loop and DMC in the outer loop. Numerical simulation results show the satisfactory improvement of thermal power response. This cascade control structure inherits the advantages of both proportional-integral-differential (PID) control and DMC, by which the zeros offset and the short settling time of thermal power are realized. Full article

This paper proposes a Direct Matrix Converter operating as a Unified Power Flow Controller (DMC-UPFC) with an advanced control method for UPFC, based on the Lyapunov direct method, presenting good results in power quality assessment. This control method is used for real-time calculation of the appropriate matrix switching state, determining which switching state should be applied in the following sampling period. The control strategy takes into account active and reactive power flow references to choose the vector converter closest to the optimum. Theoretical principles for this new real-time vector modulation and control applied to the DMC-UPFC with input filter are established. The method needs DMC-UPFC dynamic equations to be solved just once in each control cycle, to find the required optimum vector, in contrast to similar control methods that need 27 vector estimations per control cycle. The designed controller’s performance was evaluated using Matlab/Simulink software. Controllers were also implemented using a digital signal processing (DSP) system and matrix hardware. Simulation and experimental results show decoupled transmission line active (P) and reactive (Q) power control with zero theoretical error tracking and fast response. Output currents and voltages show small ripple and low harmonic content. Full article