Dynamic Matrix Control Controller Research Articles

Economic Oriented Dynamic Matrix Control of Wastewater Treatment Plants

Wastewater Treatment Plants (WWTPs) are industrial facilities, which are important for the protection of the environment, because they remove pollutants from wastewater, before it reaches natural bodies of water. WWTPs consist of complex physical, chemical, and biological energy-intensive processes, which are subject to significant disturbances and uncertainties, due to large variations in the load and quality of the influent. Rising energy prices and increasingly stringent effluent requirements have amplified the need of developing more efficient control schemes for WWTPs. In this paper a novel Economic Dynamic Matrix Control (EDMC) configuration is proposed for WWTPs, where the objective is to minimize the plant’s operating costs in terms of energy savings, while maintaining the effluent quality within acceptable regulatory limits. The novelty of the proposed scheme lies in the combination of the standard Dynamic Matrix Control (DMC) methodology, with economic oriented control strategies. The EDMC predictive models are derived from the application of step tests on the COST/IWA Benchmark Simulation Model No. 1 (BSM1). Based on the BSM1 model, the proposed method is compared to standard Multiple Input–Multiple Output (MIMO) DMC controllers, to the default BSM1 control strategy and to other economic control methods, which have been proposed in the literature. The results illustrate that the proposed EDMC scheme is superior to alternative control strategies in terms of minimizing the energy consumption while, the effluent quality of the plant is maintained at acceptable levels.

Improved sliding mode dynamic matrix control strategy: Application on spindle loading and precision measuring device based on piezoelectric actuator

Spindle workload simulation and precision measurement are important in evaluating the precision of the spindle under different working conditions. Controlling the loading and measuring system accurately is difficult due to the robustness requirements of the controller, the complex dynamic model of the spindle loading system, the difficulty of obtaining system parameters, and the hysteresis of the piezoelectric actuator (PEA). To overcome this challenge, this study refers to the dynamic matrix control (DMC) method and proposes a highly versatile, robust, improved sliding mode DMC predictive controller (ISLDMC). ISLDMC is a non-inverse model control method with strong stickiness and good tracking characteristics. To help the controller achieve good versatility, this study regards nonlinear links, such as hysteresis of PEA, as an unknown disturbance and collects the step response signals of the piezoelectric system as the model state equation. In consideration of the practical application of the piezoelectric system, the prediction function of the state equation is used at the feedforward end of the controller, and the sliding mode function and incremental output are unified at the output end via weighted form optimization. In the feedback part, a variable correction coefficient is introduced into the state equation of the controller to enhance the tracking characteristics of the controller. This improvement is independently verified in a simulation environment. The design method of the controller is provided, the Schur stability of the closed-loop control system is proven. Moreover, the proposed controller is applied to machine tool spindle workload simulation and a precision-measuring device based on piezoelectric actuators. ISLDMC-based displacement-tracking and force-tracking controllers are designed. The experiment showed that the tracking performance and anti-interference, anti-model mismatch, and linear compensation capabilities of ISLDMC are better than those of the DMC controller, PI controller, and controllers from other studies. Furthermore, the linear compensation and anti-disturbance characteristics of the ISLDMC controller with the step response as the state equation are verified through force tracking experiments, which reveal that ISLDMC has good force tracking performance and anti-disturbance and linear compensation characteristics. The effectiveness of the introduction of dynamic correction coefficients is verified through a separate simulation involving displacement tracking and piezoelectric load experiments. The findings confirm that compared with the DMC controller, the ISLDMC controller improves tracking characteristics to a certain extent and has higher robustness.

Optimization control of a 330 MW drum boiler unit based on DMC algorithm and DEB strategy

This paper firstly established a nonlinear dynamic model of a 330 MW drum boiler unit, which has a clear physical meaning and is conducive to the design of coordinated control system (CCS). On this basis, the boiler energy storage coefficient is calculated, and its relationship with that in direct energy balance (DEB) strategy is analyzed. Secondly, this paper proposes a CCS based on dynamic matrix control (DMC) algorithm and DEB strategy, in which: (1) energy demand and status signals of DEB are taken as the setting point and process value of DMC controller, so as to overcome the internal disturbance of the boiler; (2) a compensation signal is constructed as the feedforward input of DMC controller, so as to overcome the external disturbance of the turbine and realize the timely utilization and supplement of boiler energy. Simulation shows: (1) there is a certain deviation in main steam pressure when the energy demand and status signals are taken as the setting point and process value, but this is an illusion of insufficient energy, and in fact the energy status can already meet the demand; (2) compared with the main steam valve, the constructed compensation signal takes into account more operating parameters of the unit, which greatly improves the accuracy of the compensation. Practical application shows: the grid dispatch order can be well tracked by the unit, the maximum deviation of main steam pressure is ±0.6 MPa in constant pressure mode, and that is ±1.2 MPa in sliding pressure mode, thereby improving the control performance of the unit under high, medium, low, and overall load conditions.

Transient numerical modeling and model predictive control of an industrial-scale steam methane reforming reactor

A steam methane reforming reactor is a key equipment in hydrogen production, and numerical analysis and process control can provide a critical insight into its reforming mechanisms and flexible operation in real engineering applications. The present paper firstly studies the transport phenomena in an industrial-scale steam methane reforming reactor by transient numerical simulations. Wall effect and local non thermal equilibrium is considered in the simulations. A temperature profile of the tube outer wall is given by user defined functions integrated into the ANSYS FLUENT software. Dynamic simulations show that the species distribution is closely related to the temperature distribution which makes the temperature of the reactor tube wall an important factor for the hydrogen production of the reformer and the thermal conductivity of the catalyst network is crucial in the heat transfer in the reactor. Besides, there exists a delay of the reformer's hydrogen production when the temperature profile of the tube wall changes. Among inlet temperature, inlet mass flow rate and inlet steam-to-carbon (S/C) ratio, the mass flow rate is the most influencing factor for the hydrogen production. The dynamic matrix control (DMC) scheme is subsequently designed to manipulate the mole fraction of hydrogen of the outlet to the target value by setting the temperature profile trajectory of the reforming tube with time. The proportional-integral control strategy is also studied for comparison. The closed-loop simulation results show that the proposed DMC control strategy can reduce the overshoot and have a small change of the input variable. In addition, the disturbances of feed disturbance can also be well rejected to assure the tracking performance, indicating the superiority of the DMC controller. All the results give insight to the theoretical analysis and controller design of a steam methane reformer and demonstrate the potential of the CFD modeling in study the transport mechanism and the idea of combining CFD modeling with controller design for the real application.

Simultaneous stable control of temperature field distribution uniformity and consistency for multi-temperature zone systems

As a key factor characterizing the control accuracy of multi-temperature zone systems (MTZSs), the stable control of temperature field distribution uniformity and consistency is of critical importance for MTZSs, and it largely determines the product quality and production efficiency. Due to the complicated multiple input and output properties, as well as the various external variations in practice, however, it is extremely difficult to monitor the temperature field distribution in production process. To address the uniform and consistent temperature field distribution problem in MTZSs, a multi-variable dynamic matrix control (DMC)-based predictive control mechanism is proposed in this paper. Specifically, we first establish a finite element-based heat transfer model to analyse heat transfer within the multi-temperature zone, and then propose a multi-variable DMC-based decoupling design method to decompose the entire system into multiple subsystems with single-input single-output for temperature uniformity distribution control in MTZS. By utilizing the ANSYS tools to analyse the transient field temperatures, we obtain both time and space distribution characteristics of the transient temperature field with the proposed control method, and also compare such results with those obtained using the PID control method. Finally, we apply the proposed multi-variable DMC control mechanism onto a multi-temperature sintering furnace of a practical industrial product line for verification. Results show that, with the proposed control mechanism adopted, the difference between the highest and lowest temperature of any workpiece could be maintained within 5°C in the heat rising up period, which convincingly verifies the effectiveness of the proposed predictive control algorithm in different cases.

DMC controller design for an integrated Allam cycle and air separation plant

Advanced process control is developed for a novel oxy-fuel Allam cycle power plant integrated with an Air Separation Unit (ASU) with no greenhouse gas and NOx emissions. The Dynamic Matrix Control (DMC) controller was developed with AspenTech's DMC Technology Version 11 engineering software. The DMC controller improves nitrogen purity, carbon dioxide purity and flowrate, and power output by an average of 4%. Also, the DMC controller is able to handle disturbances in the range of 12.5% for feed flow changes and 10% for feed composition changes. Substantial enhancement was observed in upgrading carbon dioxide purity from 97% Enhanced Oil Recovery (EOR) grade to 99.9% food/medical grade. Therefore, this advanced control strategy is superior to regulatory PID controllers and the developed DMC controller can be used as a seed model in the actual plant control by combining with the actual plant test data in the calibrate mode.

Development and implementation of advanced control strategies for power plant cycling with carbon capture

In this paper, three model predictive control (MPC) strategies are developed and implemented for coal-fired power plant cycling applications. These strategies correspond to a dynamic matrix control (DMC)-based linear MPC, the proposed nonlinear MPC (NLMPC) and a nonlinear MPC from the literature (L-NMPC) as benchmark. For application purposes, dynamic models of two different coal-fired power plants with carbon capture are addressed: an integrated gasification combined cycle power plant with a water-gas shift membrane reactor (IGCC-MR) and a supercritical pulverized coal-fired power plant with monoethanolamine-based post-combustion carbon capture (SCPC-MEA). Successful MPC implementations on IGCC-MR system and SCPC-MEA carbon capture subsystem are addressed, including cycling trajectory tracking and disturbance rejection scenarios. The closed-loop results show that the proposed nonlinear MPC (NLMPC) improves the control performance by up to 96% when compared to the DMC controller in terms of the integral squared error (ISE) results.

Dynamic Matrix Control and Tuning Parameters Analysis for a DC Motor System Control

Model predictive control (MPC) in system control industry overrides the challenges of conventional controllers in controlling complex systems. However, for efficient control, it is essential to find the best combination of parameter values. In this paper, we present the implementation of a multivariable dynamic matrix control (DMC) algorithm. An industrial system consisting of a DC motor, coupled to a mechanical load, the assembly associated with an electronic speed variator was considered to test the implemented DMC controller. DMC’s tuning parameter analysis on the manipulated inputs and their variations on the controlled outputs was performed. Results guarantee that efficient control was presented.

Implementation of Multiple-Input Multiple-Output Dynamic Matrix Control Algorithm for Fast Processes Using Field Programmable Gate Array

The objective of this paper is to describe an implementation of a Dynamic Matrix Control (DMC) algorithm for a Field Programmable Gate Array (FPGA). The DMC algorithm is implemented in a universal version for multiple-input multiple-output dynamic processes. The results of real experiments are presented in which the DE2i-150 development board produced by Terasic with Intel Cyclone IV GX chip is used. As the controlled process a laboratory stand especially developed for this work is used, which is a dynamic system with two manipulated inputs and two controlled outputs. The implemented DMC controller is very fast, it may be used for controlling processed which need sampling periods of a millisecond order.

Adaptive Model Predictive Control of a Quadrotor

This work explores the application of Adaptive Model Predictive Control (AMPC) to quadrotor altitude control. Model Predictive Control (MPC) is a very powerful method of Advanced Control, utilizing an implicit model to make output predictions which are in turn used in computing control action. Dynamic Matrix Control (DMC) was the first MPC technique to be implemented, and is still very common in process control especially for Chemical plants. Due to changes in operating points, the implicit model in MPC typically becomes insufficient, costing the quality of the controller. This work proposes Adaptation (based on the Recursive Least Squares (RLS) algorithm), for online system identification to take changes in operating points into account when computing the DMC control action. Overall, Adaptive DMC is investigated as to whether Adaptive DMC is capable of improved quadrotor control or tracking.

Hybrid Optimization Process Applied to Tuning of Dynamic Matrix Control: Study Case with DC Motor

This paper presents study about Dynamic Matrix Control (DMC) controller applied to speed control of DC motor. DMC controller parameters (prediction horizon, control horizon and damping rate of reference) are obtained through optimization methods employing heuristic, deterministic and hybrid strategies. The use of advanced control technique combined with using of optimization methods aims to achieve highly efficient control, reducing the transient state period and variations in steady state. These methods were applied on a simulation model in order to verify which one provides better control results. Index Terms—Predictive Control, Deterministic Optimization, Heuristic Optimization, Hybrid Optimization, DC motor. 

Model Predictive Control of Vibration in a Two Flexible Link Manipulator — Part I

A model predictive controller (MPC) in the form of dynamic matrix control (DMC) is implemented for attenuating in-plane vibrations of a two flexible link planar manipulator. The rotation of the joints and inertia effect of both the joints and links induce vibration. Piezoelectric actuators, mounted in a bimorph configuration, provide the control actions to reduce vibrations. Implementation of this control scheme is shown to provide appreciable attenuation of vibration over the uncontrolled case, increasing the damping ratio for the first and second link by a factor of 5.99 and 3.40, respectively. DMC control is further shown to reduce the mean amplitude of dominant vibrations from the uncontrolled case by 90.0% and 87.4%, respectively, for the first and second links. Furthermore, for the two link setup, this control is shown to outperform the more conventional ProportionalIntegral-Derivative (PID) control and is sufficiently robust to handle an unknown payload.

Robust stability conditions for DMC controller with uncertain time delay

For Networked Control Systems (NCSs), the conventional Dynamic Matrix Control (DMC) may not applicable due to the unknown transmission delay. The uncertain time delay was usually converted to constant time delay by using registers. This paper addresses the stability problem for single-input-single-output (SISO) linear NCSs with uncertain time delay via DMC controller. A novel DMC controller which is effective for such NCSs has been proposed. Applying Jury’s dominant coefficient lemma, the sufficient stability and stabilization conditions are presented. Finally, numerical examples are given to demonstrate the theoretical results.

Study on Predictive Control Technology Based on Disturbance of Electrically Heated Boiler

Electrically heated boiler is a large inertia, time invariant and nonlinear process. Classical PID control algorithm cannot make the system meet up the needs of performance index. Dynamic matrix control (DMC) predictive control algorithm is adopted to control electrically heated boiler temperature. Simulation result shows that the temperature control system’s overshoot is zero, settling time is 200s, the steady-state error is 9.7574e-006. Compared with PID control, DMC predictive control decreases the overshoot and settling time and improves the system dynamic performance. Aim at disturbance, improved DMC control algorithm is put forward and enhance the system disturbance suppression ability.

Study on Predictive Control Algorithm of Reactor Temperature

Reactor is hard to control because it has the characteristics of time-varying, nonlinear and large time delay. There are many uncertainties in its working process. In order to improve the dynamic performance of reactor temperature control, predictive control technology was adopted in this paper. On the basis of considering the system performance requirements and uncertainties, predictive control model was established. Improved dynamic matrix control (DMC) control algorithm with system uncertainty was designed to control reactor temperature. The simulation result shows that the system has no overshoot and its steady-state error is little than 6.2645e-004. The system has stronger robust performance when uncertainty parameter of system unknown uncertainty matrix G changes. Improved predictive DMC control algorithm can make the system have better dynamic performance than PID control and meet the need of the industry productive process.

Model Predictive Control Strategy for Industrial Process

Model Predictive control (MPC) is shown to be particularly effective for the self-tuning control of industrial processes. It makes use of a truncated step response of the process and provides a simple explicit solution in the absence of constraints. Here we use Dynamic Matrix Control (DMC). DMC uses a set of basis functions to form the future control sequence. The industrial success of DMC has mainly come from its application to high dimension multivariable system without constraints. Here main objective of DMC controller is to drive the output as close to the set point as possible in a least square sense with the possibility of the inclusion of a penalty term on the input moves. Therefore, the manipulated variables are selected to minimize a quadratic objective that can consider the minimization of future error. Implementation of the internal model control is also shown here. The control strategy is to determine the best model for the current operating condition and activate the corresponding controller. Internal model control (IMC) continues to be a powerful strategy in complex, industrial processes control application. This structure provides a practical tool to influence dynamic performance and robustness to modeling error transparently in the design. It is particularly appropriate for the design and implementation of controllers for linear open loop stable system. A simulated example of the control of nonlinear chemical process is shown. The nonlinear chemical process study in this work is the exothermic stirred tank reactors system with the first order reaction. The reaction is assumed to be perfectly mixed and no heat loss occurs within the system. Using internal model control and dynamic matrix control has simulated control of the total process in CSTR. Simulation example provided to show the effectiveness of the proposed control strategy.

Temperature Control of Ultrasupercritical Once-through Boiler-turbine System Using Multi-input Multi-output Dynamic Matrix Control

Multi-input multi-output (MIMO) dynamic matrix control (DMC) technique is applied to control steam temperatures in a large-scale ultrasupercritical once-through boiler-turbine system. Specifically, four output variables (i.e., outlet temperatures of platen superheater, finish superheater, primary reheater, and finish reheater) are controlled using four input variables (i.e., two spray valves, bypass valve, and damper). The step-response matrix for the MIMO DMC is constructed using the four input and the four output variables. Online optimization is performed for the MIMO DMC using the model predictive control technique. The MIMO DMC controller is implemented in a full-scope power plant simulator with satisfactory performance.

Implementation of dynamic matrix control on field programmable gate array

High performance computer is often required by model predictive control (MPC) systems due to the heavy online computation burden. To extend MPC to more application cases with low-cost computation facilities, the implementation of MPC controller on field programmable gate array (FPGA) system is studied. For the dynamic matrix control (DMC) algorithm, the main design idea and the implemental strategy of DMC controller are introduced based on a FPGA’s embedded system. The performance tests show that both the computation efficiency and the accuracy of the proposed controller can be satisfied due to the parallel computing capability of FPGA.

Online implementation of wavelet-based identification and dynamic matrix control in a heat exchanger unit

This paper presents an online implementation of wavelet-based least-square identification (WLSI) and wavelet-based dynamic matrix control (WDMC) in a plate-type heat exchanger unit. Wavelet domain 'blocking' and 'condensing' (B&C) techniques are used to reduce the computation time for optimisation of dynamic matrix control (DMC) performance index. Algorithms for WLSI and WDMC are developed and implemented in the online identification and control of temperature in a heat exchanger unit. The results are compared with conventional PID and DMC controllers. It is observed that the WDMC is better and robust than the other controllers.

Robust stability conditions for remote SISO DMC controller in networked control systems

A two level hierarchy is employed in the design of networked control systems (NCSs) with bounded random transmission delay. At the lower level a local controller is designed to stabilize the plant. At the higher level a remote controller with the dynamic matrix control (DMC) algorithm is implemented to regulate the desirable set-point for the local controller. The conventional DMC algorithm is not applicable due to the unknown transmission delay in NCSs. To meet the requirements of a networked environment, a new remote DMC controller is proposed in this study. Two methods, maximum delayed output feedback and multi-rate sampling, are used to cope with the delayed feedback sensory data. Under the assumption that the closed-loop local system is described by one FIR model of an FIR model family, the robust stability problem of the remote DMC controller is investigated. Applying Jury’s dominant coefficient lemma and some stability results of switching discrete-time systems with multiple delays; several stability criteria are obtained in the form of simple inequalities. Finally, some numerical simulations are given to demonstrate the theoretical results.