Multivariable Dynamic Matrix Control Research Articles

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.

Optimization and Simulation of DMC Algorithm with Terminal Weight Based on LQR Controller

Based on the multivariable dynamic matrix control algorithm, there are many similarities between the dynamic matrix control algorithm and LQR in the form of state equation, state variable and objective function. We can use the objective function of LQR to improve the objective function of DMC and improve the control effect of the system. Using the given mathematical model, the terminal weighting matrix of LQR is applied to DMC algorithm, and the optimized multivariable dynamic matrix control algorithm is simulated. The results show that the algorithm can effectively reduce the response time of the system, make the system sink faster and reach the given value faster.

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.

Dynamic matrix control for thermal power of multi-modular high temperature gas-cooled reactor plants

To suppress the fluctuation from both load side and intermittent renewable energy (IRE), nuclear power plants (NPPS) should be operated in load-following mode to improve economic competitiveness. The modular high temperature gas-cooled reactor (MHTGR) belongs to the category of small nuclear reactor (SMRs) and is suitable for load-following by the virtue of online refueling ability and inherent safety. To realize economies of scale for MHTGR, multi-modular scheme that multiple nuclear steam supply system (NSSS) modules are connected in parallel providing superheated steam for common turbine is recommended to achieve desired power ratings. However, because of the large heat capacity in the pebble-bed of MHTGR and thermal coupling of different NSSSs through common secondary loop fluid network, the current control strategy which suppresses the nuclear power, coolant temperatures measurement from their set-points without considering thermal dynamic of NSSS itself, may not favorable for heat transfer in the NSSS. To improve the load-following ability, a multivariable dynamic matrix control (DMC) is constituted to dynamic compensate the thermal energy variation of NSSS. The implementation of the DMC has a typical cascade structure, where DMC revises the set-points of NSSS module in outer loop and the existing PID control law is adopt for stabilization in inner loop. Numerical results show that this cascade DMC can improve the transient of thermal power under power maneuvering, and can also attenuate the nuclear power, helium flowrate set-points and feed-water temperature step disturbance.

Dynamic matrix control of thermal power for multiple nuclear steam supply system modules

Abstract To suppress the fluctuation from both load side and intermittent renewable energy (IRE), nuclear power plants (NPPS) should be operated in load-following mode to improve economic competitiveness. The modular high temperature gas-cooled reactor (MHTGR) belongs to the category of small nuclear reactor and is suitable for load-following by the virtue of online refueling ability and inherent safety. To realize economies of scale for MHTGR, multi-modular scheme that multiple nuclear steam supply system (NSSS) modules are connected in parallel providing superheated steam for common turbine is recommended to achieve desired power ratings. However, because of the large heat capacity in the pebble-bed of MHTGR and thermal coupling of different NSSSs through common secondary loop fluid network, the current control strategy which suppresses the nuclear power, coolant temperatures measurement from their set-points without considering thermal dynamic of NSSS itself, may not favorable for heat transfer in the NSSS. To improve the load-following ability, a multivariable dynamic matrix control (DMC) is constituted to dynamic compensate the thermal energy variation of NSSS. The implementation of the DMC has a typical cascade structure, where DMC revises the set-points of NSSS module in outer loop and the existing PID control law is adopt for stabilization in inner loop. Numerical results show that this cascade dynamic matrix control can improve the transient of thermal power under power maneuvering.

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.

Optimization of sizing parameters and multi-objective control of trailing edge flaps on a smart rotor

Abstract In this study, a wind turbine model with a smart rotor is presented to optimize the sizing parameters of the trailing edge flap (TEF) and design the multi-objective TEF controller. The proposed model consists of TEF actuators, and the aerodynamic, applied load, structural, drive chain, and generator models. Additionally, under standard wind turbine control, the present model shows good agreements with FAST at different wind conditions. At the rated steady wind condition, the TEFs yield exactly the same effects as those elicited by FAST. An approach is proposed using two orthogonal experiments to optimize the TEF sizing parameters for maximizing the TEF effects on blade load alleviation and wind turbine output power smoothness. As a result, a group of optimal TEF sizing parameters is obtained. A multi-objective TEF controller adopting multivariable dynamic matrix control (M–DMC) and nonlinear dynamic matrix control (N–DMC) is used to control the flapwise blade root moment and output power. The simulations indicate that the average reduction rates of the flapwise root moment power spectral density (PSD) of blade 1 are within the range of 87–97% at 1 P frequency, and that the average reduction rate of the generator power standard deviation is within the range of 8–42%.

An Intelligent Multi-multivariable Dynamic Matrix Control Scheme for a 160 MW Drum-type Boiler-Turbine System

A 160 MW drum-type boiler-turbine system is developed in the present research through a multi-multivariable dynamic matrix control (DMC) scheme and a multi-multivariable model approach. A novel intelligence-based decision mechanism (IBDM) is realized to support both model approach and control scheme. In such case, the responsibility of the proposed IBDM is to identify the best multivariable model of the system and the corresponding multivariable DMC scheme to cope with the system at each instant of time in an appropriate manner.

Control and dynamic analysis of a packed distillation column based on modeling approaches

In this work, dynamic analysis and control of a packed distillation column have been utilized theoretically and experimentally. In theoretical studies, two types of mathematical models stagewise (Frank model) and partial differential approaches (back-mixing model), were used. Packed distillation uses 1400 mm packing height, and packing type is rashing ring with 20-15 mm diameter. The reboiler was made from a 13 L glass container. Reflux ratio was adjusted by an on-line computer. The system temperature was measured with six thermocouples. For control studies, the reflux ratio and the reboiler heat dutywere chosen as manipulated variables. Perturbation in feed composition was utilized as the disturbance. Decoupling multivariable dynamic matrix control (DDMC) and Nondecoupling multivariable dynamic matrix control (NDMC) of overhead and bottom compositions were applied for control studies. Performance of the control system was tested by using an integral absolute error (IAE) criterion and it was also compared with decoupling multivariable PID control (DPID) and Nondecoupling multivariable PID control (NDPID).

A practical multiple model adaptive strategy for multivariable model predictive control

Model predictive control (MPC) has become the leading form of advanced multivariable control in the chemical process industry. The objective of this work is to introduce a multiple model adaptive control strategy for multivariable dynamic matrix control (DMC). The novelty of the strategy lies in several subtle but significant details. One contribution is that the method combines the output of multiple linear DMC controllers, each with their own step response model describing process dynamics at a specific level of operation. The final output forwarded to the controller is an interpolation of the individual controller outputs weighted based on the current value of the measured process variable. Another contribution is that the approach does not introduce additional computational complexity, but rather, relies on traditional DMC design methods. This makes it readily available to the industrial practitioner.

Integrating process and controller models for the design of self-optimizing control

The steady-state behavior of an existing plant depends on the independent input variables, process equipment and process controllers. This paper presents a method for formulating models that represent the effects of controllers when they are included within a steady-state process flowsheet. The method replaces the controller equations with the equivalent stationarity conditions representing the relationship between the controlled variables and the implemented manipulated variables at steady state. The method is demonstrated for the centralized multivariable Dynamic Matrix Control algorithm applied to two processes, binary distillation and gasoline blending. The integrated process and control system simulation is used to design controllers that improve the profitability of processes without extensive real-time calculations; this is sometimes termed self-optimizing control. For both processes, controllers were designed that yielded higher profit than standard control methods and that approached the highest possible profit achieved by frequent real-time optimization.