A New Kind of Dynamic Matrix Algorithm in the Process Control

A new control strategy based on dynamic matrix control algorithm is proposed in this paper to deal with delay and data packet dropout from sensor to controller as well as from controller to actuator in the networked control system. Through the port setting and sequence controlling at the controller and actuator, the new output of predictive model and the new sequence of predictive control are utilized effectively to compensate the delay and the probable data packet dropout so as to promote the robustness and fault-tolerance capability against the fluctuating QoS.

Improving the wastewater treatment plant performance through model predictive control strategies

Whether the bed temperature of circulating fluidized bed boiler (CFBB) is stable or not affects the efficiency of the entire unit. According to the characteristics of an uncertain system with large delay, large inertia and time-varying of the bed temperature system, this paper proposed an adaptive dynamic matrix control (DMC) algorithm based on the real-time error correction. The algorithm can make the dynamic matrix controller more accurately, more flexibly and more applicably on reflecting system changes by revising the errors during the feedback regulating. The simulation results show that the control algorithm is of good adaptability and robustness and satisfactory control effect in the bed temperature system can be achieved.

The performance of a dynamic matrix control (DMC) algorithm was investigated for the control of a fluidized catalytic cracking (FCC) process operated under full and partial combustion modes. Several types of hierarchical control systems consisting of a supervisory controller and a basic regulatory controller were designed according to the control purpose and the combustion mode. Then, the designed control systems were applied for supervisory unconstrained and constrained control of the FCC process. The dynamic matrix controllers for the unconstrained control showed better performance than PI-controllers for tracking set-points and for rejecting disturbances under both the combustion modes. The supervisory constrained control using the dynamic matrix controllers also showed good performance.

As a typical representative of distributed model predictive control, distributed dynamic matrix control (DDMC) is able to satisfy the basic control requirements for large-scale systems. However, the constraints and disturbances in actual industrial process usually lead to the slow set-point target tracking, large overshoot and weak anti-interference ability of the system. Therefore, the relevant requirements may not be met for some complex industrial processes. The existing distributed PID type dynamic matrix control (PID-DDMC) method can improve the control performance, but it maybe not accurate enough in some cases. Based on this background, this article introduces fractional order PID (FOPID) into distributed dynamic matrix control, and proposes a distributed fractional order PID type dynamic matrix control (FOPID-DDMC) algorithm. To compare with the conventional PID control, it expands the control and parameter setting range of the controller, and makes the control effect of the controller more accurate. Furthermore, the coupling effect among subsystems is dispelled by adopting the Nash optimal theory, and information interaction between the subsystems through network communication is realized, thereby, completing the optimization of the whole large-scale system. Finally, through a numerical simulation example and a level-temperature control process, the feasibility of the proposed algorithm is demonstrated by comparing with the traditional DDMC and PID-DDMC.

Model predictive control (MPC) with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. However, unmeasurable disturbance is widespread in industrial processes, which is difficult to deal with directly at present. In most of the implemented MPC strategies, the method of incorporating a constant output disturbance into the process model is introduced to solve this problem, but it fails to achieve offset-free control once the unmeasured disturbances access the process. Based on the Kalman filter theory, the problem is solved by using a more general disturbance model which is superior to the constant output disturbance model. This paper presents the necessary conditions for offset-free model predictive control based on the model. By applying disturbance model, the unmeasurable disturbance vectors are augmented as the states of control system, and the Kalman filer is used to estimate unmeasurable disturbance and its effect on the output. Then, the dynamic matrix control (DMC) algorithm is improved by utilizing the feed-forward compensation control strategy with the disturbance estimated.

In order to reduce the influence of ink viscosity change on the printing effect, this paper explores the effect of ink temperature change on the viscosity of the ink, so as to control the ink temperature to change the ink molecular tension to achieve the purpose of adjusting the viscosity of the ink. According to the weak nonlinearity, large hysteresis and large inertia of the ink temperature control system, the conventional PID algorithm can not meet the requirements of printing quality. This paper proposes the temperature control strategy of dynamic matrix control (DMC) algorithm, which has strong The anti-interference ability, suppression overshoot, and small steady-state error overcome the influence of the variable pipeline environment on the ink ejection point. At the end of the paper, the simulation is carried out to verify that the control system has better robustness and suppression of overshoot than the conventional PID algorithm in terms of system response speed and suppression of overshoot. Not only improve the quality of the print, but also reduce the failure rate of the nozzle.

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.

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.

In this paper, a novel dynamic matrix control algorithm based on RBF neural network for time varying nonlinear system is presented. RBFNN is used for system model identification, as well as DMC is adopted as optimized controller. Besides, the predictive initiative value is solved by multi-steps prediction of RBF neural network, and nonlinear dynamic matrix coefficients are derived correspondingly. Compared to regular DMC algorithm, the RBFNN-DMC algorithm not only effectively overcomes the large disturbance but also be very robustness. At last, the algorithm is applied in a time varying, high nonlinear Continuous Stirred Tank Reactor (CSTR) pH process model and presents a better control performance.

To improve the passenger-carrying efficiency of multiple elevators in modern buildings and reduce the energy consumption of the system, we propose a dynamic elevator group control algorithm combined with a penalty function based on the scheduling strategies including the shortest distance and the shortest time. In this paper, we propose an elevator busyness degree index function, which can dynamically adjust the weight according to the real-time operation state of the elevator, rather than simply setting the weight value as a fixed constant. We build a PLC program of six elevators in TIA Portal software, then conduct simulation and debugging in EET elevator simulation software in the upper machine by Siemens S7-1200 PLC controller. The simulation results show that the dynamic elevator group control algorithm can improve carrier efficiency and reduce energy consumption to a certain extent.

The electric wheel is a power device which is used to directly drive the vehicle by installing a hub motor on the wheel rim. Distributed drive vehicles utilize the vehicle control unit (VCU) to precisely control the torque and speed of each electric wheel, enabling precise and complex vehicle dynamics control. Independently controlled electric wheels provide more observers and actuators for vehicle dynamics control, as well as higher difficulty and requirement for vehicle dynamics control algorithms, especially for multi-axle distributed drive vehicles. Aiming at distributed drive hybrid multi-axle heavy-duty truck, this paper proposes a dynamics control algorithm. The algorithm uses a hierarchical control structure, including longitudinal and lateral dynamics control strategies, as well as low-layer control functions such as traction control system (TCS), anti-lock braking system (ABS) and braking torque distribution, etc. On the MATLAB/Simulink software platform, the vehicle model was established and the simulation test of the dynamics control algorithm was carried out. The dynamics control algorithm of this paper was applied to an actual liquid hydrogen fuel cell heavy-duty truck of the Beijing Municipal Science and Technology Commission project. Simulation and road test results verified the feasibility, accuracy and real-time performance of the algorithm.

Servo applications often require fast response inner current loop in order to ensure high dynamic performance of the whole system. Predictive control algorithm can achieve fast dynamic response while avoiding overshoot. However, there is an inherent defect that it relies on the accurate mathematical model of the controlled object. An incremental predictive current control algorithm adopting an improved PWM updating strategy was presented. The relationship between system stability and parameter mismatch was analyzed using root locus method. Simulation and experimental results demonstrate that the proposed incremental predictive current control algorithm shows well dynamic performance and the improved updating strategy achieves significantly increased robustness to parameter mismatch, verifying the feasibility of the methods presented.

In order to improve the accuracy and dynamic characteristics of unmanned aerial vehicle (UAV) undertaking platform with Stewart mechanism, a posture control strategy based on dynamic model predictive control algorithm is proposed in this paper. First, the dynamics of the Stewart platform is analyzed, and the state space expression of the dynamic model of the Stewart platform is established. Then, the algorithm based on the dynamic model predictive control is proposed, and the model predictive controller is designed. Finally, a simulation model of the UAV undertaking platform is established, the parameters of the model predictive controller are tuned, and the undertaking platform pose control simulation experiment is carried out. Physical experiments are performed to compare the control effects of traditional PID control and dynamics-based model predictive control on the undertaking platform.The results show that the model predictive control method based on dynamics can effectively improve the control accuracy and dynamic characteristics of the UAV undertaking platform.

A decentralized quadratic dynamic matrix control algorithm