Design of an Improved Implicit Generalized Predictive Controller for Temperature Control Systems

Chen, Zheng; Cui, Jialun; Lei, Zhenzhen; Shen, Jiangwei; Xiao, Renxin

doi:10.1109/access.2020.2965021

Cited by 9 publications

(5 citation statements)

References 35 publications

(51 reference statements)

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“…Generally, to improve transition smoothness, controlled object's output strives to align with specified reference trajectory illustrated in Equation (8).…”

Section: Rolling Optimizationmentioning

confidence: 99%

“…The study demonstrated that the approach exhibited a high degree of anti‐interference capability, robustness, and superior control performance. For temperature control system, Chen et al 8 proposed a controller that combined PI and predictive control. By means of PI structure, objective function was optimized and recursive least squares method was employed for the direct identification of controller parameters.…”

Section: Introductionmentioning

confidence: 99%

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Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas

Liu,

Li,

Wang

et al. 2024

Adaptive Control & Signal

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SummaryThe smelting of non‐ferrous metals produces substantial quantities of sulfur dioxide (SO)‐laden flue gas, which is seriously harmful to environment and humans. To improve the conversion ratio of SO and minimize environmental pollution, controlling converter inlet temperature during acid production has proven to be an efficient approach. However, unsteadiness of smelting procedure leads to frequent changes in the concentration of SO, which affects the catalytic conversion of SO and the production of sulfuric acid. To regulate converter inlet temperature, a proposed method of multi‐model predictive control is introduced. First, working conditions are divided and characterized according to the range of SO concentration. Then, the mathematical model is established for each working condition and the model predictive controller is designed. Finally, an effective switching mechanism is established to realize smooth switching under different working conditions and closed‐loop control of the whole system. Through simulation validation, compared with traditional single‐model predictive controllers and multi‐model PID controllers, the proposed approach demonstrates improved transient performance and steady‐state performance. Simulation outcomes clearly highlight the superiority of the proposed algorithm.

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“…Generally, to improve transition smoothness, controlled object's output strives to align with specified reference trajectory illustrated in Equation (8).…”

Section: Rolling Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas

Liu,

Li,

Wang

et al. 2024

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…According to the above analysis, the three-layer and four-layer temperature control system is a MIMO system, and its discrete nonlinear model is shown in formula (6).…”

Section: Neural Network Modeling Of Converter Inlet Temperaturementioning

confidence: 99%

“…The outcomes demonstrated that the BP neural network's predictive capability outperformed the multiple linear regression model. In Reference 6, the objective function was maximized for a temperature management system by Chen et al using a PI(proportional‐integral) structure and an implicit PI generalized predictive controller. Recursive least squares approach was used to directly identify controller parameters.…”

Section: Introductionmentioning

confidence: 99%

Neural network predictive control of converter inlet temperature based on event‐triggered mechanism in flue gas acid production

Liu,

Li,

Wang

2024

Optim Control Appl Methods

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The process of smelting non‐ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event‐triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short‐term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event‐triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time‐triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden.

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“…Implicit GPC identifies parameters G and f directly, which avoids the recursive solution of the Diophantine equation, so it can decrease the computation time of algorithm. 46 To obtain the optimal control variables in equation ( 6), the solution of matrix DU must know matrix G and the open loop predictive vector f. Using input and output data, implicit GPC can recognize G and f. The P predictive control variables can be derived from the above method…”

Section: Dumentioning

confidence: 99%

Predictive control compensation for networked control system with time-delay

Tian

Wang

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study proposes a novel predictive control compensation method to deal with random time-delay in networked control system. Different from other compensation strategies, this study adopts different compensation strategies for input time-delay and output time-delay to improve the control effect. First, for the input channel time-delay, the corresponding buffer is set by the time-stamp in the packet. Combining the historical output value and the control variable, the fast implicit generalized predictive control algorithm is adopted to design the predictive controller to compensate for the input channel time-delay. Second, for the output channel time-delay, the controller cannot measure it. A control compensator is designed by adding a feedback loop. The output control variable of the predictive controller is adjusted by predicting the error between the actual control signal and the controller output at the historical sampling time, so as to compensate for the time-delay of the output channel. In addition, the stability of the proposed time-delay compensation method is analyzed. Finally, simulation and experimental results show that the proposed method has good control and compensation effect, and improves the output performance of the system. The networked control system with random time-delay is stable. Furthermore, the simulation results also show that this time-delay compensation method needs less computing time and is more suitable for the practical applications.

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Design of an Improved Implicit Generalized Predictive Controller for Temperature Control Systems

Cited by 9 publications

References 35 publications

Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas

Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas

Neural network predictive control of converter inlet temperature based on event‐triggered mechanism in flue gas acid production

Predictive control compensation for networked control system with time-delay

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