Robust Model Predictive Control for Multi-phase Batch Processes with Asynchronous Switching

Li, Hui; Song, Lijun; Jiang, Xueying; Shi, Huiyuan; Su, Chengli; Li, Ping

doi:10.1007/s12555-020-0689-x

Cited by 15 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [49] designed a robust asynchronous switching model predictive controller for multi-stage batch processing with uncertainties, unknown perturbations, and timevarying setpoints. First, an asynchronous switching model with stable and unstable cases was established for the effects of time-varying setpoints and disturbances.…”

Section: Switching Control Compensation Methodsmentioning

confidence: 99%

Review and Comparison of Clearance Control Strategies

et al. 2022

View full text Add to dashboard Cite

The nonlinearity of clearance has a significant influence on the performance of a system while ensuring the reliability of the variable-speed transmission, and hinders the development of the controlled object according to the predetermined trajectory. Aimed at the transmission clearance problem in different systems, this study summarizes the existing literature and provides a reference for the research and compensation of clearance characteristics. First, the influence of clearance on system performance is analyzed and summarized, and it is shown that the existence of clearance causes problems, such as system response delay and limited cycle oscillation. Then, the control strategies for studying clearance are introduced, which are mainly divided into the control strategy based on the clearance model and the non-clearance model control strategy, and these are respectively explained. Finally, some opinions are proposed for the perfection and development of future clearance nonlinear control theory. Ideas for realizing the suppression of the adverse effects of clearances have their characteristics, and in practical applications, the difficulty of implementation and cost control should be comprehensively considered. In the future, to cope with complex and changeable environments, the clearance control strategy will continue to be optimized.

show abstract

Section: Switching Control Compensation Methodsmentioning

confidence: 99%

Review and Comparison of Clearance Control Strategies

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As an advanced process control algorithm, twodimensional model predictive iterative learning control (2D-MPILC) is widely used in batch processes. [12,13] As a kind of learning controller, it can not only use the current batch information, but also absorb information from previous batches. [14,15] Because the 2D-MPILC method can make full use of the information from batch to batch and is more consistent with the characteristics of batch production processes, this kind of algorithm has a strong vitality.…”

Section: Introductionmentioning

confidence: 99%

A novel two‐dimensional PID controller design using two‐dimensional model predictive iterative learning control optimization for batch processes

Bai

et al. 2023

Can J Chem Eng

View full text Add to dashboard Cite

It is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one‐dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two‐dimensional PID controller optimized by two‐dimensional model predictive iterative learning control (2D‐PID‐MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two‐dimensional PID (2D‐PID) controller is developed by combining the advantages of a PID‐type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D‐PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one‐dimension to two‐dimensions. Second, the tuning guidelines of the presented 2D‐PID controller are obtained from the two‐dimensional model predictive control iterative control (2D‐MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D‐MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process.

show abstract

“…Notably, ILC's lack of reliance on the knowledge of system dynamics, coupled with its superior adaptability, renders it an ideal fit for complex control systems. ILC is widely used in robot control systems [1]- [3], medical rehabilitation [4], [5], multi-agent formation [6], [7], batch processes [8], [9], train automatic control [10], [11]and so on.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for High Relative Degree Discrete-Time Systems with Random Initial Shifts

Chen,

Lu,

Yin

2023

IJACSA

View full text Add to dashboard Cite

In this paper, an iterative learning control (ILC) strategy under compression mapping framework is presented for high relative degree discrete-time systems with random initial shifts. Firstly, utilizing the high relative degree of the system and difference term, a control law is designed and a p-order non-homogeneous linear difference equation is established. The appropriate control gain is selected according to the characteristics of solution of the difference equation and the initial shifts, so as to ensure that the high relative degree discrete-time system can reach a steady-state deviation output at a fixed time. Subsequently, a PD-type control law is employed to correct the fixed deviation of the system. Theoretical analysis indicates that this ILC strategy can ensure that the high relative degree systems achieve accurate tracking after the predefined time. Finally, the simulation experiments are conducted on a linear discretetime Multiple-Input Multiple-Output(MIMO) system with relative degree 1 and a Multiple-Input Single-Output(MISO) system with relative degree 2, respectively, and the results verify the effectiveness of the algorithm.

show abstract

Robust Model Predictive Control for Multi-phase Batch Processes with Asynchronous Switching

Cited by 15 publications

References 29 publications

Review and Comparison of Clearance Control Strategies

Review and Comparison of Clearance Control Strategies

A novel two‐dimensional PID controller design using two‐dimensional model predictive iterative learning control optimization for batch processes

Iterative Learning Control for High Relative Degree Discrete-Time Systems with Random Initial Shifts

Contact Info

Product

Resources

About