A fast MPC algorithm for reducing computation burden of MIMO

Qi, Rongbin; Mei, Hua; Chen, Chao; Qian, Feng

doi:10.1016/j.cjche.2015.10.008

Cited by 11 publications

(6 citation statements)

References 17 publications

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“…Current trends aim to develop fast-rendering MPC algorithms with lower computational complexity while maintaining performance levels. [249][250][251] The cost function is a vital part of the MPC. A proper design of the cost function guarantees the stability of the optimization.…”

Section: Discussionmentioning

confidence: 99%

Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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The biopharmaceutical industry is rapidly advancing, driven by the need for cutting‐edge technologies to meet the growing demand for life‐saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real‐time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.

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Section: Discussionmentioning

confidence: 99%

Control strategy for biopharmaceutical production by model predictive control

Eslami,

Jungbauer

2024

Biotechnology Progress

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“…Luo et al (2015) hold that the closed-loop MPC is stable and robust in dealing with disturbances, unlike the open-loop controller which, as discused by Wanjiru et al (2016b), can only deal with disturbances that do not largely change the demand pattern. This, however, comes at a higher cost in terms of computation and financial as well as more complexity, since it would require extra components to enable feedback of the height of water in the tanks to take place (Qi et al (2015)). Adoption of either controller depends on the nature of each application.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Sustainable energy-water management for residential houses with optimal integrated grey and rain water recycling

Wanjiru

Xia

2018

Journal of Cleaner Production

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South Africa is a semi-arid developing country facing water and energy insecurity. There are colossal challenges in reliably providing these resources amid growing population, increased urbanization and improved living standards causing increased demand for these resources. Development of new supply centralized systems comes at an exorbitant cost, whereas decentralized systems are touted as an attractive alternative. Grey water recycling and rain water harvesting at buildings level is such an alternative that can provide water for non-potable uses. However, there are technological challenges of optimally operating such systems while ensuring efficient use of associated energy. This paper introduces two control strategies; open loop optimal control and closed-loop model predictive control (MPC) strategies aimed at ensuring safe and reliable operation of the grey water recycling and rain water harvesting system while efficiently using associated energy. From the case study, the proposed system with either control strategy can save the cost of water and waste water by up to 32.3% and 29.5% respectively, while leading to 35.7% in energy cost savings and 31.5% in total operational cost savings in a month. Adoption of these systems would have a huge environmental effect in reducing demand for sewerage services, conservation of water hence reducing demand for potable water as well as increasing the energy efficiency. Furthermore, the system would increase the reliability and security of water supply. Despite the benefits, the system does not pay within its lifetime and therefore, government intervention is required so as to make it economically attractive. High cost of implementation coupled with low potable and waste water tariffs harbour adoption of these systems. Appropriate regulations, policies, incentives and public education are necessary to support such novel technologies in ensuring resource conservation, efficiency and security are achieved.

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“…De Klerk and Craig (2002) studied the closed-loop system identification of MIMO plants controlled by MPC controllers. Qi et al (2015) proposed a fast MPC algorithm to reduce the computational burden of MIMO in chemical processes, whereas Astrom et al (2001) designed MPC complementary PIDs for application to separate MIMO systems. Zad et al (2016) only proposed the theory of controlling the model predictive control (MPC) used in the EHSS and constructed a simulation model.…”

Section: Introductionmentioning

confidence: 99%

Multiple-input and multiple-output force control for automobile component road simulation using model predictive control with proportional–integral–derivative

Yuan

Park

et al. 2021

Journal of Vibration and Control

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When developing an entire vehicle system, testing the structure of the vehicle or each component as a module or individually is necessary to determine the reliability and ensure the endurance of the entire vehicle. Various tests have been conducted to check the durability of the parts. However, the most important part is the verification of the fatigue limit of the load vibration from the road surface when the vehicle is being driven. Verification can be achieved by experimenting while driving on a real road with a prototype vehicle best suited to the actual conditions. However, issues such as problems in time, space, and environmental constraints, inconsistency in driving characteristics of the test driver, and continuous monitoring exist. For testing the load vibration of the road surface in automobile parts in the laboratory, hydraulic servo actuators are used because they provide vibrational loads in multiple directions by configuring them in multiple axes rather than a single axis. In this article, a multiple-input multiple-output model predictive control–proportional–integral–derivative hybrid controller is proposed as the method for optimal control of a multi-axis hydraulic servo actuator used in a random road signal reproduction experiment. Its performance is compared with the simple proportional–integral–derivative controller. A method for obtaining an efficient black box multiple-input multiple-output system model using LabVIEW in a laboratory in the field is also introduced, and the effectiveness of the model predictive control–proportional–integral–derivative hybrid controller is shown by reproducing the actual road load.

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A fast MPC algorithm for reducing computation burden of MIMO

Cited by 11 publications

References 17 publications

Control strategy for biopharmaceutical production by model predictive control

Control strategy for biopharmaceutical production by model predictive control

Sustainable energy-water management for residential houses with optimal integrated grey and rain water recycling

Multiple-input and multiple-output force control for automobile component road simulation using model predictive control with proportional–integral–derivative

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