A Modified Cascade Control Strategy for Tobacco Re-Drying Moisture Control Process With Large Delay-Time

Fan, Zhi‐Ping; Ren, Zhigang; Chen, Angang

doi:10.1109/access.2019.2960192

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…The subsystem (S1) has a fast dynamic (gas flow variation) with a settling time of 2 s while the subsystem (S2) has a slow dynamic with a settling time of 4080 s. It can be inferred that the dynamic of the subsystem (S1) is 2720 times faster than the one of the subsystem (S2). Given these aforementioned properties, a cascade control strategy seems to be more suitable for this kind of process [37][38][39]. It can ensure better performances than traditional single measurement control.…”

Section: Application Of Cascade Technique For the Dryer Temperature C...mentioning

confidence: 99%

Advanced cascade control strategy applied to an indirect hybrid solar-gas dryer: Numerical and experimental investigations

Zoukit

Doubabi

Salhi

et al. 2022

Sustainable Energy Technologies and Assessments

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Section: Application Of Cascade Technique For the Dryer Temperature C...mentioning

confidence: 99%

Advanced cascade control strategy applied to an indirect hybrid solar-gas dryer: Numerical and experimental investigations

Zoukit

Doubabi

Salhi

et al. 2022

Sustainable Energy Technologies and Assessments

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“…Hanul et al propose a novel iterative feedback tuning method for cascaded control, but it lacks a systematic evaluation of the wireless network performance, and the simultaneous correlation between communication and control performances is not elaborated [25]. A modified cascade control strategy on the basis of the internal model control (IMC) principle is proposed by Fan et al for dominating a class of unstable processes with large delay times [26]. Control results are simulated with the assumed delay in seconds level, while no valuable conclusions are provided based on the [27].…”

Section: Introductionmentioning

confidence: 99%

Latency-Aware Control for Wireless Cloud Fog Automation: Framework and Case Study

Lyu¹,

Bengtsson²,

Nilsson³

et al. 2023

Preprint

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<p>The development of wireless communication has indeed promoted cloud fog automation in the industry. It also introduces new issues of reliability and latency for control systems. This study sought to investigate the impacts of the commonly used industrial wireless network on the control performance parameters using a ball-and-beam (BB) time-critical balancing control system. An internal model control-inspired latency-aware wireless control framework (IMC-LA) is presented and employed in the BB system to handle the time delays and instability-creating elements introduced by wireless communication. A preliminary control assessment of the BB system under two new-generation wireless technologies, Wi-Fi 6 and 5G, was delivered. The correlation between network performance and control performance was analyzed statistically compared to the wired Ethernet condition. Test results show a dramatic decrease in position error after utilizing the proposed latency-aware wireless control framework. This study provides practical insights into the potential impacts of industrial wireless networks on control systems with a workable latency-aware wireless control approach.</p>

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“…By introducing a second, inner controller that stabilizes the secondary influence according to the needs of the primary control loop, the overall system can be rendered stable. Examples of cascaded control include valve positioning [1], electrical motor control [2], industrial drying processes [3], steam boiler feedwater control [4] and motion control in robotic systems [5]. Due to the interplay between the different controllers resulting from the nested structure, however, cascaded controllers are cumbersome and challenging to tune in order to obtain the desired system response [6,7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Cascaded Control of Mechatronic Systems through Constrained Residual Reinforcement Learning

2023

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Cascaded control structures are prevalent in industrial systems with many disturbances to obtain stable control but are cumbersome and challenging to tune. In this work, we propose cascaded constrained residual reinforcement learning (RL), an intuitive method that allows to improve the performance of a cascaded control structure while maintaining safe operation at all times. We draw inspiration from the constrained residual RL framework, in which a constrained reinforcement learning agent learns corrective adaptations to a base controller’s output to increase optimality. We first revisit the interplay between the residual agent and the baseline controller and subsequently extend this to the cascaded case. We analyze the differences and challenges this structure brings and derive some principle insights from this into the stability and operation of the cascaded residual architecture. Next, we propose a novel actor structure to enable efficient learning under the cascaded setting. We show that the standard algorithm is suboptimal for application to cascaded control structures and validate our method on a high-fidelity simulator of a dual motor drivetrain, resulting in a performance improvement of 14.7% on average, with only a minor decrease in performance occurring during the training phase. We study the different principles constituting the method and examine and validate their contribution to the algorithm’s performance under the considered cascaded control structure.

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A Modified Cascade Control Strategy for Tobacco Re-Drying Moisture Control Process With Large Delay-Time

Cited by 7 publications

References 21 publications

Advanced cascade control strategy applied to an indirect hybrid solar-gas dryer: Numerical and experimental investigations

Advanced cascade control strategy applied to an indirect hybrid solar-gas dryer: Numerical and experimental investigations

Latency-Aware Control for Wireless Cloud Fog Automation: Framework and Case Study

Optimizing Cascaded Control of Mechatronic Systems through Constrained Residual Reinforcement Learning

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