Virtual disturbance feedback tuning

Eckhard, Diego; Campestrini, Lucíola; Boeira, Emerson Christ

doi:10.1016/j.ifacsc.2018.01.003

Cited by 19 publications

(4 citation statements)

References 13 publications

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“…Interesting results come also from the relay approach, as in refs − . Finally, several works tackle the balancing problem from a data-centric standpoint, via optimization and sometimes not yielding a PID: an example is ref .…”

Section: Literature Review and Motivationmentioning

confidence: 99%

Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection

Leva

Papadopoulos

Seva

et al. 2019

Ind. Eng. Chem. Res.

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In the process control, many PID loops are primarily devoted to rejecting load disturbances, and some of them are crucial for the quality of the overall plant operation. In such a scenario, automatic tuning is highly desired. However, load disturbance rejection calls for strong feedback up to quite high frequencies with respect to the dominant plant dynamics, on which most tuning rules are centered. As such it is difficult for a rule to yield good and, above all, uniform results in the face of all the various process structures it can be confronted with. In this paper, we propose an explicit model-based PID tuning rule specifically targeted at the problem just evidenced. The rule minimizes the magnitude of the nominal disturbance-to-output frequency response, at the same time preventing that magnitude to exhibit a peak or a plateau around its maximum. This characteristic, together with tuning the PID derivative filter, leads to sharp disturbance rejection without incurring in an excessive control sensitivity to high-frequency measurement noise and mitigates the problems caused by heterogeneous process dynamics. The proposed approach is assessed by comparing the rule with selected counterparts, on a literature benchmark with different process structures. A laboratory experiment is finally presented to show that our rule can withstand real-world operating conditions.

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Section: Literature Review and Motivationmentioning

confidence: 99%

Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection

Leva

Papadopoulos

Seva

et al. 2019

Ind. Eng. Chem. Res.

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“…The VRFT method was first introduced in Campi et al (2000) and has been extended to solve important problems as the control of NMP systems (Bazanella et al, 2012) and MIMO systems (Campestrini et al, 2016;Goncalves da Silva et al, 2018) as well as to different control strategies (Lecchini et al, 2001;Eckhard et al, 2018). Besides, it has been widely applied to real systems (Campestrini et al, 2016;Rojas et al, 2010).…”

Section: Virtual Reference Feedback Tuningmentioning

confidence: 99%

The effects of disturbances in non-iterative data-driven controller design

Silva

Campestrini

2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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Non-iterative data-driven control design techniques are very compelling for their ease of implementation and the convenience of only using one set of collected data to do the design. During the period of the data set collection, disturbances that may have occurred to the plant will influence on the design results. On this paper, we use a refrigeration system model to test the effects of different disturbance signals during a closed-loop test to the performance of the controllers obtained through two different non-iterative data-driven controller design methods. The controllers obtained were compared on how far the performance obtained was from the desired response and on the Ratio of Integrated Absolute Variation of Control signal (RIAVU). The results show that the disturbances do interfere in the estimated controllers, changing the closed-loop response. Filtering the collected data before applying the data-driven methodologies improved the results significantly, considering the chosen disturbance signals.

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“…Some recent applications with stability and convergence guarantees for these two techniques are mentioned in [ 4 ] for VSFRT and in [ 5 , 6 ] for MFVIRL. We keep in mind that VSFRT stems from the original popular VRFT approach in control systems [ 7 , 8 , 9 , 10 , 11 , 12 ], whereas MFVIRL is a reinforcement Q-learning approach from the well-known reinforcement learning framework [ 13 , 14 , 15 , 16 , 17 , 18 ], which is common both with artificial intelligence research [ 19 , 20 , 21 , 22 ] and with classical control with a focus on theoretical research [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ] and applications [ 31 , 32 , 33 , 34 , 35 , 36 ].…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

Rădac

2022

Entropy

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A hierarchical learning control framework (HLF) has been validated on two affordable control laboratories: an active temperature control system (ATCS) and an electrical rheostatic braking system (EBS). The proposed HLF is data-driven and model-free, while being applicable on general control tracking tasks which are omnipresent. At the lowermost level, L1, virtual state-feedback control is learned from input–output data, using a recently proposed virtual state-feedback reference tuning (VSFRT) principle. L1 ensures a linear reference model tracking (or matching) and thus, indirect closed-loop control system (CLCS) linearization. On top of L1, an experiment-driven model-free iterative learning control (EDMFILC) is then applied for learning reference input–controlled outputs pairs, coined as primitives. The primitives’ signals at the L2 level encode the CLCS dynamics, which are not explicitly used in the learning phase. Data reusability is applied to derive monotonic and safely guaranteed learning convergence. The learning primitives in the L2 level are finally used in the uppermost and final L3 level, where a decomposition/recomposition operation enables prediction of the optimal reference input assuring optimal tracking of a previously unseen trajectory, without relearning by repetitions, as it was in level L2. Hence, the HLF enables control systems to generalize their tracking behavior to new scenarios by extrapolating their current knowledge base. The proposed HLF framework endows the CLCSs with learning, memorization and generalization features which are specific to intelligent organisms. This may be considered as an advancement towards intelligent, generalizable and adaptive control systems.

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Virtual disturbance feedback tuning

Cited by 19 publications

References 13 publications

Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection

Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection

The effects of disturbances in non-iterative data-driven controller design

Trajectory Tracking within a Hierarchical Primitive-Based Learning Approach

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