Using Laguerre functions to improve the tuning and performance of predictive functional control

Abdullah, Mohammad; Rossiter, J.A.

doi:10.1080/00207179.2019.1589650

Cited by 18 publications

(40 citation statements)

References 26 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A brief review of the key assumptions, notations, and principles of conventional Predictive Functional Control laws together with a constraint handling method as proposed in [12] is described in this section.…”

Section: Background On Predictive Functional Controlmentioning

confidence: 99%

“…Although this simple concept often works and https://doi.org/10.31436/iiumej.v22i1.1538 provides a fast control solution, it can nevertheless be considered obsolete with modern computing facilities and moreover lacks sufficient rigour. Besides, it is easy to formulate scenarios where this approach fails or leads to significant performance degradation [12] and thus improvements are needed. A core aspect of efficient and accurate constraint handling is to ensure that the optimised predictions and the expected closed-loop behaviour are consistent [1,3,[12][13].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, it is easy to formulate scenarios where this approach fails or leads to significant performance degradation [12] and thus improvements are needed. A core aspect of efficient and accurate constraint handling is to ensure that the optimised predictions and the expected closed-loop behaviour are consistent [1,3,[12][13]. When there is inconsistency, ensuring that the model predictions meet constraints may be a poor reflection of whether the resulting closed-loop responses satisfy constraints, leading to either unpredicted constraint violations or the controlled response becomes very conservative in satisfying the constraints.…”

Section: Introductionmentioning

confidence: 99%

“…When there is inconsistency, ensuring that the model predictions meet constraints may be a poor reflection of whether the resulting closed-loop responses satisfy constraints, leading to either unpredicted constraint violations or the controlled response becomes very conservative in satisfying the constraints. In fact, the core weakness of the traditional PFC is its conventional assumption of its future input predictions being constant; in reality, this assumption is often inconsistent with the resulting closed-loop input to a significant degree [12]. Hence, even though PFC can check the expected constraint violations over a long prediction horizon, yet due to the prediction mismatch, the control law will produce a very conservative solution.…”

Section: Introductionmentioning

confidence: 99%

“…In previous work [12], an alternative parameterisation of the d.o.f. had been introduced within the PFC framework in order to improve the predictions and closed-loop behaviour's consistency, thus resulting in more accurate constraint handling and systematic tuning.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Improved Constraint Handling Approach for Predictive Functional Control Using an Implied Closed-Loop Prediction

Abdullah

Rossiter

Ghaffar³

2021

IIUMEJ

Self Cite

View full text Add to dashboard Cite

Predictive Functional Control is a simple alternative to the traditional PID controller which has the capability to handle process constraints more systematically. Nevertheless, the most basic form of PFC has suffered from ill-posed prediction due to its simplicity in formulation and assumption of constant future input dynamics. Although some constraints can be satisfied, nevertheless the performance may be very conservative due to this issue. The main objective of this paper is to improve the constrained performance of a PFC controller with a minimum modification of the existing formulation. Specifically, a novel constraint handling approach for PFC is proposed based on an implied closed-loop prediction. Instead of assuming a constant input as deployed in the conventional open-loop prediction, the implied closed-loop input dynamics are utilised to detect future constraint violations. In addition, a future perturbation is introduced into the prediction structure as an extra degree of freedom for satisfying the constraints. Two simulation results confirm that the proposed approach gives far less conservative constraint handling and thus better control performance compared to the nominal PFC. Furthermore, this novel implementation also alleviates the well-known tuning difficulties and prediction inconsistency issues that are associated with conventional PFC when handling constraints. ABSTRAK: Kawalan Kefungsian Ramalan adalah alternatif mudah kepada kawalan tradisional PID yang mempunyai kekangan keupayaan bagi mengawal proses secara lebih tersusun. Namun, keadaan paling asas pada kesan PFC adalah daripada ramalan tak teraju-rapi yang disebabkan oleh formula ringkas dan anggapan dinamik input yang sama bagi masa depan. Walau kekangan ini dapat diatasi, namun prestasi akan berubah secara konservatif disebabkan oleh isu ini. Objektif utama kajian ini adalah bagi membaiki kekangan prestasi kawalan PFC dengan modifikasi minimum formula yang ada. Secara spesifik, pendekatan nobel kawalan PFC dicadangkan berdasarkan ramalan lingkaran-tertutup. Selain anggapan input tetap seperti yang dilakukan pada ramalan lingkaran-terbuka yang konservatif, dinamik input yang dibuat pada lingkaran-tertutup telah digunakan bagi mengesan kekangan masa depan yang bertentangan. Tambahan, gangguan yang bakal berlaku pada masa depan telah diperkenalkan ke dalam struktur ramalan sebagai tambahan darjah pada kebebasan bagi mengatasi kekangan. Dua dapatan simulasi kajian menyetujui pendekatan yang dicadangkan dan menyebabkan sangat kurang kekangan pengendalian pada sistem konservatif, oleh itu kawalan yang lebih bagus pada prestasi berbanding pada PFC nominal. Selain itu, pendekatan nobel ini juga menghilangkan kesukaran pelarasan yang dikenali ramai dan ramalan isu tidak konsisten yang terdapat pada PFC konvensional apabila mengendali kekangan.

show abstract

Section: Background On Predictive Functional Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Constraint Handling Approach for Predictive Functional Control Using an Implied Closed-Loop Prediction

Abdullah

Rossiter

Ghaffar³

2021

IIUMEJ

Self Cite

View full text Add to dashboard Cite

show abstract

Pole‐zero assignment in model predictive control, using analytical tuning approach

Bagheri

2021

Optim Control Appl Methods

View full text Add to dashboard Cite

This paper proposes a new analytical tuning strategy for Model Predictive Control (MPC). In the proposed approach, MPC tuning problem is restated as a pole-zero placement problem. New analytically tuning equations are given and a deep study on the places of poles and zeros of the closed-loop system is performed. It is known that, appropriate zero placement can improve the robust stability of the closed-loop system, so the proposed tuning strategy can be applicable. In MPC tuning strategies, analytical equations are very interesting and useful. To achieve analytical equations, First Order plus Dead Time models are used in this work. These models with adequate accuracy can describe many industrial processes. With simulation studies, the effectiveness of the proposed pole-zero assignment strategy is indicated.

show abstract

Predictive Functional Control for Unstable First-Order Dynamic Systems

Aftab

Rossiter

Zhang

2020

Lecture Notes in Electrical Engineering

Self Cite

View full text Add to dashboard Cite

Predictive functional control (PFC) has emerged as a popular industrial choice owing to its simplicity and cost-effectiveness. Nevertheless, its efficacy diminishes when dealing with challenging dynamics because of prediction mismatch in such scenarios. This paper presents a proposal for reducing prediction mismatch and thus improving behaviour for simple unstable processes; a two-stage design methodology pre-stabilises predictions via proportional compensation before introducing the PFC component. It is demonstrated that pre-stabilisation reduces the dependency of the closed-loop pole on the coincidence point and also improves robustness to uncertainty. Simulation results verify the improved performance as compared to conventional PFC.

show abstract

Using Laguerre functions to improve the tuning and performance of predictive functional control

Cited by 18 publications

References 26 publications

Improved Constraint Handling Approach for Predictive Functional Control Using an Implied Closed-Loop Prediction

Improved Constraint Handling Approach for Predictive Functional Control Using an Implied Closed-Loop Prediction

Pole‐zero assignment in model predictive control, using analytical tuning approach

Predictive Functional Control for Unstable First-Order Dynamic Systems

Contact Info

Product

Resources

About