Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator Approximation

Saltik, M.B.; Jayawardhana, Bayu; Cherukuri, Ashish

doi:10.1109/cdc51059.2022.9992510

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Within this category, it is possible to optimize the complete feedforward *This work is supported by the NWO research project PGN Mechatronics, project number 17973. signal [7], [8], or to parameterize the feedforward signal as a function of time or the reference and optimize over the parameters [9], [10]. When the system performs a repetitive task, an iterative learning control (ILC) method can be used to minimize the tracking error based on the tracking error of previous repetitions by updating the feedforward input [11], the parameters of an inverse model [12], or both [13].…”

Section: Introductionmentioning

confidence: 99%

Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach

Bolderman¹,

Lazăr²,

Butler³

2023

Preprint

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Feedforward controllers typically rely on accurately identified inverse models of the system dynamics to achieve high reference tracking performance. However, the impact of the (inverse) model identification error on the resulting tracking error is only analyzed a posteriori in experiments. Therefore, in this work, we develop an approach to feedforward control design that aims at minimizing the tracking error a priori. To achieve this, we show that the norm of the tracking error can be upperbounded by the sum of the inversion error and the identification error. This yields a two-step feedforward control design procedure that consists of a feedforward controloriented system identification, and a finite-horizon optimization to compute the feedforward control signal. The nonlinear feedforward control design method is validated using physicsguided neural networks on a nonlinear, nonminimum phase mechatronic example, where it outperforms standard ILC.

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Section: Introductionmentioning

confidence: 99%

Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach

Bolderman¹,

Lazăr²,

Butler³

2023

Preprint

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Batch-to-Batch Adaptive Iterative Learning Control─Explicit Model Predictive Control Two-Tier Framework for the Control of Batch Transesterification Process

Gupta

Kodamana

et al. 2022

ACS Omega

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To harness energy security and reduce carbon emissions, humankind is trying to switch toward renewable energy resources. To this extent, fatty acid methyl esters, also known as biodiesel, are popularly used as a green fuel. Fatty acid methyl esters can be produced by a batch transesterification reaction between vegetable oil and alcohol. Being a batch process, fatty acid methyl esters production is beset with issues such as uncertainties and unsteady state behavior, and therefore, adequate process control measures are necessitated. In this study, we have proposed a novel two-tier framework for the control of the fatty acid methyl esters production process. The proposed approach combines the constrained batch-to-batch iterative learning control technique and explicit model predictive control to obtain the desired concentration of the fatty acid methyl esters. In particular, the batch-to-batch iterative learning control technique is used to generate reactor temperature set-points, which is further utilized to obtain an optimal coolant flow rate by solving a quadratic objective cost function, with the help of explicit model predictive control. Our simulation results indicate that the fatty acid methyl esters concentration trajectory converges to the desired batch trajectory within four batches for uncertainty in activation energy and six batches for uncertainty in both inlet concentration of triglyceride and in activation energy even in the presence of process disturbances. The proposed approach was compared to the heuristic-based approach and constraint iterative learning control approach to showcase its efficacy.

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Data-Driven Feedforward Control Design for Nonlinear Systems: A Control-Oriented System Identification Approach

Bolderman,

Lazar,

Butler

2023

2023 62nd IEEE Conference on Decision and Control (CDC)

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Iterative Learning and Model Predictive Control for Repetitive Nonlinear Systems via Koopman Operator Approximation

Cited by 3 publications

References 21 publications

Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach

Data-driven feedforward control design for nonlinear systems: A control-oriented system identification approach

Batch-to-Batch Adaptive Iterative Learning Control─Explicit Model Predictive Control Two-Tier Framework for the Control of Batch Transesterification Process

Data-Driven Feedforward Control Design for Nonlinear Systems: A Control-Oriented System Identification Approach

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