Robust Monotonically Convergent Spatial Iterative Learning Control: Interval Systems Analysis via Discrete Fourier Transform

Altın, Berk; Wang, Zhi; Hoelzle, David J.; Barton, Kira

doi:10.1109/tcst.2018.2868039

Cited by 17 publications

(10 citation statements)

References 35 publications

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“…Many of the models lack the capability to express the effect of deposition path directionality on the L2L dynamics, which is essential for extrusion-based processes. For model and process uncertainties, Altin et al [13] presented an interval model to account for uncertainties that arise in most practical AM applications. Nevertheless, a performance measure to characterize the spatial dynamics is not provided in the current literature.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…This is an important gap for the analysis of the spatial dynamics of AM processes. While variations of the well-known Lyapunov stability are provided for many AM spatial control applications [3], [6], [11], [13], [25], a similar measure for the L2L spatial dynamics to quantify the performance of a printed part has not yet been proposed.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Assumptions A3) and A4) ensure that the spatial dynamics are a result of material deposition in the process. Note that we only require the spatial state to be measurable at the end of the material deposition process within a layer, including any additional layerwise process treatments that may be necessary (see [11], [13], [22], [29]). While layerwise post-processes, such as material curing, may influence the deposited spatial dynamics, we consider these influences as part of the spatial dynamics and do not consider the individual effects of material deposition versus process treatment in this work.…”

Section: Assumptionsmentioning

confidence: 99%

“…The spatial height map as a result of the unit input is denoted by the matrix vec −1 (c m , n i , n j ). Examples of the spatial height map representation are given in [10], [11], [13], [14], [20], and [32]. For the linear layerwise representation, define B k ∈ R n g ×n u and u k ∈ R n u , where n u is the number of input channels (spatial locations with material input).…”

Section: Linear Layerwise Spatially Varying Systemsmentioning

confidence: 99%

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Layer-to-Layer Stability of Linear Layerwise Spatially Varying Systems: Applications in Fused Deposition Modeling

Balta

Tilbury

Barton

2021

IEEE Trans. Contr. Syst. Technol.

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Closed-loop control applications for additive manufacturing (AM) technologies introduce unique challenges in control-oriented modeling and controller development. There have been developments in current literature to model the temporal and spatial dynamics of AM processes. The temporal dynamics of AM processes are often modeled using tools from fluid dynamics and mechanics to represent the material deposition and the motion of the deposition system. Spatial dynamics are often modeled by representing the spreading dynamics of the deposited material in the layerwise spatial domain. An important challenge with the spatial dynamics of AM processes is to understand the performance of the layer-to-layer (L2L) spatial dynamics under spatial disturbances in the system. To this end, this article presents a linear layerwise spatially varying (LLSV) systems modeling framework to represent the L2L spatial height evolution of a generic AM process. The proposed unifying modeling framework can easily represent various AM processes. An L2L stability measure is provided as a performance measure for the spatial dynamic state of an AM process. L2L stability is a novel analysis tool to understand and analyze the spatial characteristics of AM processes. Fundamental L2L stability definitions for a nominal system model and a system model with known Gaussian spatial noise are presented. Theoretical robustness bounds for L2L stability are experimentally compared to a fused deposition modeling (FDM) process. The results show that the theoretical robustness bounds given in this work provide an important foundation for developing novel closed-loop L2L spatial control applications.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: B Literature Reviewmentioning

confidence: 99%

Section: Assumptionsmentioning

confidence: 99%

Section: Linear Layerwise Spatially Varying Systemsmentioning

confidence: 99%

See 2 more Smart Citations

Layer-to-Layer Stability of Linear Layerwise Spatially Varying Systems: Applications in Fused Deposition Modeling

Balta

Tilbury

Barton

2021

IEEE Trans. Contr. Syst. Technol.

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“…According to (10), the optimal control law that makes the objective function take the minimum value is…”

Section: Generalized Predictive Iterative Learning Control Schemementioning

confidence: 99%

Novel Generalized Predictive Iterative Learning Speed Controller for Ultrasonic Motors

Liang

Jingzhuo

2020

IEEE Access

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Aiming at the needs of ultrasonic motor's motion control system, generalized predictive iterative learning control (GPILC) strategy is constructed by integrating iterative learning control and generalized predictive control. By designing 2D objective function with the information of the previous control process, it attempts to integrate the generalized predictive control methods such as multi-step predictive and rolling optimization into the iterative learning control law to improve the iterative learning control effect. An effective design method of the iterative learning control law is proposed. Based on 2D prediction model, the differential optimization of the objective function is carried out to derive the GPILC law. Then, based on the nonlinear Hammerstein model of ultrasonic motor, an inverse compensation method for the motor's nonlinearity is designed to realize the effective compensation for motor's nonlinearity. On this basis, a generalized predictive iterative learning speed controller for ultrasonic motors is designed. The results of simulation and experiment indicate that the proposed control strategy and its design method are effective. The iterative learning process of ultrasonic motor's speed response is gradually convergent, and the control performance is good. INDEX TERMS Generalized predictive control, iterative learning control, ultrasonic motor.

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Construction of Spatial Filtering Matrices for Norm-Optimal ILC in Single Point Incremental Forming

et al. 2021

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Robust Monotonically Convergent Spatial Iterative Learning Control: Interval Systems Analysis via Discrete Fourier Transform

Cited by 17 publications

References 35 publications

Layer-to-Layer Stability of Linear Layerwise Spatially Varying Systems: Applications in Fused Deposition Modeling

Layer-to-Layer Stability of Linear Layerwise Spatially Varying Systems: Applications in Fused Deposition Modeling

Novel Generalized Predictive Iterative Learning Speed Controller for Ultrasonic Motors

Construction of Spatial Filtering Matrices for Norm-Optimal ILC in Single Point Incremental Forming

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