A High Precision Motion Control System With Application to Microscale Robotic Deposition

Bristow, Douglas A.; Alleyne, Andrew G.

doi:10.1109/tcst.2006.880189

Cited by 70 publications

(36 citation statements)

References 30 publications

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“…It is noted that low-pass filter may cause a phase shift while as the iterative learning input signals are calculated off-line, the phase shift can be eliminated by filtering the signal back and forth. 30 Many types of the learning functions have been developed for ILC, 22 including the PD type, H ∞ method, and plant inversion. Among them, PD-type learning function is a typical, simple, and tunable ILC learning function, which is adopted in this paper and it is written as…”

Section: A Ilc Term Of the Control Systemmentioning

confidence: 99%

“…As for the time domain analysis, lifted system framework 22,33 is used by stacking the signals in vectors. For N-sample sequences of input and output, Eq.…”

Section: Stability Analysismentioning

confidence: 99%

“…The converged control in the iteration domain is defined as U ∞ = lim i→∞ U i , and then a necessary and sufficient condition for asymptotic convergence is sense of |U ∞ − U i + 1 | < |U ∞ − U i | is presented for the system as 22,27 ρ(M)<1 (19) where ρ(·) is the spectral radius of a n × n matrix defined as ρ(·) = max λ j (·) , j = 1, 2, ..., n, and λ j (·) is the ith eigenvalue of the matrix.…”

Section: Stability Analysismentioning

confidence: 99%

“…However, the plant uncertainty and complexity lead to the difficulty in obtaining an accurate model of the micro-motion stage. Instead of acquiring accurate knowledge of the dynamic model, iterative learning control (ILC) scheme 22,23 takes full advantage of the characteristic of the repetitive motion and extract information from previous iteration trial to modify the control input in the next iteration trail to compensate for the repetitive nonlinearities and uncertainties. Therefore, the ILC scheme can be adopted as the feedforward controller in the stage to effectively improve the tracking performance of individual axis for tracking repetitive trajectory.…”

Section: Introductionmentioning

confidence: 99%

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Contour accuracy improvement of a flexure-based micro-motion stage for tracking repetitive trajectory

Jia

Jiang

et al. 2017

AIP Advances

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Flexure-based micro-motion mechanisms have been widely utilized in modern precision industry due to their inherent merits, while model uncertainty, uncertain nonlinearity, and cross-coupling effect will obviously deteriorate their contour accuracy, especially in the high-speed application. This paper aims at improving the contouring performance of a flexure-based micro-motion stage utilized for tracking repetitive trajectories. The dynamic characteristic of the micro-motion stage is first studied and modeled as a second-order system, which is identified through an open-loop sinusoidal sweeping test. Then the iterative learning control (ILC) scheme is utilized to improve the tracking performance of individual axis of the stage. A nonlinear cross-coupled iterative learning control (CCILC) scheme is proposed to reduce the coupling effect among each axis, and thus improves contour accuracy of the stage. The nonlinear gain function incorporated into the CCILC controller can effectively avoid amplifying the non-recurring disturbances and noises in the iterations, which can further improve the stage’s contour accuracy in high-speed motion. Comparative experiments between traditional PID, ILC, ILC & CCILC, and the proposed ILC & nonlinear CCILC are carried out on the micro-motion stage to track circular and square trajectories. The results demonstrate that the proposed control scheme outperforms other control schemes much in improving the stage’s contour accuracy in high-speed motion. The study in this paper provides a practically effective technique for the flexure-based micro-motion stage in high-speed contouring motion.

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Section: A Ilc Term Of the Control Systemmentioning

confidence: 99%

“…As for the time domain analysis, lifted system framework 22,33 is used by stacking the signals in vectors. For N-sample sequences of input and output, Eq.…”

Section: Stability Analysismentioning

confidence: 99%

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Contour accuracy improvement of a flexure-based micro-motion stage for tracking repetitive trajectory

Jia

Jiang

et al. 2017

AIP Advances

View full text Add to dashboard Cite

show abstract

“…Further, the input signal may be required to be time-invariant within an operation batch for practical application purposes. Apparently, the traditional ILC [14][15][16][17] that utilizes all the intermediate measurements in order to follow a reference trajectory over the finite interval is not appropriate to the above control task.…”

mentioning

confidence: 99%

Constraint data‐driven optimal terminal ILC of end product quality for a class of I/O constrained batch processes

et al. 2017

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In order to deal with the I/O constraints in a practical plant, a soft limiter is often added into the control design procedure directly; however, the performance of the soft limiter based control approach will be degraded greatly due to the use of the soft constraints. This paper proposes a datadriven optimal terminal iterative learning control (constraint-DDOTILC) approach for the end product quality control of batch processes with I/O hard constraints. To deal with nonlinearities, a novel iterative dynamic linearization method without omitting any information of the original plant is introduced such that the derived linearized data-driven model is completely equivalent to the original nonlinear system. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear inequality, a novel constraint-DDOTILC is developed by minimizing an objective function under the derived linear matrix inequality constraint. The optimal learning gain of the constraint-DDOTILC can be updated iteratively according to the input and output measurements to enhance the flexibility for modifications and expansions of the controlled plant. Both theoretical analysis and simulation results confirm the effectiveness of the proposed constraint-DDOTILC.

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Experimentally verified point‐to‐point iterative learning control for highly coupled systems

Freeman

Dinh

2014

Adaptive Control & Signal

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Iterative learning control (ILC) is a well-established approach for precision tracking control of systems, which perform a repeated tracking task defined over a fixed time interval. Despite a rich theoretical framework accompanied by a wide array of application studies, comparatively little attention has been paid to the case of multiple input, multiple output (MIMO) systems. Here, the presence of interacting dynamics often correlates with reduced performance. This article focuses on a general class of linear ILC algorithms and establishes links between interaction dynamics and reduced robustness to modeling uncertainty, and slower convergence. It then shows how these and other limitations can be addressed by relaxing the tracking requirement to include only a subset of time points along the time duration. This is the first analysis to show how so-called 'point-to-point' ILC can address performance limitations associated with highly coupled systems. Theoretical observations are tested using a novel MIMO experimental test facility, which permits both exogenous disturbance injection and a variable level of coupling between input and output pairs. Results compare experimental observations with theoretical predictions over a wide range of interaction levels and with varying levels of injected noise.POINT-TO-POINT ITERATIVE LEARNING CONTROL 303Of the small number of practical studies that have been reported, interaction between dynamics has been assumed negligible and is generally not considered [1,19,20]. With mild interaction, one approach is to treat the coupling as an exogenous disturbance and design multiple SISO ILC loops. This has yielded satisfactory results when applied to control each joint of a six degree-offreedom industrial robot [21]. The approach has also been taken in stroke rehabilitation with ILC used to control the electrical stimulation applied to muscles in the upper limb [22]. However, in the foregoing cases, a robustness filter was required to prevent instability and the tracking accuracy was considerably larger than when controlling a single joint (with the remaining joints locked). In the case of more significant multivariable coupling, this approach may therefore be expected to lead to a further loss of tracking accuracy and the likelihood of instability. A similar approach is multiaxis inversion-based ILC, which assumes a square system matrix and employs a diagonal system inverse that contains only the dominant dynamics for each output [23]. Here, omission of non-dominant dynamics leads to lack of convergence at frequencies at which the omitted dynamics have an overly large norm.Other practical studies have employed MIMO ILC to tackle vibration suppression. For example, in [24], an ILC approach is applied to a six degree of freedom LCD substrate transfer robot to reduce end-effector vibration. The reference is modified off-line to reduce link vibration using redundant actuation degrees of freedom. Another MIMO vibration suppression approach is to develop ILC algorithms that have two separate...

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A High Precision Motion Control System With Application to Microscale Robotic Deposition

Cited by 70 publications

References 30 publications

Contour accuracy improvement of a flexure-based micro-motion stage for tracking repetitive trajectory

Contour accuracy improvement of a flexure-based micro-motion stage for tracking repetitive trajectory

Constraint data‐driven optimal terminal ILC of end product quality for a class of I/O constrained batch processes

Experimentally verified point‐to‐point iterative learning control for highly coupled systems

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