Practical implementation of a real-time iterative learning position controller

Barton, A D; Lewin, P.L.; Brown, D.

doi:10.1080/002071700405941

Cited by 51 publications

(24 citation statements)

References 3 publications

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“…The ILC system operates in two dimensions, one is for time and the other is for trial number. This complicated 2-D analysis has also been studied by Arimoto et al (5) , Padieu and Su (12) , and Geng et al (13) Some practical implementations of ILC controllers are applied to the position control of mechanical systems (14) , especially for hydraulic and pneumatic systems (15), (16) . In this research, a proportional-valve controlled pneumatic X-Y table system is built for trajectory tracking control experiments.…”

Section: Introductionmentioning

confidence: 93%

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PD-Type Iterative Learning Control for the Trajectory Tracking of a Pneumatic X-Y Table with Disturbances

Chen

Hwang

2006

JSME Int. J., Ser. C

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In this paper, a proportional-valve controlled pneumatic X-Y table system is built to perform position tracking control experiments. The pneumatic system is subjected to external loads and parameter changes during the control. ILC (Iterative Learning Control) controllers are implemented in the experiments to show their ability to reject disturbances. The P and PDtyped updating laws with delay parameters are used respectively for the repetitive trajectory tracking control of X-Y table. Pre-saved control signals for different types of disturbances are also used to compare control performances. Experimental results show that under the disturbances, the PD-typed ILC controller is superior to the P-typed one and can effectively control the system to track the given circular trajectory.

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Section: Introductionmentioning

confidence: 93%

“…In contrast, smaller learning gains will take more iteration to fulfill the error requirement. Adaptive learning gain has been proposed to make the convergence more stable and faster (13), (14) . In this research, the learning gains are chosen as constants which are determined by a series of searching experiments.…”

Section: Pd-type Ilc Controllermentioning

confidence: 99%

PD-Type Iterative Learning Control for the Trajectory Tracking of a Pneumatic X-Y Table with Disturbances

Chen

Hwang

2006

JSME Int. J., Ser. C

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“…This idea also appears in Barton et al (2000), where the time delay is taken to be the system's settling time; Freeman et al (2005c), where the delay is applied to a non-minimum phase system; and Ma et al (1993), where it is used with current iteration feedback (see Section 3.4.6).…”

Section: D-type and P-type Ilc In Discrete-timementioning

confidence: 99%

“…This can be also be used to stabilise a plant before adding an IL controller to minimise instability issues as explained later in Section 3.6.4. Examples of hybrid ILC systems include Tayebi and Islam (2006), where an adaptive ILC controller is implemented together with a PD controller on a robot manipulator; and Barton et al (2000), which combines the plant in feedback with a PID controller and then applies the ILC around the entire closed-loop.…”

Section: Other Assorted Ilc Terminologymentioning

confidence: 99%

“…ILC often suffers from long term instability issues (Huang and Longman, 1996;Longman, 2000;Barton et al, 2000): the error profile appearing to converge for a number of iterations and then suddenly diverging, driving the system unstable. Long term instability is a potential barrier to the use of ILC in many applications as once a controller is implemented it would be ideal to leave it running, confident that it would perform as expected.…”

Section: Long Term Stabilitymentioning

confidence: 99%

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Robust stability for iterative learning control

Bradley

French

2009

2009 American Control Conference

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This thesis examines the notion of the long term robust stability of iterative learning control (ILC) systems engaged in trajectory tracking, using a robust stability theorem based on a biased version of the nonlinear gap metric. This is achieved through two main results:The first concerns the establishment of a nonlinear robust stability theorem, where signals are measured relative to a given trajectory. Although primarily motivated by ILC, the theorem provided is applicable to a wider range of problems. This is due to its development being made independently of any particular signal space, provided the space is furnished with a definition of causality. The theorem's formulation therefore permits its implementation on single-or multi-dimensional problems in a variety of different settings. Necessitated by an ILC constraint concerning reference signals, the trajectory that stability is measured relative to must often lie outside the signal space that is chosen. The robust stability theorem is therefore devised to address signals that lie in extended signal spaces throughout. Additionally, the theorem is applicable to nonlinear systems, and it is shown that the biased gap measure collapses to the standard nonlinear gap measure when the bias is set to zero. It is also shown to collapse to the classical linear gap when restricting the analysis to certain linear systems.The second result applies the robust stability theorem to ILC using a 2D signal space.Initially the subject of ILC is reviewed and some of the problems associated with controllers are described; in particular the issues of long-term stability and the criteria for convergence. ILC algorithms expressed in the 'lifted system' or 'supervector' formulation are discussed and then analysed using the biased robust stability theorem. Results are presented regarding the use of filtering in ILC algorithms to aid robustness, and also the robustness of inverse model-based techniques. The robust stability tool applied to ILC in this thesis is not restricted in its analysis to algorithms which are 'causal' (in an ILC sense); and is based on a general unstructured uncertainty model in contrast to the existing literature, whereby uncertainties are typically constrained to additive, multiplicative or parametric models.

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P-type iterative learning control for systems that contain resonance

Ratcliffe

Hätönen

Lewin

et al. 2005

Int. J. Adapt. Control Signal Process.

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The effect of iterative learning control (ILC) on a plant containing resonance has been experimentally investigated using an industrial gantry robot. The plant is controlled by a PID controller and a P-type learning controller in a parallel configuration. It is demonstrated that the learning controller can cause the amplitude of resonant frequencies to grow at each trial, leading to instability. The reasons for this growth are analysed both theoretically and experimentally. In order to solve the problem with a resonance, a straightforward aliasing technique is developed which is experimentally shown to suppress the increase of resonant amplitudes. The novel aliasing technique is compared with three filtering methods, the aliasing technique giving the best experimental performance. Finally, it is found that additional velocity feedback combined with the aliasing technique can further help to limit the effect of residual resonance. Due to the straightforward implementation of these algorithms, they should be highly applicable to industrial systems.

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Practical implementation of a real-time iterative learning position controller

Cited by 51 publications

References 3 publications

PD-Type Iterative Learning Control for the Trajectory Tracking of a Pneumatic X-Y Table with Disturbances

PD-Type Iterative Learning Control for the Trajectory Tracking of a Pneumatic X-Y Table with Disturbances

Robust stability for iterative learning control

P-type iterative learning control for systems that contain resonance

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