On D-type and P-type ILC designs and anticipatory approach

Wang, Danwei

doi:10.1080/002071700405879

Cited by 116 publications

(82 citation statements)

References 25 publications

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“…It is also similar to some other algorithms given in literature associated with anticipatory ILC (Wang, 1999(Wang, , 2000, and a reference shift algorithm (Cai, 2009). …”

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

confidence: 62%

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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“…It is also similar to some other algorithms given in literature associated with anticipatory ILC (Wang, 1999(Wang, , 2000, and a reference shift algorithm (Cai, 2009). …”

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

confidence: 62%

Robust stability for iterative learning control

Bradley

French

2009

2009 American Control Conference

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“…Iterative learning control (ILC) schemes have been designed and used for robotic arms and other dynamic systems performing repetitive tasks [19][20][21]. Such iterationbased control methods are model-free and have no critical dependence on system dynamics.…”

Section: Control Problem Of Reproducing Animal Locomotionmentioning

confidence: 99%

“…Using the anticipant scheme [12,[21][22][23] as a representative of contraction-type ILC methods, the iterative learning control law is stated in the form ( ) …”

Section: Ilc Scheme With a Memory-clearing Operatormentioning

confidence: 99%

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Wang

Zhou

et al. 2016

International Journal of Advanced Robotic Systems

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This article offers new insights on the learning control approach developed by [Hu et al. IEEE/ASME Trans. Mechatronics, 19(1): 191-200, 2014]. Theoretical insights are further proposed to unveil why the contraction-type iterative learning control (ILC) schemes are suitable and effective in compensating for hysteresis, widely existing in biorobotic locomotion. Under such circumstances, iteration-based second-order dynamics is adopted to describe the biorobotic systems acted upon by one unknown Preisach hysteresis term. The memory clearing operator is mathematically proven to enable feasibility of contractiontype ILC methods, regardless of whether the initial state is accurately set or not. The simulation examples confirm that the developed iteration-based controller combined with a preceded operator effectively reduce tracking errors caused by the hysteresis nonlinearity. Furthermore, the new insights on theoretical feasibility are definitively corroborated in accordance with the previously published experimental results.

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“…The derivative action can be replaced by anticipation in time (Wang, 2000). For discrete-time systems, Amann et al (1996) proposed to shift the error trajectories of the previous run backwards in time by rT s (anticipation), where r is the system relative degree and T s the sampling time.…”

Section: Types Of Ilc Algorithmsmentioning

confidence: 99%

Combined On-Line and Run-to-Run Optimization of Batch Processes with Terminal Constraints

Welz

Srinivasan

Bonvin

2004

IFAC Proceedings Volumes

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This paper describes the optimization of batch processes in the presence of uncertainty and constraints. The optimal solution consists of keeping certain path and terminal constraints active and driving the sensitivities to zero. The case where the terminal constraints have a larger bearing on the cost than the sensitivities is considered, for which a two-time-scale methodology is proposed. The problem of meeting the active terminal constraints is addressed on-line using trajectory tracking, whilst pushing the sensitivities to zero is implemented on a run-to-run basis. The paper also discusses the run-to-run improvement of trajectory tracking via iterative learning control. The proposed methodology is illustrated in simulation on a batch distillation system.

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On D-type and P-type ILC designs and anticipatory approach

Cited by 116 publications

References 25 publications

Robust stability for iterative learning control

Robust stability for iterative learning control

Theoretical Insights on Contraction-Type Iterative Learning Control for Biorobotic Systems with Preisach Hysteresis

Combined On-Line and Run-to-Run Optimization of Batch Processes with Terminal Constraints

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