2D systems theory applied to learning control systems

Amann, N.; Owens, D.H.; Rogers, Eric

doi:10.1109/cdc.1994.411268

Cited by 15 publications

(16 citation statements)

References 1 publication

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“…Notice that this model is implicit. However, with these boundary conditions it is still causal in terms of the input-output relationship, since the first equation can be written explicitly as (4) into (5), and by taking the boundary conditions into account, the input-output relation of the model thus obtained is found to be consistent with (1). In other words, the latent variable model obtained is a realisation of (1) and its size is 3, which is much smaller than the orders of the realisations shown in Table I.…”

Section: Examplesupporting

confidence: 55%

“…The addition of another latent variable z i, j yields a realisation for the (1, 3)-th order system (1). Following this idea, a new realisation method can be developed for the general (n, m)-th order 2-D system…”

Section: Examplementioning

confidence: 99%

“…Moreover, 2-D latent variable models have been successfully applied to the design of iterative learning control (ILC) schemes [16], [1].…”

Section: Introductionmentioning

confidence: 99%

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On the realisation of 2-D linear systems with implicit latent variable models

Yang

Ntogramatzidis

Cantoni

2007

2007 46th IEEE Conference on Decision and Control

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Abstract-In this paper a new latent variable model is proposed for the realisation of 2-D linear causal systems. For the general (n, m)-th order 2-D system, the latent variable model obtained here is implicit when n = m. Importantly however, even in this case the model is recursively computable. The advantage of this realisation is that its order is nm, which is much smaller than the orders of the existing latent variable realisations such as Fornasini-Marchesini (FM) models and Roesser model. In addition, given a particular 2-D system, an algorithm is developed for further reducing the size of the realisation.

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Section: Examplesupporting

confidence: 55%

Section: Examplementioning

confidence: 99%

See 1 more Smart Citation

On the realisation of 2-D linear systems with implicit latent variable models

Yang

Ntogramatzidis

Cantoni

2007

2007 46th IEEE Conference on Decision and Control

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“…Early designs tended to consider the feedback and feedforward controls separately (Moon et al 1998), such that the overall control performance assessment and optimization are difficult to achieve. Lately, it is proposed to consider the batch process with ILC as a two time dimensional (Amann et al 1994) system (Kaczorek 1985). Then, the control system can be designed, analyzed and optimized under the 2D system infrastructure.…”

Section: Introductionmentioning

confidence: 99%

“…Then, in 1994, Rogers and Owens (1994), Amann et al (1994) applied the 2D system theory to analyze the stability of an ILC system. After that, the 2D approaches in ILC field received more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Two-dimensional generalized predictive control (2D-GPC) scheme for the batch processes with two-dimensional (2D) dynamics

Shi

Yang

Cao

et al. 2015

Multidim Syst Sign Process

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Iterative learning control (ILC) system is essentially a special feedback control system with two-dimensional (2D) dynamics that can be designed and optimized under the framework of 2D system theories. Motivated by this viewpoint, it is proposed in this paper to describe a batch process with 2D dynamics directly using a 2D controlled autoregressive moving average model, and then, design a 2D feedback controller, referred to as two-dimensional generalized predictive control, in the framework of model predictive control. The proposed design method naturally results in an ILC algorithm when the process is assumed as a one dimensional process performing a given task repetitively and guarantees the better control performance along cycle by utilizing the cycle-wise dynamics of the process. The proposed control scheme is the further generalization and extension of the two-dimensional generalized predictive iterative learning control scheme which has been developed in the previous works. It solves the problem in some degree that conventional ILC cannot guarantee the convergence when there are non-repeatable dynamics in the processes and/or in desired trajectories. The effectiveness and the applicability are illustrated by the comparisons of the simulation results and the experimental results on packing pressure control of the injection molding process.

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Distinct types of nitric oxide–producing neurons in the developing and adult mouse claustrum

Guirado

Real

Olmos

et al. 2003

J of Comparative Neurology

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We studied at the light and electron microscopic levels the nitric oxide-producing neurons in the mouse claustrum. Nicotinamide adenine dinucleotide phosphate (NADPH)-diaphorase histochemistry and neuronal nitric oxide synthase (nNOS) immunohistochemical staining were used to reveal putative nitrergic neurons. We also analyzed colocalization of nNOS with the inhibitory neurotransmitter gamma-aminobutyric acid (GABA) as well as the ontogenesis of the nNOS-immunoreactive neurons, providing evidence for different populations of nitrergic neurons in the mouse claustrum. The general staining pattern was similar for the histochemical and the immunohistochemical methods, resulting in neuron and neuropil staining throughout the whole claustrum. We described two populations of nitric oxide-producing neurons in the mouse claustrum on the basis of a different level of nNOS expression. Densely nNOS-stained neurons were mostly GABA immunoreactive, displayed ultrastructural features typically seen in aspiny neurons, and may originate in the subpallium; they were first seen in the claustrum at embryonic stage 17.5 and probably represent local inhibitory interneurons. Densely stained cells were found from rostral to caudal levels throughout the dorsal claustrum and the endopiriform nucleus. Lightly nNOS-stained neurons, on the other hand, were more numerous than densely stained ones, especially in the dorsal claustrum. These claustral lightly stained cells, barely observed in the NADPH-diaphorase reacted sections, were mostly non-GABAergic, and appeared earlier during ontogenesis than densely stained cells (at embryonic stages 15.5-16.5). We suggest that these neurons are probably projection neurons.

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2D systems theory applied to learning control systems

Cited by 15 publications

References 1 publication

On the realisation of 2-D linear systems with implicit latent variable models

On the realisation of 2-D linear systems with implicit latent variable models

Two-dimensional generalized predictive control (2D-GPC) scheme for the batch processes with two-dimensional (2D) dynamics

Distinct types of nitric oxide–producing neurons in the developing and adult mouse claustrum

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