Predictor-Based Self Tuning Control with Constraints

Kaminskas, Vytautas

doi:10.1007/978-0-387-36721-7_20

Cited by 10 publications

(22 citation statements)

References 8 publications

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“…The current estimates of the parameters can be obtained in the identification process from the condition (Kaminskas, 2007) c t :…”

Section: Parameter Identification Methodsmentioning

confidence: 99%

“…A predictor-based control law is synthesized by minimizing control quality criterion Q t (x t +1 ) in an admissible domain x (Kaminskas, 2007) x * t +1 :…”

Section: Generalized Minimum Variance Controlmentioning

confidence: 99%

“…Predictive input-output structure models were proposed and investigated for exploring dependencies between virtual 3D face features and human reaction to them when dynamic virtual 3D face is observed without a virtual reality headset (Kaminskas et al, 2014;Kaminskas and Vidugirienė, 2016;Vaškevičius et al, 2014). Predictive models are necessary in the design of predictor-based control systems (Astrom and Wittenmark, 1997;Clarke, 1994;Kaminskas, 2007). Predictor-based control was applied to the control of human emotion signals, when a dynamic 3D face is observed without a virtual reality headset (Kaminskas et al, 2015;Kaminskas and Ščiglinskas, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Predictor-Based Control of Human Response to a Dynamic 3D Face Using Virtual Reality

Kaminskas¹,

Ščiglinskas²

2018

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This paper introduces how predictor-based control principles are applied to the control of human excitement signal as a response to a 3D face virtual stimuli. A dynamic human 3D face is observed in a virtual reality. We use changing distance-between-eyes in a 3D face as a stimuluscontrol signal. Human responses to the stimuli are observed using EEG-based signal that characterizes excitement. A parameter identification method for predictive and stable model building with the smallest output prediction error is proposed. A predictor-based control law is synthesized by minimizing a generalized minimum variance control criterion in an admissible domain. An admissible domain is composed of control signal boundaries. Relatively high prediction and control quality of excitement signals is demonstrated by modelling results.

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“…The current estimates of the parameters can be obtained in the identification process from the condition (Kaminskas, 2007) c t :…”

Section: Parameter Identification Methodsmentioning

confidence: 99%

“…A predictor-based control law is synthesized by minimizing control quality criterion Q t (x t +1 ) in an admissible domain x (Kaminskas, 2007) x * t +1 :…”

Section: Generalized Minimum Variance Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictor-Based Control of Human Response to a Dynamic 3D Face Using Virtual Reality

Kaminskas¹,

Ščiglinskas²

2018

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“…is one-step-ahead output prediction (Kaminskas, 1982(Kaminskas, , 2007, z is the forward shift operator (zy t = y t +1 ) and δ > 0 is a chosen constant value. Usually in identification practice δ ∈ [0.01 ÷ 0.1] what corresponds to a relative variation of prediction error standard deviation from 1% to 10%.…”

Section: Building Of Mathematical Modelsmentioning

confidence: 99%

Modeling Human Emotions as Reactions to a Dynamical Virtual 3D Face

2014

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Abstract. This paper introduces a comparison of one linear and two nonlinear one-step-ahead predictive models that were used to describe the relationship between human emotional signals (excitement, frustration, and engagement/boredom) and virtual dynamic stimulus (virtual 3D face with changing distance-between-eyes). An input-output model building method is proposed that allows building a stable model with the smallest output prediction error. Validation was performed using the recorded signals of four volunteers. Validation results of the models showed that all three models predict emotional signals in relatively high prediction accuracy.

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“…Predictive models are necessary in the design of predictor-based control systems [5], [6], [7], [15][16][17], [37]. Predictor-based control principles were applied to the control of human emotion signals when a 3D face was observed without a virtual reality headset [25], [27].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of the Control Schemes of Human Response to a Dynamic Virtual 3D Face

Kaminskas

Ščiglinskas

2019

ITC

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This paper introduces the application of predictor-based control principles for the control of human response to a virtual 3D face. A dynamic woman 3D face is observed in virtual reality. We use changing distance-between-eyes in a 3D face as a stimulus – control signal. Human responses to the stimulus are observed using EEG-based excitement signals – output signal. The technique of dynamic systems identification which ensure stability and possible higher gain of the model for building a predictive input-output model of control plant is applied. Three predictor-based control schemes with a minimum variance or a generalized minimum variance control quality and constrained control signal magnitude and change rate are developed. High prediction accuracies and control quality are demonstrated by modelling results.

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Predictor-Based Self Tuning Control with Constraints

Cited by 10 publications

References 8 publications

Predictor-Based Control of Human Response to a Dynamic 3D Face Using Virtual Reality

Predictor-Based Control of Human Response to a Dynamic 3D Face Using Virtual Reality

Modeling Human Emotions as Reactions to a Dynamical Virtual 3D Face

A Comparison of the Control Schemes of Human Response to a Dynamic Virtual 3D Face

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