Analytical and Experimental Modelling for Gain-Scheduling of a Double Scara Robot

Helvoort, J. van; Steinbuch, M Maarten; Lambrechts, Paul; Molengraft, René van de

doi:10.1016/s1474-6670(17)31081-9

Cited by 15 publications

(15 citation statements)

References 2 publications

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“…On one hand, subspace methods for the identification of LPV state space models such as the ones in [10] [13], [14] has the advantage of being much closer to the actual practice of system identification but suffers from the numerical drawbacks associated with the adoption of a fixed (generally not well conditioned) basis for the manipulation of state space models.…”

Section: E Comments and Discussionmentioning

confidence: 99%

Identification for gain-scheduling: a balanced subspace approach

Lovera

Mercère

2007

2007 American Control Conference

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Abstract-The problem of deriving MIMO parameterdependent models for gain-scheduling control design from data generated by local identification experiments is considered and a numerically sound approach is proposed, based on subspace identification ideas combined with the use of suitable properties of balanced state space realisations. Simulation examples are used to demonstrate the performance of the proposed approach.

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Section: E Comments and Discussionmentioning

confidence: 99%

Identification for gain-scheduling: a balanced subspace approach

Lovera

Mercère

2007

2007 American Control Conference

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“…This assumption may not be a reasonable one in many applications, in which it would be desirable to try and derive a parameter-dependent model on the basis of local experiments only, i.e., experiments in which the scheduling variable is held constant and only the control input is excited. Such a viewpoint has been considered in [43,34,23], where numerical procedures for the construction of parametric models for gain scheduling on the basis of local experiments and for the interpolation of local controllers have been proposed.…”

Section: Automatic Derivation Of Lft Modelsmentioning

confidence: 99%

Beyond Simulation: Computer Aided Control System Design using Equation-based Object-oriented Modelling for the Next Decade

Casella¹,

Donida²,

Lovera³

2009

SNE

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After 20 years since their birth, equation-oriented and object-oriented modelling techniques and tools are now mature, as far as solving simulation problems is concerned. Conversely, there is still much to be done in order to provide more direct support for the design of advanced, modelbased control systems, starting from object-oriented plant models. Following a brief review of the current state of the art in this field, the paper presents some proposals for future developments: open model exchange formats, automatic model-order reduction techniques, automatic derivation of simplified transfer functions, automatic derivation of LFT models, automatic generation of inverse models for robotic systems, and support for nonlinear model predictive control.

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“…Rather than using a specific non-linear model, a more generic linear parameter-varying (LPV) model can be considered (see, e.g., [37]). The development of LPV model identification for the experimental modeling of robots is advocated for two main reasons.…”

Section: Introductionmentioning

confidence: 99%

“…By construction, these techniques are restricted to specific systems where the components of the scheduling variables κ κ ∈ ⊂   n κ are totally controllable and excitable (and not only measurable). On the other hand, other methods are based on a multi-step procedure (see, e.g., [37,12]) where 1. a finite set of scheduling variable values {κ i }, i = {1, ···, N op }, is handled, 2. local experiments (corresponding to almost constant scheduling variable values) are carried out for each κ i , i = {1, ···, N op } 3. local LTI models are estimated from the sets of local I/O measurements, for each κ i , 4. a global κ-dependent model is built from the interpolation of the local LTI models.…”

Section: Introductionmentioning

confidence: 99%

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Combining Analytic and Experimental Information for Linear Parameter-Varying Model Identification: Application to a Flexible Robotic Manipulator

Vizer¹,

Mercère²,

Prot³

et al. 2014

Period. Polytech. Elec. Eng. Comp. Sci.

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Identifying a linear parameter-varying (LPV) model of a nonlinear system from local experimental data is still a problem which deserves attention. Many difficulties related to the deter- Keywords linear parameter-varying model · analytic modeling · robotic manipulator · non-smooth cost function · numerical optimization IntroductionWhen models of robotic systems are required, a standard solution consists in resorting to a white-box model obtained by combining the laws of physics governing the behavior of the system. Their descriptions are usually based on the EulerLagrange equations and the virtual work principle [9]. Interesting from a theoretical point of view, the exclusive use of the standard laws of physics makes the final model quite complex and requires an accurate knowledge of the manipulator as well as high-level skills in robotics especially when different robot structures are handled. This is all the more true when the user wants to have access to physical parameters of the system which are imperfectly known. In order to circumvent these difficulties, efforts dedicated to robot identification are more and more made in industry [17]. However, a direct identification of a non-linear black-box model is often complicated because• strong non-linearities may vary with the working conditions can appear, • the development of a global non-linear model structure can rely on strong assumptions such as a uniform density of the manipulator segments or the nature of the deformations if any.

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Analytical and Experimental Modelling for Gain-Scheduling of a Double Scara Robot

Cited by 15 publications

References 2 publications

Identification for gain-scheduling: a balanced subspace approach

Identification for gain-scheduling: a balanced subspace approach

Beyond Simulation: Computer Aided Control System Design using Equation-based Object-oriented Modelling for the Next Decade

Combining Analytic and Experimental Information for Linear Parameter-Varying Model Identification: Application to a Flexible Robotic Manipulator

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