Mínimos cuadrados recursivos para un manipulador que aprende por demostración

Rubio, José de Jesús; García, Enrique; Aquino, Gustavo; Aguilar-Ibáñez, Carlos; Pacheco, Jaime; Meda-Campaña, J. A.

doi:10.4995/riai.2018.8899

Cited by 5 publications

(4 citation statements)

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“…We thus develop a non-linear variant of partial least squares regression (PLSR) [16], which i) handles correlated 'neighboring' inputs in an appropriate fashion and thereby enjoyed a wide usage in data learning problems in the past where such cases occurred -see, e.g., the field of chemometrics [17] or in the field of control systems [18], [19] -, and ii) is able to resolve non-linearities in the co-variance structure between the input matrix X and the target y = QC q .…”

Section: Non-linear Version Of Partial Least Squaresmentioning

confidence: 99%

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models

Lughofer

Zăvoianu

Pollak

et al. 2019

Journal of Process Control

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T. 2019. Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models. Journal of process control [online], 76, pages 27-45. Available from: https://doi. AbstractIn modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on i)data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi-Sugeno fuzzy systems (termed as PLS-Fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, i.) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and ii.) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (Self-Adaptive Forecast Models with Increased Flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCsis suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic)chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-Fuzzy forecast models (showing mostly lower errors t...

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Section: Non-linear Version Of Partial Least Squaresmentioning

confidence: 99%

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models

Lughofer

Zăvoianu

Pollak

et al. 2019

Journal of Process Control

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show abstract

“…Considering the PD control in [6] and [31], the PD approximation-based controller is given as: Fig. 5 shows the approximate results of the model parameters of robotic system, and Fig.…”

Section: A Case 1: Pd Approximation-based Control (Pd-ac)mentioning

confidence: 99%

“…In fact, due to the interaction between robot and environment, it is difficult to obtain the precise values of p 1 , p 2 and p 3 . Therefore, the estimated values of P is introduced in the approximation-based controller (6). P = p 1p2p3 T wherep 1 ,p 2 andp 3 are the estimated values of p 1 , p 2 and p 3 , respectively.P is given by using the adaptive controller (9).…”

Section: Conclusion and Further Workmentioning

confidence: 99%

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A Hybrid Adaptive Control Strategy for Industrial Robotic Joints

et al. 2019

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This paper presents a hybrid adaptive approximation-based control (HAAC) strategy for a class of uncertain robotic joints' system. The proposed control structure consists of a robust sliding mode controller and an adaptive approximation-based controller. The robust sliding mode controller is designed by using the super-twisting algorithm, which is a particularly effective method to decrease the chattering caused by the traditional sliding mode control (SMC) and compensate the disturbances. Another improvement of the robust sliding mode controller is that the robust control parameters only subject to the upper bound of the derivative of the external disturbances, rather than choosing a relatively large value. Moreover, the designed adaptive approximation-based controller has the following two distinctive features: 1) the control parameters are designed to be adjusted in real time and 2) the prior knowledge of actual robotic model is not required to be known. These features contribute to compensating the uncertainties. The stability of the closed-loop system is proved by using the Lyapunov theory, and the simulation results demonstrate the effectiveness of the proposed control method. Finally, the proposed HAAC could apply in the experiments of industrial robotic joints' system. INDEX TERMS Hybrid adaptive control, robust sliding mode control, approximation-based control, robotic joints system.

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