An efficient algorithm for the tensor product model transformation

Cui, Jingyu; Zhang, Ke; Ma, Tiehua

doi:10.1007/s12555-015-0204-y

Cited by 22 publications

(23 citation statements)

References 14 publications

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“…STEP1: Under a certain degree of uniformity measure index, according to the good grid point method, the power grid method and some uniform design method is adopted to get a uniform design (UD) table [27]. Since  …”

Section: Efficient Tp Model Transformation For Image Jacobian Matrixmentioning

confidence: 99%

“…are considered, excessive number of LMIs may lead to conservative and impose great difficulties on the computation of feasible solutions, which brings the limitation for practical application of visual servoing control. In this paper, in order to conquer the shortcomings mentioned above, an efficient modified TP model transformation method based on the uniform design [27,28] is implemented to achieve a robust visual servoing control in the presence of bounded uncertain system parameters, which satisfy the operational speed in online applications.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient TP Model Transformation Algorithm for Robust Visual Servoing in the Presence of Uncertain Data

Wang¹,

Bi²,

Hou³

et al. 2020

ACTA POLYTECH HUNG

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This paper presents a robust visual servoing controller based on an efficient TP model transformation method, while taking into account an uncertain image Jacobian matrix where, the camera intrinsic parameters, image features, and depth estimations are affected by unknown random uncertainties with known bounds. The convex vertex decomposition of image Jacobian matrix through uniform design greatly reduce the number of LMIs in the quasi-min-max model predictive control (MPC) scheme, in order to obtain the optimal control inputs of the constrained visual servoing system, while meeting the real-time requirements. Simulation and Experimental results demonstrate the effectiveness of the proposed method.

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Section: Efficient Tp Model Transformation For Image Jacobian Matrixmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient TP Model Transformation Algorithm for Robust Visual Servoing in the Presence of Uncertain Data

Wang¹,

Bi²,

Hou³

et al. 2020

ACTA POLYTECH HUNG

Self Cite

View full text Add to dashboard Cite

show abstract

“…The TP model transformation has been successfully adopted and applied in many technical fields thanks to the continuous improvements in the recent times. New approaches have appeared regarding the computational improvements related to TP model transformation [39,40]. The latest research explored that the LMI based controller design methods are sensitive to the applied convex hulls.…”

Section: Tp Model Transformation and Controlmentioning

confidence: 99%

Tensor Product Model Transformation-based Parallel Distributed Control of Tumor Growth

Kovács

Eigner

2018

APH

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The current work investigates tumor growth control under antiangiogenic targeted molecular therapy by use of Tensor Product (TP) transformation. During the dynamics of the tumor growth we have considered that the tumor volume x 1 (t) is measurable, while due to the lack of information about the second state x 2 (t) (the inhibitor level in the serum), we have developed an appropriate Extended Kalman Filter (EKF) to estimate it. We applied different quasi Linear Parameter Varying (qLPV) models during the design of the EKF and the controller. Tensor Product model transformation method completed with Linear Matrix Inequality based optimization have been applied to design the main controller. The reference signals were generated by trajectory tracking kind control based on Inverse Dynamic Control-Proportional Derivate compensator, applied it on the "simulated" (original) model. We did not consider any state disturbance. However, we have taken into account sensor noise in accordance with the properties of the model. We have found that all of the control goals have been satisfied with the developed control framework: (i) the tumor volume was lower than 1 mm 3 at the end of the therapy; (ii) the developed models have approached each other with good accuracy; (iii) the totally injected inhibitor level was physiologically acceptable.

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“…TPMT has been studied and utilized in the modeling and control of dynamical systems [19,. It promises several added benefits in the prevention of overloaded computation [50][51], alleviation of the limitation to apply TPMT caused by different dimensions of inputs and output variables [34], capacities to be implemented under unknown structure [24], improvement of convex hull manipulation to handle the sensitivity problem in LMI-based control design [46][47][48][49][50][51][52][53] etc. The applications of TPMT in various engineering fields can be found in previous works, such as mechanical engineering [19,[36][37][38], aerospace engineering [29,31,[39][40][41]48], electrical engineering [42][43], robotics [33,44] and filter design [45].…”

Section: Introductionmentioning

confidence: 99%

Biological Pest Control Based on Tensor Product Transformation Method

Boonyaprapasorn¹,

Kuntanapreeda²,

Sangpet³

et al. 2020

ACTA POLYTECH HUNG

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Pest management, based on biological control, has drawn attention from several research groups, due to the exclusion of chemical pesticides, which have debilitating outcomes, both on the environment and human health. Biological pest control policies have been determined using the model-based control approach. In this study, the tensor product model transformation (TPMT) was applied to model the nonlinear dynamic of the biological pest control system. Consequently, the feedback control law representing the biological pest control policy was synthesized based on LMI. Under the designed controller, the pest population was regulated based on the desired level. The simulation of the biological pest control system was presented to confirm the performance of the designed control law. It is evident, that the feedback control method based on TPMT can be employed appropriately, in this application.

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An efficient algorithm for the tensor product model transformation

Cited by 22 publications

References 14 publications

An Efficient TP Model Transformation Algorithm for Robust Visual Servoing in the Presence of Uncertain Data

An Efficient TP Model Transformation Algorithm for Robust Visual Servoing in the Presence of Uncertain Data

Tensor Product Model Transformation-based Parallel Distributed Control of Tumor Growth

Biological Pest Control Based on Tensor Product Transformation Method

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