Search results

1 – 4 of 4
To view the access options for this content please click here
Article

Youshuang Ding, Xi Xiao, Xuanrui Huang and Jiexiang Sun

This paper aims to propose a novel system identification and resonance suppression strategy for motor-driven system with high-order flexible manipulator.

Abstract

Purpose

This paper aims to propose a novel system identification and resonance suppression strategy for motor-driven system with high-order flexible manipulator.

Design/methodology/approach

In this paper, first, a unified mathematical model is proposed to describe both the flexible joints and the flexible link system. Then to suppress the resonance brought by the system flexibility, a model based high-order notch filter controller is proposed. To get the true value of the parameters of the high-order flexible manipulator system, a fuzzy-Kalman filter-based two-step system identification algorithm is proposed.

Findings

Compared to the traditional system identification algorithm, the proposed two-step system identification algorithm can accurately identify the unknown parameters of the high order flexible manipulator system with high dynamic response. The performance of the two-step system identification algorithm and the model-based high-order notch filter is verified via simulation and experimental results.

Originality/value

The proposed system identification method can identify the system parameters with both high accuracy and high dynamic response. With the proposed system identification and model-based controller, the positioning accuracy of the flexible manipulator can be greatly improved.

Details

Industrial Robot: the international journal of robotics research and application, vol. 46 no. 5
Type: Research Article
ISSN: 0143-991X

Keywords

To view the access options for this content please click here
Article

Qi Zhou, Xinyu Shao, Ping Jiang, Tingli Xie, Jiexiang Hu, Leshi Shu, Longchao Cao and Zhongmei Gao

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might…

Abstract

Purpose

Engineering system design and optimization problems are usually multi-objective and constrained and have uncertainties in the inputs. These uncertainties might significantly degrade the overall performance of engineering systems and change the feasibility of the obtained solutions. This paper aims to propose a multi-objective robust optimization approach based on Kriging metamodel (K-MORO) to obtain the robust Pareto set under the interval uncertainty.

Design/methodology/approach

In K-MORO, the nested optimization structure is reduced into a single loop optimization structure to ease the computational burden. Considering the interpolation uncertainty from the Kriging metamodel may affect the robustness of the Pareto optima, an objective switching and sequential updating strategy is introduced in K-MORO to determine (1) whether the robust analysis or the Kriging metamodel should be used to evaluate the robustness of design alternatives, and (2) which design alternatives are selected to improve the prediction accuracy of the Kriging metamodel during the robust optimization process.

Findings

Five numerical and engineering cases are used to demonstrate the applicability of the proposed approach. The results illustrate that K-MORO is able to obtain robust Pareto frontier, while significantly reducing computational cost.

Practical implications

The proposed approach exhibits great capability for practical engineering design optimization problems that are multi-objective and constrained and have uncertainties.

Originality/value

A K-MORO approach is proposed, which can obtain the robust Pareto set under the interval uncertainty and ease the computational burden of the robust optimization process.

Details

Engineering Computations, vol. 35 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

To view the access options for this content please click here
Article

Qi Zhou, Ping Jiang, Xinyu Shao, Hui Zhou and Jiexiang Hu

Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under…

Abstract

Purpose

Uncertainty is inevitable in real-world engineering optimization. With an outer-inner optimization structure, most previous robust optimization (RO) approaches under interval uncertainty can become computationally intractable because the inner level must perform robust evaluation for each design alternative delivered from the outer level. This paper aims to propose an on-line Kriging metamodel-assisted variable adjustment robust optimization (OLK-VARO) to ease the computational burden of previous VARO approach.

Design/methodology/approach

In OLK-VARO, Kriging metamodels are constructed for replacing robust evaluations of the design alternative delivered from the outer level, reducing the nested optimization structure of previous VARO approach into a single loop optimization structure. An on-line updating mechanism is introduced in OLK-VARO to exploit the obtained data from previous iterations.

Findings

One nonlinear numerical example and two engineering cases have been used to demonstrate the applicability and efficiency of the proposed OLK-VARO approach. Results illustrate that OLK-VARO is able to obtain comparable robust optimums as to that obtained by previous VARO, while at the same time significantly reducing computational cost.

Practical implications

The proposed approach exhibits great capability for practical engineering design optimization problems under interval uncertainty.

Originality/value

The main contribution of this paper lies in the following: an OLK-VARO approach under interval uncertainty is proposed, which can significantly ease the computational burden of previous VARO approach.

Details

Engineering Computations, vol. 34 no. 2
Type: Research Article
ISSN: 0264-4401

Keywords

To view the access options for this content please click here
Article

Ji Cheng, Ping Jiang, Qi Zhou, Jiexiang Hu, Tao Yu, Leshi Shu and Xinyu Shao

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational…

Abstract

Purpose

Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the computational burden, the adaptive metamodel-based design optimization (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, a lower confidence bounding approach based on the coefficient of variation (CV-LCB) approach, to balance the exploration and exploitation objectively for obtaining a global optimum under limited computational budget.

Design/methodology/approach

In the proposed CV-LCB approach, the coefficient of variation (CV) of predicted values is introduced to indicate the degree of dispersion of objective function values, while the CV of predicting errors is introduced to represent the accuracy of the established metamodel. Then, a weighted formula, which takes the degree of dispersion and the prediction accuracy into consideration, is defined based on the already-acquired CV information to adaptively update the metamodel during the optimization process.

Findings

Ten numerical examples with different degrees of complexity and an AIAA aerodynamic design optimization problem are used to demonstrate the effectiveness of the proposed CV-LCB approach. The comparisons between the proposed approach and four existing approaches regarding the computational efficiency and robustness are made. Results illustrate the merits of the proposed CV-LCB approach in computational efficiency and robustness.

Practical implications

The proposed approach exhibits high efficiency and robustness in engineering design optimization involving computational simulations.

Originality/value

CV-LCB approach can balance the exploration and exploitation objectively.

Details

Engineering Computations, vol. 36 no. 3
Type: Research Article
ISSN: 0264-4401

Keywords

1 – 4 of 4