Multidisciplinary co-design optimization of the structure and control systems for large cable shovel considering cross-disciplinary interaction

2020 ◽  
Vol 234 (22) ◽  
pp. 4353-4365
Author(s):  
Xueguan Song ◽  
Tianci Zhang ◽  
Yongliang Yuan ◽  
Xiaobang Wang ◽  
Wei Sun
Keyword(s):  
Energy Consumption ◽  
Design Optimization ◽  
Integrated Design ◽  
Structure Design ◽  
Sequential Design ◽  
Simultaneous Optimization ◽  
Open Pit ◽  
Structure Control ◽  
Cable Shovel ◽  
And Control

Large cable shovel is a complex mechatronic system used for primary production in the open pit mine. For such structure-control highly coupled system, the conventional sequential design strategy (structure design followed by the control optimization in sequence) cannot manage this interaction adequately and explicitly. In addition, the large cable shovel consists of large number of sub-systems and/or disciplines, which also poses challenges to the global optimal design for large cable shovel. To enhance large cable shovel’s performance, an integrated design optimization strategy combining the structure-control simultaneous design (co-design) and the multidisciplinary design optimization is established in this study to perform the global optimization for the large cable shovel. In this proposed multidisciplinary co-design, the point-to-point trajectory planning method is extended to achieve the simultaneous optimization of the structure and control system. Besides the structure and control, the dynamics/vibration and energy consumption are taken into account in this multidisciplinary co-design. The objectives are to minimize the energy consumption per volume of ore and to minimize the excavating time. By comparing the multidisciplinary co-design and the conventional sequential design, it is found that the multidisciplinary co-design can not only make large cable shovel’s structure more compact with relatively small vibration, but also generate more flexible control speeds by making the best of the power motors.

Theoretical Evaluation of the Integrated Structure and Control Design Approach by Optimization Theory

2002 ◽  
Author(s):  
Ke Fu ◽  
James K. Mills
Keyword(s):  
Optimization Problems ◽  
Integrated Design ◽  
Optimization Theory ◽  
Control Design ◽  
Structure Design ◽  
Sequential Design ◽  
Design Approach ◽  
Theoretical Evaluation ◽  
Integrated Design Approach ◽  
And Control

Comparing to the traditional sequential design approach, the integrated structure and control design approach integrates the mechanical structure design and the control system design by formulating the design as an optimization problem. This paper investigates two important questions pertaining to the integrated structure and control design approach: Why and when should the integrated design approach be used rather than the traditional sequential design approach? In this paper, we have formulated both approaches as optimization problems. The benefit of the integrated design approach over the sequential design approach is proved in this paper. The conditions for when the integrated design approach will result in better closed-loop system performances are also given. A simple example is given to illustrate the theoretical results derived. The conclusion is given following this example and the simulation results are shown at the end of the paper.

Integrated design optimization of actuator structural parameters and control voltages for morphing structural shapes

2014 ◽  
Vol 22 (6) ◽  
pp. 1538-1546
Author(s):  
高仁璟 GAO Ren-jing ◽  
张莹 ZHANG Ying ◽  
吴书豪 WU Shu-hao ◽  
刘书田 LIU Shu-tian
Keyword(s):  
Design Optimization ◽  
Structural Parameters ◽  
Integrated Design ◽  
And Control

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Abstract Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for an RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating the importance of the robust approach on the integrated design solutions and performance measures.

scholarly journals Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3118
Author(s):  
Qiushi Bi ◽  
Guoqiang Wang ◽  
Yongpeng Wang ◽  
Zongwei Yao ◽  
Robert Hall
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Trajectory Planning ◽  
Open Pit ◽  
Open Pit Mining ◽  
Second Phase ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Phase Optimization ◽  
Cable Shovel

As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20   s to 10   s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

Robust MDSDO for Co-Design of Stochastic Dynamic Systems

2018 ◽  
Author(s):  
Saeed Azad ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Design Optimization ◽  
System Design ◽  
Design Problem ◽  
Integrated Design ◽  
Problem Formulation ◽  
Robust Design Optimization ◽  
Design Problems ◽  
Embodiment Design ◽  
And Control

Optimization of dynamic engineering systems generally requires problem formulations that account for the coupling between embodiment design and control system design simultaneously. Such formulations are commonly known as combined optimal design and control (co-design) problems, and their application to deterministic systems is well-established in the literature through a variety of methods. However, an issue that has not been addressed in the co-design literature is the impact of the inherent uncertainties within a dynamic system on its integrated design solution. Accounting for these uncertainties transforms the standard, deterministic co-design problem into a stochastic one, thus requiring appropriate stochastic optimization approaches for its solution. This paper serves as the starting point for research on stochastic co-design problems by proposing and solving a novel problem formulation based on robust design optimization (RDO) principles. Specifically, a co-design method known as multidisciplinary dynamic system design optimization (MDSDO) is used as the basis for a RDO problem formulation and implementation. The robust objective and inequality constraints are computed per usual as functions of their first-order-approximated means and variances, whereas analysis-based equality constraints are evaluated deterministically at the means of the random decision variables. The proposed stochastic co-design problem formulation is then implemented for two case studies, with the results indicating a significant impact of the robust approach on the integrated design solutions and performance measures.

Interference mitigation in point to point wireless sensor networks using LSP protocol and time division multiplexing approach

2020 ◽  
Vol 39 (4) ◽  
pp. 5449-5458
Author(s):  
A. Arokiaraj Jovith ◽  
S.V. Kasmir Raja ◽  
A. Razia Sulthana
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Control Parameter ◽  
Interference Mitigation ◽  
Wireless Sensor ◽  
Point To Point ◽  
A Minor ◽  
Two Stages ◽  
Time Division ◽  
And Control

Interference in Wireless Sensor Network (WSN) predominantly affects the performance of the WSN. Energy consumption in WSN is one of the greatest concerns in the current generation. This work presents an approach for interference measurement and interference mitigation in point to point network. The nodes are distributed in the network and interference is measured by grouping the nodes in the region of a specific diameter. Hence this approach is scalable and isextended to large scale WSN. Interference is measured in two stages. In the first stage, interference is overcome by allocating time slots to the node stations in Time Division Multiple Access (TDMA) fashion. The node area is split into larger regions and smaller regions. The time slots are allocated to smaller regions in TDMA fashion. A TDMA based time slot allocation algorithm is proposed in this paper to enable reuse of timeslots with minimal interference between smaller regions. In the second stage, the network density and control parameter is introduced to reduce interference in a minor level within smaller node regions. The algorithm issimulated and the system is tested with varying control parameter. The node-level interference and the energy dissipation at nodes are captured by varying the node density of the network. The results indicate that the proposed approach measures the interference and mitigates with minimal energy consumption at nodes and with less overhead transmission.

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