Energy-efficient wing design for flapping wing micro aerial vehicles

Zhonglai Wang,Xiaorong Hu,Yingdong Wu 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.9

Flapping wing micro aerial vehicles (FWMAVs) have attracted more attention during the development of the robotic systems field. The size of the flapping wing plays an important role in the lift force and torque generation based on quasi-steady aerodynamic model. Therefore, it is necessary to study energy-efficient design methods for wings to provide sufficient lift force and torque with minimal energy consumption for hovering flight. In this paper, the sensitive parameters for the lift force and power consumption were first selected based on design of experiment (DOE) and the parameter of the distributed wing stiffness was determined based on experimental data. Design optimization models for three different cases were then built by considering the lift force as one constraint and the energy consumption as the objective function. The combination of subset simulation and the gradient-based optimization was finally used for solving design optimization models, and the corresponding sensitivity analysis was provided.

Reliability-based design optimization for box-booms considering strength degradation and random total working time

Zhonglai Wang,Hong-Zhong Huang,Xiao-Ling Zhang,Yanfeng Li 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.7

Box-booms are widely used in some cranes. The strength of box-booms would continuously deteriorate with the use of time. In this paper, gamma process is employed to describe strength degradation when dealing with the overall stability of the box-boom. Furthermore,the total working time is not deterministic but random because of the random arrivals of tasks and the random working time of every task. By accounting for strength degradation and the random total working time, a time-dependent reliability-based design optimization (RBDO) model for the box-boom is proposed. An engineering case is used to illustrate the proposed model.

Uncertainty estimation of reliability redundancy in complex systems based on the Cross-Entropy method

Gui-Bao Wang,Hong-Zhong Huang,Yu Liu,Xiaoling Zhang,Zhonglai Wang 대한기계학회 2009 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.23 No.10

The article aims to estimate the uncertainty of possible failure events of redundancy systems based on the crossentropy (CE) method. Failure events of subsystems and components always result in the incomplete or complete failure of engineering systems, yet optimal condition monitoring of a complex system is heavily dependent on the accuracy analysis of all the failure events of subsystems and components and their interaction effects. The CE method is a versatile tool for estimating probabilities of rare events in complex systems with the least bias beyond conditional constraints. In this paper we introduce the CE method for analyzing the system reliability with the highest uncertainty among all possibilities satisfying supplied moment constraints, and developed numerical CE algorithms capable of estimating the uncertainty of failure modes in an M-dimensional redundancy system domain with moment constraints of order up to N. A general computational framework of event estimation and condition monitoring of redundancy systems is illustrated in which the Monte Carlo simulations and CE optimization algorithms are combined. Numerical results indicate potential improvements in the measure of the uncertainty of redundancy systems that would lead to the best-fit analysis of all the complete or incomplete failure events.

Mean-value first-order saddlepoint approximation based collaborative optimization for multidisciplinary problems under aleatory uncertainty

Debiao Meng,Hong-Zhong Huang,Zhonglai Wang,Ning-Cong Xiao,Xiao-Ling Zhang 대한기계학회 2014 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.28 No.10

Reliability-based multidisciplinary design optimization (RBMDO) has received increasing attention in engineering design for achievinghigh reliability and safety in complex and coupling systems (e.g., multidisciplinary systems). Mean-value first-order saddlepoint approximation(MVFOSA) is introduced in this paper and is combined with the collaborative optimization (CO) method for reliabilityanalysis under aleatory uncertainty in RBMDO. Similar to the mean-value first-order second moment (MVFOSM) method, MVFOSAapproximated the performance function with the first-order Taylor expansion at the mean values of random variables. MVFOSA usessaddlepoint approximation rather than the first two moments of the random variables to estimate the probability density and cumulativedistribution functions. MVFOSA-based CO (MVFOSA-CO) is also formulated and proposed. Two examples are provided to show theaccuracy and efficiency of the MVFOSA-CO method.

An efficient approach to reliability-based design optimization within the enhanced sequential optimization and reliability assessment framework

Hong-Zhong Huang,Xudong Zhang,Debiao Meng,Zhonglai Wang,Yu Liu 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.6

Reliability based design optimization (RBDO) has been widely implemented in engineering practices for high safety and reliability. It is an important challenge to improve computational efficiency. Sequential optimization and reliability assessment (SORA) has made great efforts to improve computational efficiency by decoupling a RBDO problem into sequential deterministic optimization and reliability analysis as a single-loop method. In this paper, in order to further improve computational efficiency and extend the application of the current SORA method, an enhanced SORA (ESORA) is proposed by considering constant and varying variances of random design variables while keeping the sequential framework. Some mathematical examples and an engineering case are given to illustrate the proposed method and validate the efficiency.

Enhanced sequential optimization and reliability assessment for reliability-based design optimization

Hong-Zhong Huang,Xudong Zhang,Yu Liu,Debiao Meng,Zhonglai Wang 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.7

Reliability-based design optimization (RBDO) has been receiving increasing attention for achieving high safety and reliability in engineering design. Sequential optimization and reliability assessment (SORA), as one of the efficient single-loop methods, decouples an RBDO problem into sequential deterministic optimization and reliability analysis. An enhanced SORA (ESORA) method is proposed with the aim of further improving the computational efficiency for RBDO, considering both cases of constant and varying variances of random design inputs while keeping the single-loop framework. Vehicle side impact example is used to test and compare the efficiency of the proposed method with existing approaches.

Evaluation and decision of products conceptual design schemes based on customer requirements

Hong-Zhong Huang,Yanfeng Li,Wenhai Liu,Yu Liu,Zhonglai Wang 대한기계학회 2011 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.25 No.9

Within the competitive market environment, understanding customer requirements is crucial for all corporations to obtain market share and survive competition. Only the products exactly meeting customer requirements can win in the market place. Therefore, customer requirements play a very important role in the evaluation and decision process of conceptual design schemes of products. In this paper, an evaluation and decision method based on customer requirements is presented. It utilizes the importance of customer requirements, the satisfaction degree of each evaluation metric to the specification, and an evaluation metric which models customer requirements to evaluate the satisfaction degree of each design scheme to specific customer requirements via the proposed BP neural networks. In the evaluation and decision process, fuzzy sets are used to describe the importance of customer requirements, the relationship between customer requirements and evaluation metrics, the satisfaction degree of each scheme to customer requirements, and the crisp set is used to describe the satisfaction degree of each metric to specifications. The effectiveness of the proposed method is demonstrated by an example of front suspension fork design of mountain bikes.

New evaluation methods for conceptual design selection using computational intelligence techniques†

Hong-Zhong Huang,Yu Liu,Yanfeng Li,Lihua Xue,Zhonglai Wang 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.3

The conceptual design selection, which aims at choosing the best or most desirable design scheme among several candidates for the subsequent detailed design stage, oftentimes requires a set of tools to conduct design evaluation. Using computational intelligence techniques, such as fuzzy logic, neural network, genetic algorithm, and physical programming, several design evaluation methods are put forth in this paper to realize the conceptual design selection under different scenarios. Depending on whether an evaluation crite-rion can be quantified or not, the linear physical programming (LPP) model and the RAOGA-based fuzzy neural network (FNN) model can be utilized to evaluate design alternatives in conceptual design stage. Furthermore, on the basis of Vanegas and Labib’s work, a multi-level conceptual design evaluation model based on the new fuzzy weighted average (NFWA) and the fuzzy compromise decision-making method is developed to solve the design evaluation problem consisting of many hierarchical criteria. The effectiveness of the proposed methods is demonstrated via several illustrative examples.

Swarm Intelligence Based Model Predictive Control Strategy for Optimal State Control of Discrete Time-varying MIMO Linear Systems

Hao Zheng,Yanwei Zhang,Haider Muhammad Husnain,Pengpeng Zhi,Zhonglai Wang 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.10

It is a challenging task to effectively control multi-input and multi-output (MIMO) discrete time-varying linear systems. This paper proposes a swarm intelligence based model predictive control (MPC) strategy for addressing the challenge. First, a swarm intelligence based iterative dynamic optimal control solver is proposed to avoid the difficulty in solving the algebraic Riccati equation of finite-horizon optimal state control problem. Then, a swarm intelligence based online optimal controller is designed based on the MPC strategy, which can extend the optimal control problem from the finite-horizon to the infinite-horizon. Finally, the feedback structure of the online optimal state control system for MIMO discrete time-varying linear systems is constructed. A real-time simulation and a practical control experiment of a first order inverted pendulum system are employed to elborate the proposed method. The results show that the proposed method has the high efficiency, high accuracy, and anti-interference capability.