Integrated Design and Control of a Flying Wing Using Nonsmooth Optimization Techniques

Author(s):  
Yann Denieul ◽  
Joël Bordeneuve ◽  
Daniel Alazard ◽  
Clément Toussaint ◽  
Gilles Taquin
2021 ◽  
Author(s):  
Alessandro Ferraris ◽  
Davide De Cupis ◽  
Henrique de Carvalho Pinheiro ◽  
Alessandro Messana ◽  
Lorenzo Sisca ◽  
...  

1989 ◽  
Vol 42 (4) ◽  
pp. 117-128 ◽  
Author(s):  
S. S. Rao ◽  
P. K. Bhatti

Robotics is a relatively new and evolving technology being applied to manufacturing automation and is fast replacing the special-purpose machines or hard automation as it is often called. Demands for higher productivity, better and uniform quality products, and better working environments are primary reasons for its development. An industrial robot is a multifunctional and computer-controlled mechanical manipulator exhibiting a complex and highly nonlinear behavior. Even though most current robots have anthropomorphic configurations, they have far inferior manipulating abilities compared to humans. A great deal of research effort is presently being directed toward improving their overall performance by using optimal mechanical structures and control strategies. The optimal design of robot manipulators can include kinematic performance characteristics such as workspace, accuracy, repeatability, and redundancy. The static load capacity as well as dynamic criteria such as generalized inertia ellipsoid, dynamic manipulability, and vibratory response have also been considered in the design stages. The optimal control problems typically involve trajectory planning, time-optimal control, energy-optimal control, and mixed-optimal control. The constraints in a robot manipulator design problem usually involve link stresses, actuator torques, elastic deformation of links, and collision avoidance. This paper presents a review of the literature on the issues of optimum design and control of robotic manipulators and also the various optimization techniques currently available for application to robotics.


2001 ◽  
Author(s):  
Jie Xiao ◽  
Bohdan T. Kulakowski

Abstract Vehicle dynamic models include parameters that qualify the dependence of input forces and moments on state and control variables. The accuracy of the model parameter estimates is important for modeling, simulation, and control. In general, the most accurate method for determining values of model parameters is by direct measurement. However, some parameters of vehicle dynamics, such as suspension damping or moments of inertia, are difficult to measure accurately. This study aims at establishing an efficient and accurate parameter estimation method for developing dynamic models for transit buses, such that this method can be easily implemented for simulation and control design purposes. Based on the analysis of robustness, as well as accuracy and efficiency of optimization techniques, a parameter estimation method that integrates Genetic Algorithms and the Maximum Likelihood Estimation is proposed. Choices of output signals and estimation criterion are discussed involving an extensive sensitivity analysis of the predicted output with respect to model parameters. Other experiment-related aspects, such as imperfection of data acquisition, are also considered. Finally, asymptotic Cramer-Rao lower bounds for the covariance of estimated parameters are obtained. Computer simulation results show that the proposed method is superior to gradient-based methods in accuracy, as well as robustness to the initial guesses and measurement uncertainty.


Author(s):  
O. Ghouati ◽  
H. Lenoir ◽  
J. C. Gelin ◽  
M. Baida

Abstract The paper deals with the design and control of forming processes. The finite element code used is based on isoparametric shell elements with three or four nodes, the workpiece being considered as a sheet metal. An optimization technique is used in order to achieve the design or the control of the process by determining the optimal process parameters. The criterion used in that purpose can be based on thickness distribution as well as the respect of the final shape desired for the product. Numerical examples are presented as illustration.


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