This work deals with the elastostatic identification of industrial manipulators. By reviewing the basics of the physical elastic properties of both links and joints in the framework of the lumped stiffness modeling techniques, the Gramian nature of the stiffness matrices has been found out adequate to do so. Then, a novel optimization method has been developed, which incorporates the Gramian matrix formulation along a non-linear optimization process, acting as an intrinsic constraint for the conservativeness of the elastostatic modeling. Numerical and experimental analyses evince the effectiveness of the proposed method, as the elastostatic models obtained by means of the proposed technique predict more than 93.7% of the compliance deviations of a real industrial robot. The proposed method is simple enough to be jointly applicable to the most recent elastostatic model reduction techniques.

This paper presents the design of non-uniform concentric circular antenna arrays (CCAAs) of isotropic radiators with optimum sidelobe level (SLL) reduction. The biogeography-based optimization (BBO) method is used to determine an optimum set of excitation amplitudes that provide a radiation pattern with optimum SLL reduction with the constraint of a fixed major lobe beamwidth. The BBO method represents a new global evolutionary algorithm for optimization problems in electromagnetics. It is shown that the BBO results provide an SLL reduction that is comparable to that obtained using well-known algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and evolutionary programming (EP). Moreover, BBO results are compared with those obtained using the Matlab function Fmincon which uses a sequential quadratic programming (SQP) method. The comparison shows that the design of non-uniformly excited CCAAs using the SQP method provides a SLL reduction that is better than that obtained using global stochastic optimization methods, indicating that global optimization techniques might not really be needed in this problem.