Kinematic Calibration is an effective and economical way to improve the accuracy of the six degreeof-freedom (DoF) parallel kinematic machine (PKM), named as Stewart platform, for the large component assembly in aviation or aerospace. The conventional online calibration requires a powerful and complicated control system, whereas the current offline calibration methods are not satisfactory in terms of the compromise between efficiency and accuracy. This paper proposes a semi-online calibration method in which the geometric errors are identified offline and compensated online. The geometric errors are inserted into the inverse kinematic model. Instead of formulating the linear mapping model between geometric errors and the pose error of moving platform, the error model is written as the function of geometric errors with respect to the actuation inputs. Hence, a nonlinear error model is obtained. Without worrying about the identifiability, the error identification equations are converted into an optimization problem and solved by the hybrid genetic algorithm (HGA). In the traditional offline compensation, the identified kinematic parameters are adopted to modify the nominal kinematic model, which is inconvenient when the control system is not transparent to the users. A new control block that calculating the equivalent actuation inputs from the identified errors is added to the control flow. The errors are compensated in an efficient manner. Simulations and experiments are implemented to validate the accuracy, efficiency and convenience of the proposed method. The results indicates that our approach improves position and orientation accuracy of the Stewart platform by 85.1% and 91.0%.

No abstract

No abstract

No abstract

This paper is concerned with the robust distributedH∞filtering problem for nonlinear systems subject to sensor saturations and fractional parameter uncertainties. A sufficient condition is derived for the filtering error system to reach the requiredH∞performance in terms of recursive linear matrix inequality method. An iterative algorithm is then proposed to obtain the filter parameters recursively by solving the corresponding linear matrix inequality. A numerical example is presented to show the effectiveness of the proposed method.