Motion Control of Elastic Joint Based on Kalman Optimization with Evolutionary Algorithm

Actual industrial ambition is to remove a maximum of sensor to improve reliability and cost. Performances are then decreasing a lot, specially for a system with variable parameters and direct drives. Moreover, a two-mass system representing numerous class of industrial problem can become unstable. Keeping stability, a simple controller and observer tuning approach and a lower time consuming are main goals of this study. A previous calculated state feedback is used as base for two Kalman filters with special a noise matrix. An evolutionary algorithm optimizes observer´s degrees of freedom to keep stability all over the stiffness variation. The results show that the stability and performances are kept on an experimental test bench.