Kalman-Filter Based Control and Performance Monitoring Systems
Attila L. Bencsik
Budapest Polytechnic, Bánki Donát Faculty of Mechanical Engineering, H-1081 Budapest, Népszínház utca 8
This paper addresses the problem to design control and performance monitoring systems for robotic manipulators. Basically three control schemes are proposed. The first one is a modification of the computed torque method where the input torques necessary to drive the robotic manipulator are on-line computed as functions of the optimal – unbiased, minimum variance – estimates of the joint coordinates and velocities, and the corrected accelerations. The optimal state estimator based on a stochastic discrete-time linear dynamic model and previous observations is generated the discrete Kalman filter. The idea of the second control system is to use a better reference trajectory for linearization in each working cycle. Two ways are given to perform this relinearization process: the relinearization is performed around the estimated trajectory, and the extended Kalman filter. The third novel control method addresses the problem to design a nonlinear feedback controller in case of uncertain dynamical parameters and other disturbances. Monitoring schemes for real-time detection and isolation of failures in robotic manipulators are outlined in the second part of the paper. The problem of fault detection is formulated as a problem in Hypothesis Testing by regarding the normal operation of the robot manipulator as the null hypothesis.