State Estimation and Joint Actuator Fault Reconstruction for Planar One-Legged Articulated Hopping Robots Using an Extended Kalman Filter Approach

When joint actuator faults of the legged robot systems occur, it is important to estimate the states and unknown faults (inputs) accurately for the emergent operation of the control system to protect the robot per se and the surrounding environment. The traditional extended Kalman filter (EKF) is a routine method for state estimation, however, it requires that all the input data are measured. In this paper, an extended Kalman filter with fault reconstruction, referred to as EKF-FR, is proposed to estimate the states as well as unknown joint actuator faults (inputs) for the planar one-legged articulated hopping robot system, which owns the fundamental dynamic characteristics for advanced legged robots. The major merit of the proposed EKF-FR approach lies in the fact that the unknown faults (inputs) are reconstructed by designing an input-output linearization controller with asymptotical stability based on the nonlinear robot system, hence the proposed approach is with more accuracy in comparison with the ones based on the Jacobian linearized systems, such as the extended Kalman filter with unknown inputs (EKF-UI). Simulation results for two single-fault cases demonstrate the effectiveness of the proposed EKF-FR algorithm.