Abstract
In order to satisfy the real-time need of model-based controllers for model parameters and full states feedback, this paper has conducted in-depth research on the states and parameters estimation of electro-hydraulic actuator in legged robot with three problems for time-varying parameters estimation (including system parameters and external load force), non-measurable states estimation and measurable states filtering. The first-order trajectory sensitivity method based on the dynamic model is used to determine the parameter set to be estimated, and the parameter fast and slow characteristics are analyzed in detail to obtain the generalized states and slow-varying parameters. Then, the combined algorithm with a fast-varying time scale (composed of a fusion kalman filter and a fast-varying time scale extended kalman filter) and a slow-varying time scale (composed of a slow-varying time scale extended kalman filter) is innovatively proposed to realize the data-driven multi-scale online joint estimation of states and parameters for the actuator system. Finally, the results of three comparative experiments show that the proposed algorithm has better stability, faster convergence speed and more accurate estimation than the dual extended kalman filter algorithm, and the states and parameters estimated by the proposed algorithm accurately reflect the actual characteristics of actuator. Moreover, the algorithm has strong adaptability and robustness in different actuator hardware environment and strong convergence ability for different initial values of states and parameters.
Highlights
Electro-hydraulic actuators are extensively used in heavyduty electromechanical systems and legged robots, for their high load capacity and large power density ratio [1]–[7]
The contribution of this paper is to innovatively propose a data-driven multi-scale online joint estimation algorithm with a fast-varying time scale (composed of a fusion KF (Kalman filter) and a fast-varying time scale extended kalman filter (EKF) (Extended kalman filter)) and a slow-varying time scale for states and parameters applied to actuator with three problems
THE DATA-DRIVEN MULTI-SCALE ONLINE JOINT ESTIMATION ALGORITHM FOR STATES AND PARAME-TERS Under the condition of limited sensor configuration, aiming at the three problems for time-varying parameters estimation, non-measurable states estimation, and measurable states filtering of electro-hydraulic actuators, based on the fast and slow changing characteristics of states and parameters analyzed in the previous section, this paper innovatively proposes a data-driven multi-scale online joint estimation algorithm with a fast-varying time scale and a slow-varying time scale to realize the real-time online estimation of actuator states and parameters
Summary
Electro-hydraulic actuators are extensively used in heavyduty electromechanical systems and legged robots, for their high load capacity and large power density ratio [1]–[7]. Colorado has proposed a novel on-line closed-loop parameter identification algorithm for second order nonlinear systems, designed a cost function based on the optimization method by using a linear combination of the actual and an estimation system, and used algebraic techniques to estimate the velocity and acceleration signals, avoiding noise processing problems This method converges faster than the online and off-line least squares algorithms, and has strong robustness against disturbance, but without requiring any type of data pre-processing [30], [31]. The contribution of this paper is to innovatively propose a data-driven multi-scale online joint estimation algorithm with a fast-varying time scale (composed of a fusion KF (Kalman filter) and a fast-varying time scale EKF (Extended kalman filter)) and a slow-varying time scale (composed of a slow-varying time scale EKF) for states and parameters applied to actuator with three problems This is the key step and technique to ensure the position accuracy of actuators for model-based controllers. The above forces are combined into one as an external load force, which exhibits high dynamic characteristic
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