A Data-Driven Multi-Scale Online Joint Estimation of States and Parameters for Electro-Hydraulic Actuator in Legged Robot

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. INDEX TERMS Data-driven, fast-varying time scale, slow-varying time scale, multi-scale online joint estimation of states and parameters, electro-hydraulic actuator.

Model Predictive Trajectory Tracking Control of Electro-Hydraulic Actuator in Legged Robot With Multi-Scale Online Estimator

“…Based on the analysis of the first-order trajectory sensitivity and parameter change characteristics, this paper has selected fast-varying parameter set θ fast = [β e , F L ] T and slow-varying parameter set [24]. Further, this paper sets the fast varying parameter set θ fast = [β e , F L ] T and the states [x p ,ẋ p , P 1 , P 2 ] T on the same fast scale for estimation, and the slow varying parameter set…”

“…Since the control and estimation algorithm is similar to the double loop form, in order to ensure stability, the MEKF estimator is set to the fast loop with a frequency of 10000 Hz, and the LTV-MPC controller is set to the slow loop with a frequency of 1000 Hz. The estimator's parameter setting and estimation effect have been shown in detail in [24], and this paper will not repeat them.…”

Modeling and Parameter Sensitivity Analysis of Valve-Controlled Helical Hydraulic Rotary Actuator System

“…Ba et al [30][31][32] proposed a first-order sensitivity matrix method and applied it to the dynamic sensitivity analysis of the valvecontrolled actuator system of a legged robot. Huang et al [33] used the first-order trajectory sensitivity method to determine the dominant parameters of the electrohydraulic actuator in a legged robot and then estimated them online in real time.…”

Adaptive Predictive Control for Torque Applying System of High-powered Test Rig

“…Different approaches have been used for modeling these actuators, among which, it can be mentioned to linearized mathematical models, simplified physical models as well as data-driven models. Considering the nonlinear and uncertain dynamics of EHA, the use of data-driven modeling is superior compared to other approach [8], [9] and [10]. Yao et al [11] utilized the dynamic model of EHA in the form of mathematical relations and also used multi-layered neural network (NN) structure as a black-box estimator to overcome the mismatched and matched disturbances besides improving the compensation accuracy.…”