This paper addresses the trajectory tracking problem for constrained high dynamic electrohydraulic actuator in the presence of time-varying parameters, high frequency external load interference, measurement noise and some unmeasurable states. An adaptive robust optimal control scheme is proposed for the electro-hydraulic actuator in legged robot. The framework of our presented scheme is based on a linear time-varying model predictive controller (LTV-MPC) embedded with a multi-scale online estimator (MEKF). With fast- varying and slow- varying time scales, the MEKF part is used not only for measurable states filtering and unmeasurable states estimation, but also for time-varying parameters and external load interference estimation, which will be integrated into the mpc model in real time. The LTV-MPC part is a trajectory tracking controller designed by constrained MPC with an approximate high-precision real-time model and a rapidly solved cost function, which guarantees that the input and output constraints are satisfied during the receding horizon and optimal control process. Finally, with a series of highly dynamic conditions, the comparison experiment results show that the proposed controller has a simple design process, strong adaptive robust performance and trajectory tracking performance, which verifies the effectiveness of the control scheme.

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.

Learning local navigation maneuvers from expert demonstrations is attractive when the desired behavior is difficult to encode with hand-crafted rules, but behavioral cloning (BC) can accumulate errors during closed-loop deployment. This paper proposes a diffusion-assisted progressive imitation learning framework for a point-cloud-to-velocity local motion planner. A state-action temporal diffusion model provides adversarial learning signals for a deterministic planner optimized through a critic-free horizon-level Gaussian proximal policy optimization (PPO) formulation, while diffusion-purified behavioral cloning (DP-BC) and diffusion-augmented behavioral cloning (DBC) are used for continual fine-tuning. The method is evaluated on held-out expert trajectories and ten randomized closed-loop deployment trials per configuration in a CARLA scenario. The full framework yields the lowest observed mean trajectory dynamic time warping (DTW), 0.65 ± 0.10, compared with 1.77 ± 0.23 for BC and 0.84 ± 0.23 for standalone DBC. Within the staged ablation, diffusion adversarial imitation learning (DAIL) establishes the initial reduction in trajectory discrepancy, DP-BC provides only a modest numerical reduction when added to DAIL, and DBC produces the larger incremental improvement; the lowest observed mean DTW occurs in the complete pipeline. These results support improved expert-behavior reproduction within the evaluated scenario.