It is difficult to describe precisely, and thus control satisfactorily, the dynamics of an electro-hydraulic actuator to drive a high thrust liquid launcher engine, whose structural resonant frequency is usually low due to its heavy inertia and complicated mass distribution, let alone one to drive a heavy kerolox engine with high-order dynamics. By transforming classic control block diagrams, a baseline two-mass-two-spring load model and a normalized actuator-engine system model were developed for understanding the basic physics and methodology, where a fourth-order transfer function is used to model the multi-resonance-frequency engine body outside of the rod position loop, another fourth-order transfer function with two pairs of conjugated zeros and poles to represent the composite hydro-mechanical resonance effect in the closed rod position loop. A sixth-order model was thereafter proposed for even higher dynamics. The model parameters were identified and optimized by a full factor search approach. To meet the stringent specification of static and dynamic performances, it was demonstrated that a notch filter network combined with other controllers is needed since the traditional dynamic pressure feedback (DPF) is difficult to handle the high-order dynamics. The approach has been validated by simulation, experiments and successful flights. The models, analysis, data and insights were elaborated.

It is difficult to describe precisely, and thus control satisfactorily, the dynamics of an electrohydraulic actuator to drive a high thrust liquid launcher engine, whose structural resonant frequency is usually low due to its heavy inertia and its complicated mass distribution. A generalized model is therefore put forward for maximum simplification and sufficient approximation, where a second-order transfer function is used to model the heavy mass-spring nature of the large engine body outside of the rod position loop, another second-order transfer function with two zeros and two poles representing the hydro-mechanical composite resonance effect in the closed rod position loop. A combined control strategy is applied to meet the stringent specification of static and dynamic performances, including a notch filter, a piecewise or nonlinear proportional, integral and differential (PID) controller and a feed-forward compensation. The control algorithm is implemented in digital signal processors with the same software structure but different parameters for different aerospace actuators. Compared to other approaches, this one makes it easier to grasp the system resonance nature, and, most importantly, the traditional dynamic pressure feedback (DPF) is replaced with the convenient digital algorithm, bringing prominent benefits such as a simplified design, reduced hardware cost and inherent higher reliability. The approach has been validated by simulation, experiments and successful flights.

The addition of telescopic joints will enhance the manipulator’s motion dexterity but increase the motion complexity, thereby potentially reducing the efficiency of motion planning. To address the issue, a manipulability priority principle is used to reduce the computational dimension, aiming to enhance computational efficiency. Firstly, an improved Monte Carlo algorithm combined with Weighted Ensemble Learning is proposed to rapidly obtain a well-distributed and information-complete reachable workspace under small-sample conditions. Then, all dexterous points are computed by the manipulability formula and the least squares algorithm is applied to obtain the boundary of the reachable workspace. Based on the boundary, a spatial quadratic surface-based segmentation algorithm is proposed to obtain the dexterous workspace corresponding to all dexterous points. Subsequently, the Telescopic Motion Computation (TMC) method is designed to transform the kinematics problem of a 9-DOF manipulator into a 7-DOF problem by precomputing the telescopic joint lengths, effectively reducing the computational time for inverse kinematics. Finally, simulations are performed to verify the effectiveness, dexterity, and efficiency of the TMC method. The results indicate that applying the TMC method to PInv, DLS, and PSO algorithms enhances the efficiency by 3.43, 3.53, and 3.64 times while maintaining the motion dexterity.