Kinematics analysis of a four-legged heavy-duty robot with a force–position hybrid control servo actuator in a parallel-executed cylinder system

Abstract

Abstract. In this research, an electrohydraulic servo four-legged heavy-duty (FLHD) robot has been designed and developed. The study proposes an integration layout cylinder design scheme for a non-lightweight hydraulic servo four-legged robot with high loads and torques of hip joint and derives the mathematical element analysis model for a parallel-executed cylinder (PEC) system. The multiple inherent characteristics of the PEC integration system model are explored further. Based on the controllable functional requirements of interconnected joints, and to weaken the influence of internal force coupling, a force–position hybrid control scheme for the PEC is designed, and the force–position signal module design unit is used to solve the force–position hybrid control in reverse. Considering the inherent requirements of the servo-executed cylinder (SEC) force control unit module (CUM), the implementation process of magnetic flux compensation and speed compensation is discussed in detail. The minimum amplitude controller is applied to the SEC force CUM, and the proportional integrated controller has been determined in the SEC position CUM. A compound control strategy proposed in this paper is verified on a parallel hydraulic servo platform. The experimental verification results reveal that the values of position/force attenuation amplitude and lag phase are not greater than 9 % and 18∘, respectively. The feasibility of the interconnected implementation of the hybrid control scheme proposed in this paper is further increased. The conclusions of this research will be useful for application in fields of four-legged heavy-load (FLHL) robot control systems.

The model reference adaptive impedance control for underwater manipulator compliant operation

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“…The numerical method based on the Mehler formula solves the equivalent correlation coefficient and realizes the simulation of non-stationary and non-Gaussian random processes ( Zheng et al, 2022 ). The core work of the non-Gaussian stochastic process simulation method based on memoryless nonlinear translation lies in the transformation of a potential Gaussian stochastic process conforming to the target non-Gaussian stochastic process to a non-Gaussian stochastic process, and the focus is on the transformation of the correlation coefficient between the two ( Wang G. et al, 2021 ; Yun et al, 2022 ). The traditional calculation method of correlation coefficient transformation involves solving complex two-dimensional integral problems ( Li F. et al, 2020 ; Chen et al, 2022b ).…”