This paper mainly studies the trajectory tracking control problem of the manipulator of TianGong-2 when capturing floating objects in the experimental cabin. Microgravity environment will lead to the existing control system of space robot can not produce good control effect. Different gravity environments change the design requirements of space robots, and the ground test cannot meet these space requirements. In this paper, a fuzzy control strategy based on a spatial extended state observer(FC-SESO) is proposed to solve these inconsistent problems. First, a spatial extended state observer is designed to estimate the influence of disturbance on the system. In order to estimate the disturbance quickly and accurately, a disturbance model including gust is established. Second, in order to ensure the rapid convergence of the desired trajectory, the fuzzy control law is derived. Based on Lyapunov stability theory, the stability of the control system based on the spatial extended state observer is proven. Finally, the proposed system is simulated and captured on the Tiangong-2 manipulator platform to verify the effectiveness of the control algorithm.

Semi-autonomous teleoperation based on Learning from Demonstration is an effective method of remote operation of space manipulators, especially in the scenarios with limited communication and repeated operation problems. However, the joint trajectories generated by those methods may not be suitable for space manipulator control. In this paper, we present a novel semi-autonomous teleoperation method, which has been used for the TianGong-2 manipulator system. The proposed method can not only reproduce trajectories for the tasks according to the current environment, but also generate the smoother and smaller joint control torques. To implement the method, we first collected kinesthetic demonstrations by the space manipulator teaching platform as prior knowledge. Then, based on these kinesthetic demonstrations, we designed the joint control commands with Dynamics Constraint Learning from Demonstration algorithm. We finally evaluated our method with the simulation and on-orbit experiment by locating the dexterous hand to the pre-screwing bolt. Our results show a significant reduction of joint control torque fluctuations and peak-to-peak values, and also can reduce energy consumption.

Based on the prototype Halbach planar magnet array (HPMA), a honeycomb Halbach flexible permanent magnet array (HHFA) is proposed in this paper. HHFA can effectively optimize the magnetic field distribution, increase the magnetic field strength, and reduce the magnetic field distortion. The magnetic flux density distribution for HHFA was obtained through a numerical analytical approach. The simulation and comparative analysis show that the intensity amplitude of the magnetic field generated by HHFA is 23% higher than that of HPMA, and the periodic distortion rate, amplitude fluctuation of the magnetic field, and harmonic distortion of HHFA are optimized compared with HPMA. The simulation results demonstrate that MLPM employing HHFA exhibits superior electromagnetic thrust coefficient and electromagnetic force stability compared to MLPM utilizing HPMA.

This study proposes a semi-analytic harmonic modeling method that significantly improves the accuracy and efficiency of complex magnetic field modeling by integrating numerical and analytical approaches. Compared to traditional methods such as the equivalent charge method and finite element method, this approach optimizes the distribution of surface and body charges in the magnetic dipole model and introduces a finite and variable permeability model to accommodate material non-uniformity. Through harmonic expansion and analytical optimization, the method more accurately reflects the characteristics of real magnets, providing an efficient and precise solution for complex magnetic field problems, particularly in the design of high-performance magnets such as Halbach arrays. In this study, the effectiveness of the new modeling method is verified through the combination of simulation and experiment: the magnetic field distribution of the new Halbach array is accurately simulated, and the applicability of the model in the description of complex magnetic fields is analyzed. The dynamic response ability of the optimized model is verified by modeling and simulating the variation of the permeability under actual conditions. The distribution of scalar potential energy with permeability was simulated to evaluate the adaptability of the model to the real physical field. Through the comparative analysis of simulation and experimental results, the advantages of the new method in modeling accuracy and efficiency are clearly pointed out, and the effectiveness of the semi-analytic harmonic modeling method and its wide application potential in the design of new magnetic fields are proved. In this study, a semi-analytic harmonic modeling method is proposed by combining numerical and analytical methods, which breaks through the efficiency bottleneck of traditional modeling methods, and achieves the unity of high precision and high efficiency in the magnetic field modeling of the new Halbach array, providing a new solution for the study of complex magnetic field problems.

This paper proposes a measurement method to address the challenges of multi-degree-of-freedom (DOF) measurement and noise suppression in magnetic levitation planar motors. The approach incorporates an adaptive Kalman filter (AKF) and phase-locked loop (PLL) technology into traditional Hall measurement techniques. The harmonic model of the mover’s magnetic field in the moving-magnet maglev planar motor (MPM) is derived, providing a foundation for subsequent optimization design. To tackle the issue of low signal-to-noise ratio (SNR), the optimal window parameters for the windowed Kalman filter are investigated. An AKF is designed to operate under conditions of unknown noise variance. Based on the harmonic model of the magnetic field, PLL phase demodulation technology is employed to suppress various interference factors in the magnetic field signals. Finally, experimental results validate the measurement system’s excellent resolution, linearity, and motion repeatability.

The pursuit of extreme acceleration in next-generation mechatronic systems necessitates a design paradigm shift for actuators like magnetically levitated planar motors (MLPMs). Conventional design methods, which optimize the electromagnetic plant in isolation, often result in motors whose theoretical performance cannot be achieved due to limitations of the drive electronics. This article introduces an MLPM-oriented mechatronic co-design framework, termed square error minimization-multiple parameter normalization (SSM-MPN), that fundamentally links the motor's physical structure to its final system-level performance. The framework synergistically optimizes the relationships among the Halbach magnet array, coil geometry, and critical drive-system metrics, such as thrust density and thrust-to-weight ratio. By establishing a unified analytical model in which all vital parameters are functions of the magnet array's pole pitch, the SSM-MPN method facilitates holistic, multiobjective optimization. To validate this framework, a complete prototype system was built, featuring the SSM-MPN-optimized motor plant, custom high-current-density flat-wire coils, and high-performance linear amplifiers. The prototype successfully achieved a peak acceleration of 50 m/s$^{2}$ with a 5 kg payload, rigorously validating that our co-design approach creates a superior physical plant capable of fully leveraging the power of advanced drive systems.