The assurance of faster transient response rate, higher steady-state tracking accuracy, and global adaptability are crucial for enhancing the efficiency and robustness of manipulators during operation. This article explores a novel fuzzy adaptive predefined-time controller based on global prescribed performance for robotic systems with unknown dynamics and bounded disturbances. First, a predefined time error transformation function (PTETF) is developed and combined with a barrier function based on constant value constraints for control design, which not only significantly simplifies the derivation process of the proposed predefined time prescribed performance control (PTPPC), but also equips it with the ability of global constraints on the trajectory tracking error. Then, we utilize the fuzzy logic system (FLS) with a single-parameter update mechanism to compensate for the dynamic uncertainty of manipulators, thereby reducing computational complexity and cost. In addition, a fixed time disturbance observer (FxTDOB) is introduced to alleviate the effect of nonparametric disturbances on the tracking performance. Further, by integrating the predefined time theory with Lyapunov method to ensure that all state signals of the controlled system converge in a predefined time. Finally, numerical simulations and practical experiments are carried out to demonstrate the effectiveness of the proposed framework.

Endowing robots with the ability to maintain precise interaction force is critical for performing force control tasks in dynamic environments characterized by unknown and varying stiffness and geometry, such as aircraft wing skins and other thin, soft materials. This article presents an adaptive force-tracking admittance controller (AFTAC), ensuring tracking performance through the meticulous design of both the force-based outer loop and the position-based inner loop. First, a finite-time controller based on the adaptive neural networks (NNs) and prescribed performance function (PPF) is proposed to improve the transient convergence speed and steady-state accuracy of robot position tracking. The estimation of the robot dynamics model is based on the idea of single-parameter, which reduces the computational complexity of the entire process. Then, an adaptive force controller combining the admittance model and a disturbance observer (DOB) is designed to compensate for both structural and nonstructural uncertainties of the interaction environment, thereby enhancing force-tracking accuracy. Furthermore, the uniform convergence of the system is proved by Lyapunov's method. Finally, a series of experiments are conducted on the ROKAE platform of a multidegree-of-freedom robot, and the results show the effectiveness of the proposed framework.

Human-robot interaction (HRI) is a crucial component in the field of robotics, and enabling faster response, higher accuracy, as well as smaller human effort, is essential to improve the efficiency, robustness, and applicability of HRI-driven tasks. In this article, we develop a novel neuroadaptive admittance control with human motion intention (HMI) estimation and output error constraint for natural and stable interaction. First, the interaction force information of the robot is utilized to predict the HMI and the stiffness in the admittance model is dynamically updated based on surface electromyography (sEMG) signals of the human upper limb to achieve human-like compliance. Then, based on the designed error transformation mechanism, an innovative prescribed performance control (PPC) is proposed that allows the trajectory error to converge to the given constraint range within a predefined time for any bounded initial conditions, thus enabling the robot to maintain a comprehensive performance of moving in the desired direction as guided by the human. Also, an adaptive neural network (NN) is employed to compensate for the uncertainty of robotics systems to improve the tracking accuracy further. According to the Lyapunov stability analysis criterion, our approach ensures that all states of the closed-loop system remain globally uniformly ultimately bounded. Finally, a series of real-world robot experiments demonstrate the effectiveness of the proposed framework.

Transmission compliance and hysteresis behaviors are associated with harmonic drives. The experimental observation of a harmonic drive showed that these behaviors are significantly degraded with increasing service time, and their accurate modeling is expected to improve the performance of the control system of harmonic drive-based devices such as robot manipulators. In this paper, a new model is proposed to capture transmission compliance and hysteresis and their degradation. The phenomenon is represented through a combination of the nonlinear stiffness component and the micro-sliding friction in the tooth engagement area. The proposed model considered the multi-tooth meshing and the interference effects of the reducer. The parameters of the model were identified using optimization techniques. Numerical simulations and experimental data using a specialized harmonic drive test device were compared to demonstrate the proposed model.

At present, the mean value of the meshing stiffness and the gear backlash is a fixed value in the nonlinear dynamic model. In this study, wear is considered in the model of the gear backlash and time-varying stiffness. With the increase of the operating time, the meshing stiffness decreases and the gear backlash increases. A six degrees-of-freedom nonlinear dynamic model of a new rigid-flexible gear pair is established with time-varying stiffness and time-varying gear backlash. The dynamic behaviors of the gear transmission system are studied through bifurcation diagrams with the operating time as control parameters. Then, the dynamic characteristics of the gear transmission system are analyzed using excitation frequency as control parameters at four operating time points. The bifurcation diagrams, Poincaré maps, fast Fourier transform (FFT) spectra, phase diagrams, and time series are used to investigate the state of motion. The results can provide a reference for the gear transmission system with wear.

Based on mixed lubrication analysis, considering the influence of rough interface contact stiffness, contact damping, and interface friction on the gear transmission system, the relationship between interface contact and the overall performance of the gear transmission system has been established. First, the surface topography is characterized using statistical parameters of rough surfaces, and the contact stiffness and damping for tooth surfaces with different roughnesses are calculated. Subsequently, a six degree of freedom gear tribo-dynamics coupling model is developed. Finally, the established tribo-dynamics model is employed to investigate the relationship between surface roughness and the overall performance of the gear transmission system. This study provides a more intimate connection between the contact interface and the general behavior of the gear transmission system, enabling a better representation of real-world engineering problems. The research findings reveal that contact stiffness and damping decrease with increasing surface roughness. Higher roughness leads to greater gear vibration amplitude. Moreover, elevated surface roughness results in intensified meshing force and more significant energy loss. Surprisingly, when the roughness is appropriate, gears with rough surfaces lose less energy than those with smooth surfaces.