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[Objective] The change trend of vegetation cover in the Three-River headwaters region, and its ecological environment were analyzed, in order to provide a scientific basis for the macro layout and implementation of ecological construction projects in the future. [Methods] Based on MODIS-NDVI, DEM, and meteorological data, the spatio-temporal evolution characteristics and future development trend of vegetation cover in the Three-River Headwaters region from 2001 to 2020 were comprehensively analyzed from multiple perspectives by using the maximum-value composite procedure (MVC), trend analysis, the Hurst index, and other methods. The responses of climate change and human activities to vegetation cover were analyzed by combining partial correlation analysis and multiple regression residual analysis. [Results] ① The vegetation cover of the Three-River headwaters region has increased significantly over the past 20 years, with a growth rate of 2.1%/10 yr. Overall, vegetation cover was high in the southeast and low in the northwest, and gradually decreased from southeast to northwest. ② Vegetation cover in the Three-River headwaters region increased over time, with the area of increasing vegetation cover accounting for 74.59%, of the total area, and the area of decreasing cover accounting for 25.41%. The vegetation in northeast and northwest areas increased significantly, and the areas in South Qumalai, North Zaduo, and Southwest Gande decreased. ③ The reverse characteristics of future changes in vegetation cover in the Three-River headwaters region were more obvious than those in the same direction. The area of continuous improvement accounted for 29.22% of the total area, and the area of improvement to degradation accounted for 45.54%. ④ Temperature and precipitation had positive impacts on vegetation cover in the Three-River headwaters region, and precipitation was the main driving factor. ⑤ The impact of human activities on vegetation cover in the Three-River headwaters region increased over time, and the positive impact was mainly located in the northeast Yellow River basin, south of the Tongtian River in Yangtze River basin, and the southeast part of the Lancang River basin. [Conclusion] The Three River headwaters region is an important area for ecological security in China. Vegetation coverage is greatly affected by climate and human activities. It will be necessary to strengthen regional ecological protection and ecological maintenance.

In this study, a fuzzy adaptive impedance control method integrating the backstepping control for the PAM elbow exoskeleton was developed to facilitate robot-assisted rehabilitation tasks. The proposed method uses fuzzy logic to adjust impedance parameters, thereby optimizing user adaptability and reducing interactive torque, which are major limitations of traditional impedance control methods. Furthermore, a repetitive learning algorithm and an adaptive control strategy were incorporated to improve the performance of position accuracy, addressing the time-varying uncertainties and nonlinear disturbances inherent in the exoskeleton. The stability of the proposed controller was tested, and then corresponding simulations and an elbow flexion and extension rehabilitation experiment were performed. The results showed that, with the proposed method, the root mean square of the tracking error was 0.032 rad (i.e., 21.95% less than that of the PID method), and the steady-state interactive torque was 1.917 N·m (i.e., 46.49% less than that of the traditional impedance control). These values exceeded those of the existing methods and supported the potential application of the proposed method for other soft actuators and robots. [ABSTRACT FROM AUTHOR]

Effective adaptive grasping capability is regarded as crucial for climbing robots. However, many dry adhesion legged climbing robots are primarily focused on mobility and load capacity to perform various climbing tasks, often overlooking their operational grasping abilities. Furthermore, flexible grippers designed for adaptive grasping are typically not capable of supporting autonomous climbing or perching motions; they must be rigidly integrated with legged climbing robots, which results in increased weight and reduced load capacity. To address this challenge, a novel dry adhesion climbing robot, MST-G, is proposed, featuring autonomous climbing, perching, and flexible adaptive grasping capabilities. During operation, MST-G is integrated with a legged climbing robot to perform tasks, but can autonomously climb when no task is present, thereby reducing load and ensuring stable motion. Additionally, a robust controller based on prescribed performance is introduced and tested on MST-G, which limits the joint tracking error to a prescribed safety limit, ensuring that motion trajectories can be executed safely and reliably. [ABSTRACT FROM AUTHOR]

Reinforcement learning algorithms usually focus on a specific task, which often performs well only in the training environment. When the task changes, its performance drops significantly, with the algorithm lacking the ability to adapt to new environments and tasks. For the position control of a pneumatic continuum manipulator (PCM), there is a high degree of similarity between tasks, and the training speed of new tasks can be accelerated by utilizing the training experience from other tasks. To increase the adaptability of control policies to new tasks, this paper proposes an adaptive position control algorithm of the PCM based on the Model-Agnostic Meta-Learning (MAML) meta-reinforcement learning algorithm. The MAML meta-reinforcement learning algorithm is used to train the control strategy of PCM, and the information and experience collected during the training process across multiple tasks are used to quickly learn the control policy for new tasks, improving the ability of PCM to quickly adapt to new tasks. The experimental results demonstrate that after training with the MAML meta-reinforcement learning algorithm, the PCM can significantly reduce the training time when faced with new tasks and obtain the control policies suitable for these new tasks. [ABSTRACT FROM AUTHOR]

The parallel driving soft manipulator with multiple extensors and contractile pneumatic artificial muscles (PAMs) is able to operate continuously and has varying stiffness, achieving smooth movements and a fundamental trade-off between flexibility and stiffness. Owing to the hysteresis of PAMs and actuator couplings, the manipulator outputs display coupled hysteresis behaviors with stiffness dependence, causing significant positioning errors. For precise positioning control, this paper takes the lead in proposing a comprehensive model aimed at accurately predicting the coupled hysteresis behavior with the stiffness dependence of the soft manipulator. The model consists of an inherent hysteresis submodule, an actuator coupling submodule, and a stiffness-dependent submodule in series. The asymmetrical hysteresis nonlinearity of the PAM is established by the generalized Prandtl–Ishlinskii model in the inherent hysteresis submodule. The serial actuator coupling submodule is dedicated to modeling the actuator couplings, and the stiffness-dependent submodule is implemented with a fuzzy neural network to characterize the stiffness dependence and other system nonlinearities. In addition, an inverse compensator on the basis of the proposed model is conducted. Experiments demonstrate that this model possesses high accuracy and good generalization, and its compensator is effective in decoupling and mitigating hysteresis coupling of the manipulator. The proposed model and control methods significantly improve the positioning accuracy of the pneumatic soft manipulator. [ABSTRACT FROM AUTHOR]

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals. [ABSTRACT FROM AUTHOR]