Fast motion and low vibration are often conflicting requirements in industrial robots. Due to joint flexibility, operating at high accelerations can excite mechanical resonance, leading to vibrations that may accelerate gearbox wear and degrade control performance. Effectively suppressing these vibrations requires accurate modeling, identification, and compensation of joint flexibility across the entire workspace and under various payloads. The conventional two-mass model, which focuses on joint rotational stiffness in the gearbox, fails to capture the global flexibility characteristics, as flexibility also arises from bending in the bearings. This article presents a practical multi-local-mode-based model and the corresponding identification method to globally characterize a manipulator’s joint flexibility. Based on this model, a vibration suppression method is developed to mitigate mechanical resonance, achieving low vibration even at high acceleration. Comparative experiments demonstrate that the proposed approach effectively suppresses vibrations across the workspace and under various payload conditions.

This article investigates the problem of visual tracking of an unknown moving target by a network of robotic manipulators equipped with uncalibrated eye-in-hand cameras. The objective is to ensure that, for each robot, the target's projection is maintained at a specified position on the image plane, despite the uncalibrated camera parameters and uncertain, time-varying feature depths. The target's motion is assumed to be generated by a neutrally stable linear system, whose state and system matrix are not directly accessible to all robots. To address this problem, a distributed control scheme is developed in three steps. First, an adaptive distributed observer is introduced to estimate the motion of the moving target. Second, a novel image-space observer is designed for each robot to estimate the image-space position and to simultaneously provide the estimated image-space velocity, based on which the proposed distributed controller avoids using image-space velocity measurements. Third, by leveraging the linearly parameterized properties of the depth-independent image Jacobian matrix and the depth, adaptive laws are proposed to cope with uncertain parameters in cameras and robots. By using the Lyapunov stability theory, a rigorous analysis is provided to show the stability of the closed-loop system and asymptotic convergence of the image-space tracking errors. The effectiveness of the proposed scheme is illustrated through simulation with a group of three-DOF robotic manipulators.

Digital elevation model (DEM) of difference (DoD) produced by unmanned aerial vehicle (UAV)-borne laser scanning (ULS) data has been one of the important methods for monitoring catchment-scale landscape change processes, while its accuracy has been limited by the lack of understanding for the spatially variable uncertainties from systematic errors and random errors included in the DoD. In this study, the DoD uncertainty derivation (DUD) method was improved by undertaking an exhaustive error analysis, estimation of residual systematic errors, and incorporating different DoD uncertainty elimination strategies, based on multitemporal ULS data acquired from a small catchment of the Chinese Loess Plateau. The adapted method was employed to estimate the soil erosion and deposition of the catchment, while the reliability of the method was verified by the volume of mass movement and the depths of gullies measured through field surveys. Results showed that mean systematic biases were 0.025, 0.008, -0.074, and 0.051 m for multitemporal point clouds, respectively. After coregistration, the corresponding systematic bias were 0.001, 0.008, -0.016, and -0.021 m, respectively. The change results showed a significant relationship with the results of mass movement and gully depths ($R^{2}~\gt 0.8$,$p~\lt 0.01$). The adapted DUD method was able to capture different erosion processes, including gully headcut retreat, gully development, mass movement, and localized deposition, while it also achieved an underestimation of the changes compared to field survey results. In the catchment, the area of human activity contributed the highest percentage of the volumetric changes, followed by the gully slope and gully bottom, and the hillslope normally contributed the lowest. Overall, the adapted DUD method provided a reliable way for estimating geomorphic changes at the catchment scale.

High-resolution topographic data acquired by remote aerial vehicles (RAVs) have facilitated the use of digital elevation model (DEM) and DEM of difference (DoD) methods for studying geomorphic processes in complex terrain. However, insufficient understanding of systematic bias and random errors for DEMs constrained the application. In this study, we comprehensively analyzed the spatial pattern and magnitude of errors (including systematic and random errors) of DEMs derived from RAV-acquired point clouds for a topographically complex area (a subcatchment of Qiaogou in the hilly and gully loess plateau (SC_QG), China). The relationships between random errors and influential factors associated with topography, point cloud density, vegetation, and interpolation algorithms were also evaluated. On this basis, an error source thresholding (EST) method was adapted through incorporating residual systematic errors and including more impacting factors in the fuzzy inference system for random error estimation. The adapted EST (AEST) method was then employed to quantify the DoD uncertainty and geomorphic changes in two small catchments with complex terrain (i.e., SC_QG and a sub-catchment of Telagou (SC_TLG) in the hilly and gully Loess Plateau, China), while the results were verified by the changes measured by terrestrial laser scanning (TLS) and erosion pins, respectively. Results showed that mean value of systematic errors of DEMs were 0.065 and 0.005 m for SC_QG and SC_TLG, while the residual errors were reduced to 0.002 and 0.001 m after co-registration, respectively. Significant statistical relationships (${p} \lt 0.01$) were found between random errors and influential factors. The erosional volume of two study sites detected by the adapted method were −252.29 and −981.07 m3 and the corresponding depositional volume were 30.57 and 1594.32 m3, respectively. The adapted method achieved a comparable pattern and magnitude of volumetric changes with TLS results, which was superior to the original EST method in SC_QG. Besides, our method showed a lower absolute error (0.034 m) compared to the original method (0.087 m) through a comparison with erosion pins measurement in the SC_TLG. Overall, the AEST method provided a reliable tool for geomorphic change detection in areas associated with complex terrain.

The issue of fuzzy-observer-based (FOB) fault-tolerant (FT) steering control for autonomous driving with persistent disturbances via model predictive control approach is studied in this article, where velocity variation, potential faults, nonlinearity, and unmeasurable states are considered simultaneously. Due to the variable longitudinal velocity during cruising on diverse road conditions and various vehicle maneuvers, the Takagi–Sugeno fuzzy method is employed to handle parameter variations in the steering control system. For the sake of obtaining information on fault behavior and unmeasurable system states, this article constructs a fuzzy observer to estimate the fault signal and faulty system states. Improved conditions for designing the observer gains are proposed, such that the estimation error converges to a minimal robust positively invariant set. Subsequently, this article introduces a FOB FT model predictive controller, which is designed through the resolution of a Min-Max optimization issue. In the end, the benefits of the approach developed in this article are validated by utilizing the Carsim/Matlab joint simulation.

Accurate rigid-body dynamics is crucial for serial industrial robot applications such as force control and physical human-robot interaction. Despite decades of research, the precise identification of dynamic parameters—particularly low-magnitude inertia parameters—remains a challenge for serial industrial robots. Researchers usually focus on developing various parameter estimation methods, while optimizing exciting trajectories in similar ways, typically minimizing the condition number of the information matrix. However, such optimization usually fails to ensure sufficient excitation for each parameter, due to non-convex coupling effects. To address this limitation, we propose a fully decoupled rigid-body dynamics identification (FDRDI) method in this article. This approach innovatively eliminates coupling effects by using novel symmetrical exciting trajectories based on reciprocating S-curve (RSC). This innovation enables the independent identification of dynamic parameters associated with joint friction, as well as the gravity and inertia of links and payloads. Comparative experiments show that FDRDI achieves superior identification accuracy, evidenced by reduced joint torque prediction errors and payload parameter estimation errors.

In this paper, the problem of steering control is investigated for vehicle path tracking in the presence of parametric uncertainties and nonlinearities. In practice, the vehicle mass varies due to the number of passengers or amount of payload, while the vehicle velocity also changes during normal cruising, which significantly influences vehicle dynamics. Moreover, the vehicle dynamics are strongly nonlinear caused by the tire/road forces under different road surface conditions. With fuzzy modeling method, the original nonlinear path tracking system with parameter variations is first formulated as a T-S fuzzy model with additive norm-bounded uncertainties, and then an approach to the fuzzy observer-based output feedback steering control for vehicle dynamics is proposed under a fuzzy Lyapunov function framework. By employing matrix inequality convexifying techniques, a sufficient condition is developed in the form of linear matrix inequalities such that the closed-loop path tracking error system is asymptotically stable with a guaranteed H∞level. Finally, the effectiveness of the proposed fuzzy observer-based output feedback controller is demonstrated in Carsim/Matlab joint simulation environment, via which the advantage of a T-S fuzzy observer-based output controller over the closed-loop driver model embedded in Carsim is also shown with parametric uncertainties and nonlinearities.

Trajectory planning approaches including off-line and on-line algorithms are developed to deal with physical constraints in practical systems. However, by now the existing trajectory planning algorithms have to assume that the desired trajectory function to be planned is fully or at least partly known in advance, and it may not be true in some applications. To overcome this limitation, a general framework of on-line planning a desired trajectory under physical constraints whose function is unknown in advance is proposed. The desired trajectory is first on-line interpolated to achieve a mathematical expression, and then planned under the physical constraints by the bound estimator and nonlinear filter. A heuristic critical test curve algorithm is proposed to solve the potential stability issue. A telerobotic system, where the function of slave-desired trajectory is unknown in advance, is selected as a typical case. The experimental results validate the effectiveness of the proposed planning algorithm.

Due to the unavailability of full state variables in many control systems, this paper is concerned with the design of reliable observer-based output feedback controller for a class of network-based Takagi-Sugeno fuzzy systems with actuator failures. In order to better allocate network resources under the case that the sensor nodes are physically distributed, the decentralized event triggering communication scheme is adopted such that each sensor node is capable to determine the transmission of its local measurement information independently. Considering that the implementation of the controller may not be synchronized with the plant trajectories due to asynchronous premise variables with such communication mechanism, a novel piecewise fuzzy observer-based output feedback controller is developed. By applying a piecewise Lyapunov function and some techniques on matrix convexification, an approach to the design of observer and controller gain is derived for the augmented closed-loop system to be asymptotically stable with a guaranteed H∞performance and reduced transmission frequency. Finally, two examples are given to show the effectiveness of the developed method.

This paper is concerned with the design of H∞event-triggered filter for a class of Takagi-Sugeno fuzzy systems. Based on the proposed communication strategy, only the measured outputs of the physical plant that violate a predefined triggering condition will win the right for transmission in the shared communication channel. Considering that the implementation of the filter may not be synchronized with the plant trajectories due to the asynchronous premise variables in network environment, a novel observer-based piecewise fuzzy filter is proposed. By adopting the idea of input delay method, the filtering error dynamics is reformulated as a new event-triggered piecewise fuzzy system. By applying a piecewise Lyapunov-Krasovskii functional and some techniques on matrix convexification, a method of event-triggered H∞piecewise filter design is developed for the filtering error system concerned to be asymptotically stable with a given disturbance attenuation level and reduced transmission rate. Moreover, a co-design algorithm to derive the filter gains and the event triggering parameters is proposed. Illustrative examples are finally given to show the effectiveness of the developed method.