The primary objective of this study is to enable the end effector of robot manipulators driven by brushless DC motors (BLDC), subjected to model uncertainties, to track the desired trajectory. Direct control in task space, with the primary goal of minimizing the tracking error of the end effector, is favored. Besides, incorporating actuator dynamics (AD)actuator dynamics (AD) into control synthesis and stability analysis is intended to enhance the sensitivity in terms of positioning and the reliability of robot manipulators. Consideration is given to uncertainties in both the robot manipulator and AD to achieve enhanced tracking performance. In order to improve the efficiency of the closed-loop control system, uncertainties in the dynamic model and AD were estimated using a self-organized adaptive fuzzy logic (AFL)adaptive fuzzy logic (AFL) framework, and the obtained estimates were applied to the control torque input. In the employed AFL framework, the means and variances of the membership functions (MFs)membership functions (MFs) are updated online in each iteration, enabling a more accurate estimation of uncertainties. The use of the newly created Lyapunov function demonstrates that the closed-loop system is uniformly ultimately bound. Experimental comparisons were conducted on a two-degree-of-freedom planar robot manipulator driven by a BLDC motor to test the applicability of the presented controller.

This work concentrates on dynamical parameter estimation problem for super-coiled polymer (SCP) actuator systems. Specifically, a filtered-based least squares estimator has been proposed. The stability of the estimator is ensured using Lyapunov-based arguments. Numerical studies are presented to illustrate the estimation performance.

This work concentrates on a neural network-based, prescribed time controller formulation for a class of nonlinear systems having parametric uncertainty. The aim of the controller is to ensure that the tracking error converges to the origin within a user-defined prescribed time despite the presence of bounded disturbances and parametric uncertainties with controller/update law modularity. The stability of closed–loop error system has been ensured via Lyapunov-based arguments. Numerical simulations are conducted to illustrate the feasibility of the proposed method.

This paper presents an adaptive kinematic control strategy for robotic manipulators, which takes advantage of a concurrent learning-based approach to address kinematic uncertainties. Departing from typical approaches that rely on position-level inverse kinematics, the developed framework operates directly in Cartesian space, thereby reducing computational complexity and mitigating singularity-related issues. The control framework incorporates a concurrent learning-based adaptive update law, providing precise end-effector trajectory tracking and real-time identification of uncertain kinematic parameters under interval excitation condition, which is less stringent than persistent excitation. Stability analysis is conducted using a Lyapunov-Based framework which proves the global exponential convergence of both tracking and parameter estimation errors. Numerical simulations validate the effectiveness of the developed approach, accurately demonstrating trajectory tracking and identification of the uncertain kinematic terms.

This paper presents a novel control strategy for brushless DC (BLDC) motor-driven, multi-degree-of-freedom robot manipulators, addressing the challenges posed by the highly nonlinear and coupled dynamics of the motor and manipulator. The proposed controller leverages the universal approximation property of Legendre polynomials, a class of orthogonal functions, to effectively compensate for modeling uncertainties and system nonlinearities. A rigorous stability analysis is conducted using Lyapunov-based methods, guaranteeing semi-global uniform ultimate boundedness of the closed-loop system. Experimental studies on an in-house developed BLDC-driven robotic device validate the effectiveness of the proposed controller, demonstrating its capability to achieve precise trajectory tracking with robust performance.

This work presents a robust, neural network based controller formulation for the displacement tracking problem of electro–hydraulic systems subject to uncertainties associated with their dynamical parameters and actuator saturation. Specifically, a neural network based compensator is utilized to estimate some of the nonlinear components of the uncertain dynamical terms and then in conjunction with robust backstepping procedure, the overall formulation ensure the uniform practical stability of the closed-loop system. Stability and convergence of error terms are proven using Lyapunov based arguments and numerical studies are presented in order to illustrate the feasibility of the proposed methodology.

In this paper, a robust controller is designed to achieve accurate positioning of an unactuated surface vessel by using multiple unidirectional tugboats. After initially locating opposing tugboats to specific configurations, the control problem is transformed into a second order system with an uncertain non-symmetric input gain matrix. Upon applying a matrix decomposition, a robust controller is proposed. Detailed stability analysis ensured asymptotic tracking. Numerical simulation results demonstrate the efficiency of the proposed controller.

In this work inspired by flocking of birds or fish communities traveling together in the nature, we have developed a novel dynamically reformable mobile formation algorithm for the navigation of wheeled mobile robot (WMR) groups operating in complex and/or obstacle dense environments. The proposed method is formed via the combination of simple and computationally efficient tools such as (i) a mobile network of a small number of WMR sensors for detecting obstacles; (ii) cardinal cubic splines or least squares fits for modeling the formation boundaries based on this small network; and (iii) reference frames to ensure uniform spacing and velocity profiles along the ensemble. By these components a simple geometrical formation is developed for real time flock path planning of relatively large groups of small agents. To our best knowledge the proposed approach is novel and its effectiveness is verified by simulations in complex environments.

Path planning for formations of wheeled mobile robots (WMRs) has been an active research topic of robotics in the recent years. The methods of route generation for WMRs usually utilize complex algorithms, which require global information of the workspace or mapping of the nearby environment. Hence they are not very efficient for formation based motion of large collections of small entry-level agents. In this work a path generation method is developed for synthesizing non-holonomic paths for small unicycle WMRs, and then is integrated into simple geometrical formations for extension to navigation of relatively large groups of small agents. Our technique is based on a computationally low-cost algorithm; and works effectively for transfer of many robots in obstacle cluttered environments. The efficiency of our technique is verified by simulation results.

In this paper, we utilize a Lyapunov-based design approach. to construct a visual servoing controller for a robot manipulator that ensures uniformly ultimately bounded (UUB) end-effector position tracking performance despite parametric uncertainty throughout the entire robot/camera system. The UUB end-effector tracking result exploits information from both a fixed camera and a camera-in-hand. although both cameras contain parametric uncertainty in the calibration parameters (e.g., focal length, image center, scaling factors, and camera position and orientation). The advantages of the cooperative camera configuration are that: (i) the fixed camera can be mounted so that a large robot workspace is visible, (ii) the camera-in hand is mounted so that a high resolution, close-up view of an object is achieved, facilitating the potential for more precise robotic motion. and (iii) the fixed camera provides a mechanism for treating the problem of determining the relative velocity of the robot end-effector with respect to the object for the camera-in-hand object tracking problem when the camera is uncalibrated.

In this paper, a learning-based feedforward term is developed to solve a general control problem in the presence of unknown nonlinear dynamics with a known period. Since the learning-based feedforward term is generated from a straightforward Lyapunov-like stability analysis, the control designer can utilize other Lyapunov-based design techniques to develop hybrid control schemes that utilize learning-based feedforward terms to compensate for periodic dynamics and other Lyapunov-based approaches (e.g., adaptive-based feedforward terms) to compensate for nonperiodic dynamics. To illustrate this point, a hybrid adaptive/learning control scheme is utilized to achieve global asymptotic link position tracking for a robot manipulator.

This paper considers the problem of position/orientation tracking control of wheeled mobile robots via visual servoing in the presence of parametric uncertainty associated with the mechanical dynamics and the camera system. Specifically, we design an adaptive controller that compensates for uncertain camera and mechanical parameters and ensures global asymptotic position/orientation tracking. Simulation and experimental results are included to illustrate the performance of the control law.

This paper presents the design of a differentiable, kinematic control law that achieves global asymptotic tracking. In addition, we also illustrate how the proposed kinematic controller provides global exponential tracking provided the reference trajectory satisfies a mild persistency of excitation (PE) condition. We also illustrate how the proposed kinematic controller can be slightly modified to provide for global asymptotic regulation of both the position and orientation of the mobile robot. Finally, we embed the differentiable kinematic controller inside of an adaptive controller that fosters global asymptotic tracking despite parametric uncertainty associated with the dynamic model. Experimental results are also provided to illustrate the performance of the proposed adaptive tracking controller.

We present a global, output feedback, tracking controller for rigid-link, flexible-joint robots. Specifically, we design an exact model knowledge, Lyapunov-based controller which provides global asymptotic link position tracking despite the fact that only link position measurements are available.