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 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.