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 study focuses on the challenges in controlling altitude changes in agricultural UAVs due to dynamic weight changes as a result of spraying. An innovative controller framework is developed to adapt quickly to weight changes, maintaining precise altitude control. The framework uses correction functions based on weight changes and height error, validated through experimental tests on quadcopters and hexacopters. The results demonstrate high performance in mitigating disturbances such as weight changes, wind, gusts, and thus enhance the reliability and effectiveness of UAVs in agriculture, promoting their broader adoption.

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.

Accurate identification of Time Difference/Delay of Arrival (TDoA) and gain ratio of arrival (GRoA) is of great importance for signal source localization. Most of the studies in the literature proposed frequency domain solutions for this problem. In this work, in a seemingly novel departure from the main stream of existing results, a time domain method is designed to simultaneously estimate TDoA and GRoA for location-measurement applications. The method is applicable to signals in vector form composed of sum of multiple signals received from multiple sources. Another advantage of the method is that it is structured to operate online. The stability of the proposed identification framework is analyzed via novel Lyapunov-type tools where global uniform ultimate boundedness of the estimation errors is ensured. The performance of the method is evaluated by numerical simulations and satisfactory results are obtained even when the received signals are subject to measurement noise.

This article aims to design a joint space tracking controller for robotic manipulators having uncertainties in their mathematical representations under the additional constraint that joint velocity sensing not being available. A two-part design is followed where in the first part, the modeling uncertainties are dealt with a self-organized adaptive fuzzy-logic (AFL)-based controller where full-state feedback (FSFB) is assumed. The stability analysis yields semiglobally uniformly ultimately bounded tracking results. In the second part, a high-gain joint velocity observer is designed followed by replacing error vectors in the FSFB controller with their saturated versions obtained from the observer design to arrive at a self-organized AFL-based robust output-feedback controller. The stability analysis is performed via a multiple-step Lyapunov-type method where the semiglobal uniform ultimate boundedness of the tracking error is ensured. Comparative experiment results obtained from a planar robotic manipulator are presented to demonstrate the efficacy of the proposed control 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 paper, teleoperated control of a kinematically redundant, continuum slave manipulator with a non-redundant, rigid-link master system is considered. This problem is novel because the self-motion of the redundant robot can be utilized to achieve secondary control objectives while allowing the user to concentrate on controlling only the tip of the slave system. To that end, feedback linearizing controllers are proposed for both the master and slave systems, whose effectiveness is demonstrated using numerical simulations for the case of singularity avoidance as a subtask.

The emergence of wind turbine systems for electric power generation can help satisfy the growing global demand. This paper proposes a control strategy to maximize the wind energy captured in a variable speed wind turbine, with an internal induction generator, at low to medium wind speeds. The proposed strategy controls the tip speed ratio, via the rotor angular speed, to an optimum point at which the efficiency constant (or power coefficient) is maximal for a particular blade pitch angle and wind speed. This control method allows for aerodynamic rotor power maximization without exact wind turbine model knowledge.

In this paper, the tracking control of a underactuated quadrotor aerial vehicle is presented where position and yaw trajectory tracking is achieved using feedback control system. The control design is complicated by considering parametric uncertainty in the dynamic modeling of the quadrotor aerial-robot. Robust control schemes are then designed using a Lyapunov-based approach to compensate for the unknown parameters in each dynamic subsystem model. Lyapunov-type stability analysis suggests a global uniform ultimately bounded (GUUB) tracking result.

In this paper, a novel Euclidean position estimation technique using a single uncalibrated camera mounted on a moving platform is developed to asymptotically recover the three-dimensional (3D) Euclidean position of static object features. The position of the moving platform is assumed to be measurable, and a second object with known 3D Euclidean coordinates relative to the world frame is considered to be available a priori. To account for the unknown camera calibration parameters and to estimate the unknown 3D Euclidean coordinates, an adaptive least squares estimation strategy is employed based on prediction error formulations and a Lyapunov-type stability analysis. The developed estimator is proven to recover the 3D Euclidean position of the unknown object features despite the lack of knowledge of the camera calibration parameters.

In this paper, a new range identification technique for a calibrated paracatadioptric system mounted on a moving platform is developed to recover the range information and the three-dimensional (3D) Euclidean coordinates of a static object feature. The position of the moving platform is assumed to be measurable. To identify the unknown range, first a function of the projected pixel coordinates is related to the unknown 3D Euclidean coordinates of an object feature. This function is nonlinearly parameterized (i.e., the unknown parameters appear nonlinearly in the parameterized model). An adaptive estimator based on a min-max algorithm is then designed to estimate the unknown 3D Euclidean coordinates of an object feature relative to a fixed reference frame which facilitates the identification of range. A Lyapunov-type stability analysis is used to show that the developed estimator provides an estimation of the unknown parameters within a desired precision.

In this paper, a new dynamic model for continuum robot manipulators is derived. The dynamic model is developed based on the geometric model of extensible continuum robot manipulators with no torsional effects. The development presented in this paper is an extension of the dynamic model proposed by Mochiyama and Suzuki (2002) to include a class of extensible continuum robot manipulators. Numerical simulation results are presented for a planar 3-link extensible continuum robot manipulator.

In this paper, the dynamic model for planar continuum manipulators that was presented in our previous work is extended to include new terms reflecting the effects of potential energy. First the gravitational potential energy of the manipulator is derived. Then, the elastic potential energy of the manipulator is derived for both bending and extension. Finally, the effects of the total potential energy are included in the dynamic model. Numerical simulation results are presented for a planar 3-section extensible continuum robot manipulator. The results show a much stronger match to physical continuum robots than with previously available models.