This work presents the design and the corresponding stability analysis of a self-tuning external load observer for an electro-hydraulic actuator system. The proposed observer does not require the exact knowledge of the dynamical parameters of the actuator and ensures the asymptotic convergence of the external load estimate to the actual value, removing the need of an extra force sensor. An adaptive observation gain algorithm is also introduced to ease the tuning process. The stability and convergence of the observer is ensured via Lyapunov based arguments. Extensive numerical studies are performed to demonstrate the viability and effectiveness of the proposed method.

This work proposes a novel robust observer-controller formulation for a class of second order nonlinear systems where only the output measurements and partial knowledge of the dynamical system parameters are available. Specifically, an observer formulation that makes use of nominal values of the dynamical system parameters in conjuction with a robust output feedback controller is designed to ensure semi-global exponential convergence of the position tracking error to a practical bound in the neighborhood of the origin. The stability of the closed-loop system and convergence of the tracking error terms are guaranteed via Lyapunov based arguments. Extensive numerical studies are presented to illustrate the performance and feasibility of the observer-controller couple.

This work presents the design and corresponding analysis of a robust tracking controller for a class of nonlinear systems subject to hysteresis. Specifically, the hysteresis effects are assumed to be in the form of the Bouc-Wen model and despite the lack of accurate knowledge of system dynamics and hysteresis model parameters, a continuous robust controller is designed via the use of the robustness of integral of sign of the error feedback. The stability of the closed–loop system and the convergence of the tracking error are ensured via Lyapunov based arguments. The tracking performance of the proposed control framework is investigated by numerical simulation studies for a second order system.

Velocity observers, specifically model independent observers, are of great value for controller implementation of mechatronic systems where the velocity of the system is either not measurable or the corresponding sensor is too costly. Therefore, the use of a surrogate signal is imperative. In this research, we present the design and associated stability of a saturation function based velocity estimator/observer for a general class of mechatronic systems. The proposed method estimates velocity without requiring prior knowledge of system dynamics. A novel adaptive update algorithm automatically adjusts the observer gains, eliminating the need for manual gain-tuning. We establish the stability of the velocity observer, along with the auto-tuned gain update rules, through a Liapunov-type analysis. Experimental validation on a rotary inverted pendulum demonstrates the effectiveness of the proposed method. Two case studies are conducted: the first compares the real-time performance of the proposed observer with a signum function based observer, while the second evaluates the observer's reliability with constant gains, highlighting its capability to support control design and maintain robustness against external disturbances.

This paper presents a concurrent learning-based, high-order tuner adaptive control framework for the end effector tracking control of robotic manipulators. Via defining the tracking error in task space, the proposed controller circumvents the computational intensity of resolving position level inverse kinematic solutions at each sampling instance. A Lyapunov-based stability analysis verifies the exponential stability of both parameter estimation and task space tracking errors. The effectiveness of the controller and adaptation algorithm is validated through numerical studies conducted on the model of a two link, revolute joint robotic arm, with a focus on trajectory tracking and parameter identification performance.

This work presents an observer based estimation method for the model uncertainties of super coiled polymer (SCP) actuators using an adaptive fuzzy logic (AFL) based approach. Specifically, an observer formulation based on fuzzy logic estimator is proposed for the uncertainties present in the nonlinear model of the SCP actuator. The proposed observer utilizes the known parts of the system model while employing AFL to estimate the unknown parameters and uncertainties. The design incorporates a self-learning fuzzy logic based term, where the control representative value matrix, as well as the means and variances of the membership functions, are dynamically updated. The stability and convergence of the overall system is ensured rigorously via Lyapunov type arguments. Simulation studies are presented to illustrate the performance and viability of the proposed method.

This work presents a concurrent learning based adaptive controller formulation for a class of uncertain linear systems having unknown effectiveness matrix that represents potential degradations in actuator performance. Specifically, making use of a memory stack containing selected data from the recorded and current information gathered from the system's runs incorporated with a model reference adaptation algorithm, asymptotically convergent error tracking is guaranteed. When the memory stack contains sufficiently rich system information, the proposed methodology ensures both exponential convergence of the tracking error signal and exact estimation of the parameters.

This work presents the design and analysis of a robust partial state feedback controller formulation for the tracking control of robotic manipulators driven by brushless direct current (BLDC) motors. The proposed framework explicitly incorporates actuators' electrical dynamics together with the manipulator's mechanical dynamics, thereby providing a more realistic closed-loop model. The proposed approach explicitly addresses actuator dynamics and uncertainties in both the dynamic and actuator subsystem models. Specifically, the proposed controller ensures uniformly ultimately bounded convergence of the position tracking error signal in the presence of parametric uncertainties and external disturbances, as well as under the absence of joint velocity measurements. Lyapunov-based arguments are used to ensure the semi-global stability and convergence of the system states. To compensate for the absence of joint velocity measurements, a pseudo-velocity filter is integrated into the controller framework. The effectiveness of the proposed control law is verified through comparative numerical simulations and is further validated via experimental studies conducted on an in-house built two-link planar robotic manipulator actuated by BLDC motors, thereby validating the theoretical developments.

This work presents a novel solution to adaptive output feedback position tracking control of a class of nonlinear systems with guaranteed dynamical parameter estimation. Specifically, a concurrent learning-based update rule fused by the filtered version of the desired system dynamics in conjunction with a desired state-based regression matrix have been utilized to ensure that both the position tracking error and parameter estimation error terms converge to origin exponentially while requiring only the position measurements. To the best knowledge of the authors, the proposed method is the first adaptive output feedback controller with guaranteed parameter convergence without needing to satisfy the persistence of excitation condition.

This paper presents a deep neural network-based adaptive backstepping control strategy for permanent magnet synchronous motors operating under parametric uncertainties and external disturbances. The proposed controller leverages the universal approximation capability of neural networks together with Lyapunov-based adaptation laws to compensate lumped unmodeled dynamics without requiring precise identification of the full electromechanical model. Embedded within a field-oriented control framework, the design combines an adaptive backstepping speed-control loop with a q-axis current loop augmented by a deep neural network approximation of the total uncertainty in the torque-producing channel. A Lyapunov-based stability analysis establishes boundedness of all closed-loop signals and semi-global asymptotic convergence of the speed and current tracking errors under the stated assumptions and a verifiable gain condition. A hybrid learning strategy is adopted in which the inner layers of the deep neural network are pretrained offline using data collected from a PI-controlled drive, while the output-layer parameters are updated online in real time. The proposed method is validated using numerical simulations and then implemented on a C2000-based embedded motor-drive platform to demonstrate real-time applicability. In simulation, periodic inner-layer retraining using newly collected closed-loop data is also investigated to assess the effect of feature refinement; in the embedded implementation, the inner-layer parameters remain fixed and only the output layer is adapted online due to real-time constraints.

An adaptive control methodology is developed in this work to regulate task space motions of kinematically redundant manipulators while rigorously respecting user-defined prescribed constraints. To enforce task space constraints while handling model uncertainties, the control structure combines a barrier Lyapunov function based design with an adaptive neural network, enabling both uncertainty compensation and guaranteed constraint satisfaction under a unified stability analysis. This integration leads to a reliable and constraint aware control framework capable of ensuring stable tracking behavior for safety-critical robotic applications. In addition, a null space controller is designed to exploit the kinematic redundancy, allowing the manipulator to meet a secondary objective without interfering with the primary task space objective. A comprehensive Lyapunov based stability analysis is conducted to rigorously guarantee both the boundedness of the tracking error and the satisfaction of the user-imposed constraints. Comparative numerical simulations are performed to validate the feasibility and effectiveness of the proposed adaptive neural network based task space constraint control strategy.

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 study presents a novel continuous controller, in conjunction with a fuzzy logic-based estimator, designed to address the compensation of parametric uncertainty in a category of high-order, multiple-input-multiple-output nonlinear systems. The proposed controller-estimator methodology tackles parametric uncertainties with self-adjusting adaptive fuzzy logic-based robust integral of sign of error algorithm. In the employed adaptive fuzzy logic (AFL) framework, the means and variances of the membership functions are updated online in each iteration, enabling a more accurate estimation of uncertainties. The boundedness of the closed-loop system and asymptotic stability of the error signals are verified via Lyapunov-based arguments. Numerical simulations are additionally presented to evaluate the efficacy of the proposed methodology.

In this study, a machine learning integrated adaptive control algorithm was investigated for a robotic manipulator. The proposed approach employs a feedforward neural network algorithm to enhance the control torque signals under the disturbance signals. Neural network algorithms were used in the control algorithm to approximate the unknown nonlinearities of the robot dynamics and suppress the unknown external disturbance. An offline training procedure was used for the assignment of the initial weights of the neural network algorithm. Additionally, the outer-layer weight parameters of the neural network algorithms were updated online using adaptation laws based on the Lyapunov function. The effectiveness of the proposed control strategy was examined through numerical simulations on a two-link robotic manipulator for trajectory tracking. Performance was quantitatively evaluated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Numerical simulation results demonstrate that the proposed algorithm achieves accurate trajectory tracking under unknown disturbance signals, highlighting its potential for practical applications in robotic systems subject to uncertainties and external effects.

The main objective of this study is to devise a control structure for robot manipulators actuated by brushless direct current (BLDC) motors while accounting for uncertainties in kinematic, dynamic, and electrical models. Robustness properties of sliding mode control techniques are employed to address uncertainties associated with dynamic and electrical models, whereas a gradient-based adaptive approach is utilized for handling kinematic model uncertainties. To keep the control efforts at reasonable limits, the known nominal values of the dynamical and electrical model parameters are also employed in the controller design. As part of the backstepping based methodology, a novel continuous virtual controller design strategy is tailored as well. The stability of the closed-loop system is analyzed rigorously utilizing Lyapunov-type arguments and global asymptotic stability is ensured. Experimental results obtained from a custom-built two degree-of-freedom planar robot manipulator actuated by BLDC motors demonstrated the efficacy of the proposed control framework where the task space tracking errors remained below 0.8 millimeters.

This study presents the design and the corresponding stability analysis of a robust observer based partial state feedback end-effector tracking controller for collaborative robot manipulators actuated via brushless DC motors. The proposed controller-observer couple compensate for the uncertainties associated with the robot link dynamics, kinematics of the manipulator and joint actuator parameters while not requiring joint velocity measurements. The overall stability and convergence of the error signals into an ultimate bound around origin is ensured via Lyapunov type arguments. Numerical simulation studies are also presented to back the theoretical findings and to illustrate the effectiveness of the proposed methodology.

Electro-hydraulic systems are an essential part of modern industry. Nonetheless, these systems have input constraints and must be controlled under model uncertainties and disturbances using a saturated control signal. In this study, a saturation compensation scheme is developed using an adaptive fuzzy logic based methodology via the use of Lyapunov based synthesis and analysis method. The proposed methodology ensures uniform ultimate boundedness of the error signals despite the uncertainties in the system dynamics. The effectiveness of the proposed methodology is demonstrated through numerical simulations.

This work introduces a variable structure velocity observer formulation for a broad class of second order nonlinear engineering systems. The proposed observer formulation incorporates a novel error signal that performs similarly to a sliding-mode observer for larger values of the position observation error and acts as a high-gain term when the position observation error is small. The stability of the observer formulation is validated through a novel Lyapunov-like argument, ensuring the practical asymptotic convergence of the observation error. Specifically, the velocity observation error is guaranteed to converge to a small region near the origin, that can be adjusted to be arbitrarily small by the observation gains. The effectiveness of the observer formulation is examined through numerical simulations on the New England 39-bus power system, a testing platform comprising 39 buses and 10 generators.

In this study, an adaptive controller design is carried out for robot manipulators whose joints are driven using brushless direct current (BLDC) motors and which have parametric uncertainties in the dynamic model, by considering the actuator dynamics. Thanks to the realization of controller design directly in the task space, the proposed controller structure does not need inverse kinematics calculations at the position level. Despite the parametric uncertainties in the robot dynamic model, the global asymptotic stability of the developed controller structure with full state feedback, which does not need acceleration measurements, is guaranteed by using Lyapunov type synthesis and stability analysis method. To demonstrate the performance and feasibility of the proposed method, a simulation study was carried out using a two degree of freedom, planar robot manipulator model, whose joints are driven using BLDC motors.

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.