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Recent advances in concurrent learning based adaptive controllers have relaxed the persistency of excitation condition required to achieve exponential tracking and parameter estimation error convergence. This was made possible via the use of additional concurrent learning stacks in the parameter estimation algorithm. However, the proposed concurrent learning components, that is, the history stacks, needed to be filled with "selected" values dependent on the actual system states. Therefore, the previously proposed concurrent learning adaptive controllers required the system to be stable initially for a finite time so that the corresponding history stacks can be filled (finite excitation condition). In this work, motivated to remove the finite excitation condition, a novel desired system state based concurrent learning adaptive controller is proposed. In order to remove the system state dependencies in the controller and estimation algorithms, a filtered version of the dynamics and a novel prediction error formulation have been designed. The overall exponential stability, parameter error convergence and boundedness of the system states during closed loop operations are ensured via Lyapunov based arguments. The main advantages of the proposed method are its dependence on the desired system states and the overall stability results that paved the way in removing the need for finite excitation condition. Numerical studies performed on a two link robotic device are also presented to illustrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, an algorithm is presented that aims to extend the use of Euler-Lagrange back integration method to optimal control of linearizable, affine-in-the-input nonlinear systems having more than two states. The algorithm generates optimal paths by selecting a starting point iteratively on the ellipsoid close to the equilibrium point, representing the optimal borderline of the linearized system, and backward integrating the optimal nonlinear dynamics. The generated paths are then utilized in the optimal control loop. [ABSTRACT FROM AUTHOR]

This work presents the design and the corresponding stability analysis of a model-based, joint position tracking error constrained, adaptive output feedback (OFB) controller for robot manipulators. Specifically, provided that the initial joint position tracking error starts within a predefined region, the proposed controller algorithm ensures that the joint tracking error remains inside this region and asymptotically approaches zero, despite the lack of joint velocity measurements and uncertainties associated with the system dynamics. The constraint imposed on the position tracking error term ensures predictable overshoot for the overall system and enables a predetermined transient performance. The need for the joint velocity measurements is removed via the use of a surrogate filter formulation in conjunction with the use of desired model compensation. The stability and the convergence of the closed-loop system are proved via a barrier Lyapunov function (BLF)-based argument. Extensive numerical simulations and experimental studies performed on a two-link, direct-drive robotic manipulator are provided to illustrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This work presents the design and the corresponding stability analysis of a desired model-based, joint position constrained, controller formulation for robotic manipulators. Specifically, provided that the initial joint position tracking error signal starts within some predefined region, the proposed controller ensures that the joint tracking error signal remains inside this region and approaches to zero asymptotically. Extensive numerical simulations and experimental studies performed on a two-link direct-drive planar robot are provided in order to illustrate the effectiveness and feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

This work tackles the tracking control problem of robotic manipulators where the robot dynamics contains uncertain parameters and joint velocity measurements are not available. Specifically when the robotic manipulator is required to perform a periodic task repetitively, as in most industrial applications, a repetitive learning controller is proposed that does not require joint velocity measurements and can compensate the uncertainties of the robot dynamical parameters and additive disturbances caused due to the periodic joint motion. The proposed solution is achieved via the use of a novel learning component in the controller design in conjunction with a novel model‐free joint velocity observer design. The stability of the closed‐loop system and the convergence of both the joint position tracking error and the joint velocity observation error to the origin are guaranteed via Lyapunov based arguments. Experimental results performed on a 2 degree of freedom robot manipulator are presented to demonstrate the performance of the proposed observer–controller couple. [ABSTRACT FROM AUTHOR]

In this work, we propose the development and the corresponding stability analysis of a novel, observer-based output feedback (OFB), tracking controller for rigid-link robot manipulators. Specifically, a model-independent variable-structure-like observer in conjunction with a desired dynamic compensation technique have been utilized to remove the link velocity dependency of the controller formulation. Asymptotic stability of the observer--controller couple is then guaranteed via Lyapunov-based arguments. An adaptive controller extension is also presented to illustrate the expansiveness of the proposed scheme. Experimental studies performed on a two-link planar robot with dynamical uncertainties are included in order to demonstrate the performance and feasibility of the proposed method. [ABSTRACT FROM PUBLISHER]

In this study, we present a new robust continuous controller mechanism for the tracking problem of uncertain nonlinear systems. The proposed strategy is based on a Lyapunov-type stability argument and only requires the uncertainties of the dynamical system to be the first-order differentiable to achieve asymptotic practical tracking. For the ease of presentation, the controller formulation is presented on a general, second-order dynamical system, extension to higher order versions are also possible with a considerably small effort. Simulation studies comparing the performance of the proposed method with the classical Sliding mode and robust integral of the sign of the error controller are presented to illustrate the performance and the feasibility of the proposed strategy. Experimental validation on a two link direct drive robot manipulator are also included to illustrate the implementability of the proposed method. [ABSTRACT FROM AUTHOR]

This work introduces an innovative robust partial state feedback controller designed for robotic manipulators actuated by brushless DC motors. The proposed controller/observer structure relies solely on actuator current and robot joint position measurements, while effectively compensating for dynamic uncertainties in both the electrical (actuator dynamics) and mechanical (robot's dynamical terms) subsystems. Specifically, a model–based robust observer, eliminating the need of joint velocity measurements, is combined with a backstepping‐type controller design. As opposed to the previous model based observers that depend on the actual model parameters, the proposed observer structure utilizes the best guest estimates of the system parameters supported with a robust compensation term. This approach eliminates the need for precise knowledge of system parameters of the observer design which is a significant improvement over most of the previous results. This approach ensures the semi‐global, uniformly ultimately bounded joint tracking error signal. The overall stability of the closed‐loop system is affirmed through Lyapunov‐based arguments. Experimental studies conducted on an in‐house‐built two‐link robotic manipulator, actuated by brushless DC motors, are presented to demonstrate the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, an extended Jacobian matrix formulation is proposed for the operational space tracking control of kinematically redundant robot manipulators with multiple subtask objectives. Furthermore, to compensate the structured uncertainties related to the robot dynamics, an adaptive operational space controller is designed, and then, the corresponding stability analysis is presented for kinematically redundant robot manipulators. Specifically, the proposed method is concerned with not only the stability of operational space objective but also the stability of multiple subtask objectives. The combined stability analysis of the operational space objective and the subtask objectives are obtained via Lyapunov based arguments. Experimental and simulation studies are presented to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, a continuous robust-adaptive operational space controller that ensures asymptotic end-effector tracking, despite the uncertainties in robot dynamics and on the velocity level kinematics of the robot, is proposed. Specifically, a smooth robust controller is applied to compensate the parametric uncertainties related to the robot dynamics while an adaptive update algorithm is used to deal with the kinematic uncertainties. Rather than formulating the tracking problem in the joint space, as most of the previous works on the field have done, the controller formulation is presented in the operational space of the robot where the actual task is performed. Additionally, the robust part of the proposed controller is continuous ensuring the asymptotic tracking and relatively smooth controller effort. The stability of the overall system and boundedness of the closed loop signals are ensured via Lyapunov based arguments. Experimental results are presented to illustrate the feasibility and performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we provide a tracking controller formulation for dynamically positioned surface vessels with an asymmetric added mass terms that affects the overall system dynamics at the acceleration level. Specifically a novel continuous robust controller is proposed for surface vessels that in addition to unstructured uncertainties in its dynamics, contains added mass effects in its inertia matrix. The proposed controller compensates the overall system uncertainties while ensuring asymptotic tracking by utilizing the knowledge of the leading principal minors of the input gain matrix. Stability of the closed–loop system and asymptotic convergence are proven via Lyapunov based approaches. Simulation studies are also presented to illustrate the viability of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we propose an observer-based adaptive output feedback tracking controller for dynamically positioned surface vessels. Specifically, to remove the velocity measurement dependency of the control formulation a nonlinear, model-free observer formulation have been proposed. The proposed observer does not make use of the system dynamics and together with the proposed controller structure ensure that the tracking error signal and the velocity estimation error asymptotically converges to zero. Stability of the closed-loop system is ensured by Lyapunov-based arguments. Simulation studies are also presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This work presents the design and the corresponding analysis of a nonlinear robust adaptive controller for model reference tracking of an aircraft that has parametric uncertainties in its system matrices and additive state- and/or time-dependent nonlinear disturbance-like terms in its dynamics. Specifically, robust integral of the sign of the error feedback term and an adaptive term is fused with a proportional integral controller. Lyapunov-based stability analysis techniques are utilised to prove global asymptotic convergence of the output tracking error. Extensive numerical simulations are presented to illustrate the performance of the proposed robust adaptive controller. [ABSTRACT FROM PUBLISHER]

In this work, a control moment gyroscope (CMG) supported by active magnetic bearing (AMB) is considered with a gimbal angular movement. Gimbal angular movement in a CMG imposes extra load on magnetic bearings. A complete model of the CMG system with flywheel, AMBs, and gimbal dynamics is derived for control design. Lyapunov based composite adaptive output feedback control is proposed for fast uncertainty compensation in the CMG system, and some simulation results are presented. For a gimbal angular velocity input, a flywheel rotor magnetic bearing system is simulated at constant rotational speed of the flywheel rotor. [ABSTRACT FROM AUTHOR]

In this work, we present a novel continuous robust controller for a class of multi-input/multi-output nonlinear systems that contains unstructured uncertainties in their drift vectors and input matrices. The proposed controller compensates uncertainties in the system dynamics and achieves asymptotic tracking while requiring only the knowledge of the sign of the leading principal minors of the input gain matrix. A Lyapunov-based argument backed up with an integral inequality is applied to prove the asymptotic stability of the closed-loop system. Simulation results are presented to illustrate the viability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the problem of accurate positioning of an unactuated surface vessel by using multiple uni-directional tugboats is investigated. Specifically a robust controller that ensures asymptotic position tracking is designed. The control design procedure is implemented in two steps: Initially by locating opposing tugboats to specific configurations, the overall problem is transformed into a second order system with an uncertain non-symmetric input gain matrix. Then via a matrix decomposition, a novel robust controller methodology is proposed. The stability of the overall system is ensured via rigorous stability analysis where asymptotic position tracking is ensured. Numerical simulation results are presented to demonstrate the efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

This work presents the design and the corresponding stability analysis of a robust backstepping controller for robot manipulators driven by brushless DC motors. The overall stability of the mechanical and electrical subsystems is validated via Lyapunov based arguments. The proposed methodology achieves global practical tracking (i.e. , globally uniformly ultimate boundedness) of the desired joint level trajectories despite the presence of uncertainties associated with the dynamical parameters of the mechanical and the electrical actuation system. Experimental studies performed on an in house built 2 link robotic device actuated via brushless DC motors are presented to illustrate the performance and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

This work concentrates on output feedback trajectory tracking control of marine vehicles with dynamical uncertainties. The system under consideration, due to the natural response of oceanic waves, is also subject to periodic external disturbances. The output feedback structure of the proposed controller algorithm is established via a novel nonlinear model-free observer in conjunction with a Fourier series expansion-based periodic disturbance estimator. Lyapunov-based arguments have been utilized to prove the stability of the closed loop system and the convergence of tracking and unmeasured state observation errors under the closed loop operation. Performance demonstration and viability of the proposed method are realized via comparative numerical simulations. [ABSTRACT FROM AUTHOR]

This work presents an alternative solution to the tracking control of dynamically positioned surface vessels having uncertainties associated with their dynamical model while using only vessel's position measurements. Specifically, a surrogate state filter in conjunction with a composite parameter estimation algorithm is applied to ensure semi-global asymptotic convergence of the states of the vessel to their respective desired values. As opposed to its solely gradient based counterparts, the proposed algorithm utilizes an update law that is composed of a gradient update law driven by position tracking error and a least squares update law driven by the prediction error. This enables the proposed controller to compensate for the model uncertainties in a relatively shorter period of time. Rigorous analysis based on Lyapunov type approaches are applied in order to ensure the stability and boundedness of the closed loop system. Comparative simulations performed on the model of a surface vessel are presented in order to illustrate the tracking, and parameter estimation performance of the proposed controller/adaptation algorithm. • A combination of estimation based with gradient based parameter estimation for faster parameter estimation convergence. • Control of the system using only position measurements. • Lyapunov based approaches are applied to ensure the stability of the system. [ABSTRACT FROM AUTHOR]

Considers the problem of orientation tracking control of wheeled mobile robots (WMR) via visual servoing in the presence of parametric uncertainty associated with the mechanical dynamics and the camera system. Description of the kinematic model of a WMR in the task-space and camera-space; Design of an adaptive controller for WMR; Simulation and experimental results of the performance of the controller.

In this study, the design of a smooth robust velocity observer for a class of uncertain nonlinear mechatronic systems is presented. The proposed velocity observer does not require a priori knowledge of the upper bounds of the uncertain system dynamics and introduces time-varying observer gains for uncertainty compensation. Practical stability of the velocity observation error is ensured via Lyapunov-type stability analysis. Experimental results obtained from Phantom Omni haptic device are presented to illustrate the performance of the proposed velocity observer. [ABSTRACT FROM AUTHOR]

Tracking control of marine vessels in the presence of parametric uncertainty and additive periodic disturbances is considered. For optimal estimation of environmental forces, periodic disturbance estimation method inspired from Fourier series expansion have been applied. Stability of the closed–loop system and the convergence of the tracking error under the closed–loop operation are established via Lyapunov based arguments. Simulation studies are provided to support the theoretical results and the effectiveness of the proposed method. • A novel robust adaptive controller is designed for position tracking control of a dynamically positioned marine vessels. • Periodic disturbance estimation method is proposed. • The overall system dynamics is considered to be uncertain. [ABSTRACT FROM AUTHOR]