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.

Continuum or hyper-redundant robot manipulators can exhibit behavior similar to biological trunks, tentacles, or snakes. Unlike traditional rigid-link robot manipulators, continuum robot manipulators do not have rigid joints, hence these manipulators are extremely dexterous, compliant, and are capable of dynamic adaptive manipulation in unstructured environments. However, the development of high-performance control algorithms for these manipulators is quite a challenge, due to their unique design and the high degree of uncertainty in their dynamic models. In this paper, a controller for continuum robots, which utilizes a neural network feedforward component to compensate for dynamic uncertainties is presented. Experimental results using the OCTARM, which is a soft extensible continuum manipulator, are provided to illustrate that the addition of the neural network feedforward component to the controller provides improved performance.

This paper describes the results of field trials and associated testing of the OctArm series of multi-section continuous backbone "continuum" robots. This novel series of manipulators has recently (Spring 2005) undergone a series of trials including open-air and in-water field tests. Outcomes of the trials, in which the manipulators demonstrated the ability for adaptive and novel manipulation in challenging environments, are described. Implications for the deployment of continuum robots in a variety of applications are discussed

A visual servo tracking controller is developed in this paper for a monocular camera system mounted on an underactuated wheeled mobile robot (WMR) subject to nonholonomic motion constraints (i.e., the camera-in-hand problem). A prerecorded image sequence (e.g., a video) of three target points is used to define a desired trajectory for the WMR. By comparing the target points from a stationary reference image with the corresponding target points in the live image and the prerecorded sequence of images, projective geometric relationships are exploited to construct Euclidean homographies. The information obtained by decomposing the Euclidean homography is used to develop a kinematic controller. A Lyapunov-based analysis is used to develop an adaptive update law to actively compensate for the lack of depth information required for the translation error system. Experimental results are provided to demonstrate the control design.

Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a nonlinear observer is used to identify a class of actuator faults once the fault has been detected by some other method. Advantages of the proposed fault-identification method are that it is based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), it does not require acceleration measurements, and it is independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault. Experimental results are provided to illustrate the performance of the identification method.

A monocular camera-based vision system attached to a mobile robot (i.e., the camera-in-hand configuration) is considered in this paper. By comparing corresponding target points of an object from two different camera images, geometric relationships are exploited to derive a transformation that relates the actual position and orientation of the mobile robot to a reference position and orientation. This transformation is used to synthesize a rotation and translation error system from the current position and orientation to the fixed reference position and orientation. Lyapunov-based techniques are used to construct an adaptive estimate to compensate for a constant, unmeasurable depth parameter, and to prove asymptotic regulation of the mobile robot. The contribution of this paper is that Lyapunov techniques are exploited to craft an adaptive controller that enables mobile robot position and orientation regulation despite the lack of an object model and the lack of depth information. Experimental results are provided to illustrate the performance of the controller.

In this paper, the 3-dimensional (3D) position of a camera held by the end-effector of a cartesian robot manipulator is regulated to a constant desired position despite (i) the lack of depth information of the actual or desired camera position from a target, (ii) the lack of a 3D model of the target object, and (iii) the lack of information of intrinsic and extrinsic camera calibration matrices. Specifically, by fusing 2D image-space and 3D task-space information (i.e., 2.5D visual servoing), an adaptive controller is developed that is proven to ensure asymptotic position regulation of the cartesian robot. The stability of the proposed controller is proven through a rigorous Lyapunov-based stability analysis.

Several factors must be considered for robotic task execution in the presence of a fault, including: detection, identification, and accommodation for the fault. In this paper, a prediction error based dead-zone residual function and nonlinear observers are used to detect and identify a class of actuator faults. Advantages of the proposed fault detection and identification methods are that they are based on the nonlinear dynamic model of a robot manipulator (and hence, can be extended to a number of general Euler Lagrange systems), they do not require acceleration measurements, and they are independent from the controller. A Lyapunov-based analysis is provided to prove that the developed fault observer converges to the actual fault.

In this paper, we consider the problem of task-space tracking control of robot manipulators. Based on a quaternion representation of the end-effector orientation, we design a class of task-space controllers that ensure asymptotic end-effector position and orientation tracking. To facilitate the control design, we first develop model-based and adaptive full-state feedback controllers. We then present a model-based output feedback controller that eliminates link velocity measurements via a model-based observer. The application of the proposed control strategy to redundant robots is also discussed. Simulation results based on a six-link manipulator system are presented for the output feedback controller.

In this paper, a homography-based adaptive visual servo controller is described to enable a robot end-effector to track a desired Euclidean space trajectory as determined by a sequence of images for the fixed camera configuration. To achieve the objective, a Lyapunov-based adaptive control strategy is employed to actively compensate for unknown depth measurements and the lack of an object model. The error systems are constructed as a hybrid of pixel information and reconstructed Euclidean variables obtained by comparing the images and decomposing a homographic relationship.

A visual servo tracking controller is developed in this paper for a monocular camera system mounted on an underactuated wheeled robot (WMR) subject nonholonomic motion constraints (i.e., the camera-in-hand problem). A prerecorded image sequence (e.g., a video) of three target points is used to define a desired trajectory for the WMR. By comparing the target points from the prerecorded sequence with the corresponding target points in the live image, projective geometric relationships are exploited to construct a Euclidean homography. The information obtained by decomposing the Euclidean homography is used to develop a kinematic controller. A Lyapunov-based analysis is used to develop an adaptive update law to actively compensate for the lack of depth information required for the translation error system.

In this paper, we develop an adaptive torque control input for wheeled mobile robots (WMRs) that can be utilized in a modular manner with parameter estimate update laws to enable unified tracking and regulation, that is, a prediction error based update law ensures the parameter estimate vector is bounded, all of the signals are proven to be bounded. An additional stability analysis is provided to prove that if the adaptive update law is designed such that the prediction error is square-integrable and the estimated inertia matrix is positive-definite, the WMR tracking and regulation errors are globally asymptotically forced to a control term that can be made arbitrarily small.

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.

A Simulink robotic toolkit (SRTK) for the Puma 560 robot manipulator is developed on the MATLAB/Simulink-based platform. Through the use of the real-time Linux target and the real-time Windows target, the SRTK can be executed on the Linux or Win32-based operating systems in real-time. Moreover, the graphical user-friendly nature of Simulink allows the SRTK to be a flexible tool that can easily be customized to fit the specific needs of the user, that is, based on the layered approach of the SRTK, the user can perform operations such as calibration, joint control, Cartesian control, Cartesian PD control, impedance control, some trajectory generation tasks, and real-time simulation of the Puma 560 through a user-friendly MATLAB-based graphical user interface without writing any code. The SRTK allows a researcher to use the Puma 560 without the burden of the external issues related to the control, interface, and software issues, while providing for the flexibility for easily modifying components for increased functionality.

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.

We introduce a new approach to fault detection for robot manipulators. The technique, which is based on the isolation of fault signatures via filtered torque prediction error estimates, does not require measurements or estimates of manipulator acceleration as is the case with some of the previously suggested methods. The method is formally demonstrated to be robust under uncertainty in the robot parameters. Furthermore, an adaptive version of the algorithm is introduced, and shown to both improve coverage and significantly reduce detection times. The effectiveness of the approach is demonstrated by experiments with a two-joint manipulator system.

In this paper, we introduce a new approach to fault detection for robot manipulators. The technique, which is based on the isolation of fault signatures via filtered torque prediction error estimates, does not require measurements or estimates of manipulator acceleration as is the case with some previously suggested methods. The method is formally demonstrated to be robust under uncertainty in the robot parameters. Furthermore, an adaptive version of the algorithm is introduced, and shown to both improve coverage and significantly reduce detection times. The effectiveness of the approach is demonstrated by experiments with a two-joint manipulator system.

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.

A direct adaptive control scheme is developed using orthonormal activation function-based neural networks (OAFNN's) for trajectory tracking control of a class of nonlinear systems. Multiple OAFNN's are employed in these controllers for feedforward compensation of unknown system dynamics. Choice of multiple OAFNN's allows a reduction in overall network size reducing the computational requirements. The network weights are tuned on-line, in real time. The overall stability of the system and the neural networks is guaranteed using Lyapunov analysis. The developed neural controllers are evaluated experimentally and the experimental results are shown to support theoretical analysis. The effects of network parameters on system performance are experimentally evaluated and are presented in this research. The superior learning capability of OAFNN's is demonstrated through experimental results. The OAFNN's were able to model the true nature of the nonlinear system dynamics characteristics for a rolling-sliding contact as well as for stiction.

We consider the problem of designing a boundary controller for a flexible link robot arm with a payload mass at the link's free-end. Specifically, we utilize a nonlinear, hybrid dynamic system model (the model is hybrid in the sense that it comprises a distributed parameter, dynamic field equation coupled to discrete, dynamic boundary equations) to design a model-based control law which asymptotically stabilizes the link displacement while driving the actuator hub's position to a desired setpoint. We then illustrate how the control law can be redesigned as an adaptive controller which achieves the same control objective while compensating for parametric uncertainty including unknown payload mass. The control strategy is composed of a boundary control torque applied to the actuator hub and a boundary control force at the link's free-end. Experimental results are presented to illustrate the performance of the proposed control laws.

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.

A robust neural-network (NN) controller is proposed for the motion control of rigid-link electrically driven (RLED) robots. Two-layer NN's are used to approximate two very complicated nonlinear functions. The main advantage of our approach is that the NN weights are tuned on-line, with no off-line learning phase required. Most importantly, we can guarantee the uniformly ultimately bounded (UUB) stability of tracking errors and NN weights. When compared with standard adaptive robot controllers, we do not require lengthy and tedious preliminary analysis to determine a regression matrix. The controller can be regarded as a universal reusable controller because the same controller can be applied to any type of RLED robots without any modifications.

This paper considers the problem of controlling the position and force of a constrained rigid-link electrically-driven robot manipulator actuated by brushed DC motors (BDC-RLED robot). Based on inexact knowledge of almost all of the system parameters and the lack of link velocity measurements, the integrator backstepping approach is used to design a voltage-level, adaptive position/force controller which ensures semi-global asymptotic tracking for the end-effector position, velocity, and force.

This paper presents an adaptive partial state feedback controller for rigid-link flexible-joint (RLFJ) robots. The controller compensates for parametric uncertainty throughout the entire mechanical system while only requiring measurement of link position and actuator position. To eliminate the need for measuring link velocity and actuator velocity a set of filters is utilized as a surrogate for the unmeasurable quantities. Based on this set of filters, an adaptive integrator backstepping procedure is used to develop a torque input controller which guarantees semiglobal asymptotic link position tracking while also ensuring that all signals remain bounded during closed-loop operation. Simulation results for a two-link RLFJ robot are utilized to validate the performance of the proposed controller.

In this paper, we design an adaptive position/force controller for robot manipulators during constrained motion. The proposed controller can compensate for parametric uncertainty while only requiring measurements of link position and end-effector force. A filtering technique is utilized to produce a pseudo-velocity error signal and thus, eliminate the need for link velocity measurements. The control strategy provides semiglobal asymptotic tracking performance for the end-effector position and the interaction force between the constraint and the end-effector. An experimental implementation of the proposed controller on a two-link planar robot is also presented.

Presents a control strategy for the horizontal position control of a two degree-of-freedom overhead crane. The approach transforms the underactuated crane dynamics into a form similar to the well known ball-and-beam dynamics. A controller is then developed based on the work of Teel (1992, 1995) for the transformed dynamics using a saturation control design technique. The control approach is based on a crane model which includes the typically neglected, higher-order nonlinearities. The resulting controller provides for asymptotic positioning of the crane payload from a large set of initial conditions. Experimental trials demonstrate the performance of the proposed controller while a computer simulation is used to suggest the benefit of the proposed saturation controller over a standard linear controller.

In this paper, we consider the problem of designing a position/force controller for robot manipulators during constrained motion. The proposed controllers are based on exact knowledge of the system dynamics and do not require measurements of link velocity and end-effector force. The elimination of velocity measurements is accomplished by two different methods: 1) a model-based velocity observer, and 2) a high-pass filtering technique. Both of the controllers also include an open-loop force control strategy to achieve semi-global exponential end-effector position/velocity/force tracking.

In this paper, we propose a robust output feedback link position tracking controller for the full-order model used to describe rigid-link flexible-joint (RLFJ) robot manipulators. The method uses a bank of dynamic filters, with the link position tracking error as the input, to generate surrogates for the immeasurable system states. One of these generated filter states is then used in the feedback control strategy. The method is distinguished from previous results by the limited amount of modeling information required, the utilization of only link position measurements, and the overall simplicity of the controller. Even though the approach only requires link position measurements, the controller exhibits good robustness properties while ensuring semi-global uniformly ultimately bounded link position tracking.

In this paper, we present a voltage input controller for robot manipulators actuated by induction motors. The control is designed to compensate for the nonlinear dynamics associated with the mechanical subsystem and the electrical subsystems while only requiring measurements of link position, link velocity, stator winding currents, and exact model knowledge. Specifically, through the use of nonlinear observers for rotor flux and an observed integrator backstepping approach, we obtain semiglobal uniform ultimately bounded link position tracking. The result is semiglobal in the sense that a control parameter must be made sufficiently large relative to the electromechanical initial conditions in order to guarantee that control singularities are avoided. To provide insight into the design procedure, the controller is first presented for a one-link robot manipulator system. We then extend the controller from the one-degree of freedom system to the multidegree of freedom system. Experimental results are then presented to illustrate the performance of the proposed approach applied to a one-link robot. Finally, simulation results are presented to illustrate the tracking performance for the proposed controller applied to a two-link robot manipulator system.

In this paper an adaptive partial state feedback controller is designed for rigid-link electrically-driven (RLED) robot manipulators actuated by multi-phase motors. The controller is based on structural knowledge of the electromechanical dynamics of the RLED robot and measurements of link position and electrical winding current in each of the link actuators. The proposed controller is designed to adapt for parametric uncertainty in the electromechanical dynamics while utilizing a dynamic filter to generate link velocity tracking error information. The controller, adaptation laws, and the pseudo-velocity filter are designed via a Lyapunov-like approach, the benefit of which is that at the end of the design procedure, the controller can be mathematically shown to produce semiglobal asymptotic link position tracking.

In this paper, we present an adaptive partial state feedback position/force controller for rigid-link robot manipulators under constrained motion. The proposed controller can compensate for parametric uncertainty while only requiring measurements of link position and end-effector force. The control strategy provides semi-global asymptotic tracking performance for the position of the end-effector and the interaction force between the constraint and the end-effector.

Presents an approach for designing robust tracking controllers for rigid-link flexible-joint (RLFJ) robot manipulators with redundant actuators in the joints. With the proposed controller, the authors prove that the link tracking error is globally uniformly ultimately bounded (GUUB) in spite of additive bounded disturbances, parametric uncertainty, and other modeling uncertainty. The authors also illustrate how the load at each joint is shared by two actuators. Finally simulation results are presented to illustrate the effectiveness of the proposed controller.>

Two robust controllers are developed for the position and force control of a single n-joint nonredundant manipulator. The control strategies are shown to apply when the manipulator is unconstrained and when it is constrained by a rigid environment. Starting with the model of the robot dynamics and constraints in joint space, a dynamic equation is developed with respect to a set of task space coordinate variables. Using this task space model, the two control strategies are developed for the manipulator, and are shown to be robust with respect to uncertainty in the system. The robust control strategies have the following characteristics: (1) exponential convergence for motion tracking, (2) the controller is continuous for all times of interest, and (3) the controller is the same for constrained and unconstrained motion.>

The authors illustrate how the nonadaptive part of the desired compensation adaptive law (DCAL) is a special case of a class of controllers that can be used to obtain a global stability result for the trajectory following problem for robot manipulators. This class of robot controllers is a simple linear proportional derivative (PD) controller plus additional nonlinear terms that are used to compensate for uncertain nonlinear dynamics. To analyze the stability of this class of controllers, Lyapunov's second method is used to derive a global uniform ultimate boundedness (GUUB) stability result for the tracking error. How the controller gains can be adjusted to obtain better tracking performance in spite of the uncertainty present in the robot manipulator dynamic equation is shown.>

For the trajectory following problem of a robot manipulator, a novel linear learning control law, consisting of the conventional proportional-integral-differential (PID) control law, with respect to position tracking error, and an iterative learning term, is provided. The learning part is a linear feedback control of position, velocity, and acceleration errors. It is shown that this learning control can make the position, velocity, and acceleration tracking errors asymptotically stable in the presence of highly nonlinear dynamics. It is also shown that the proposed control is robust in the sense that no knowledge about nonlinear dynamics is required except for its bounding function. As a consequence, neither linear approximation of nonlinear dynamics nor repeatability of robot motion is required. A constructive algorithm for choosing the control gains is also provided. A simple two-degrees-of-freedom manipulator was simulated to test the proposed learning control law.>

The commenters point out an error in the adaptive impedance-control approach for robotic manipulators given in the above-titled paper by W.-S. Lu and Q.-H. Meng (ibid., vol.7, no.3, p.408-15, June 1991). Lu and Meng assert that their adaptive impedance-control scheme can compensate for uncertainty in the force measurements; however, it is shown that it is possible for the uncertainty in the force measurements to cause the parameter estimates to go unbounded. To remedy this possible instability problem, an additional auxiliary controller is suggested. In their reply, Lu and Meng fail to find any error in the Lyapunov analysis of their algorithm. They are not convinced that one should seek a theoretically asymptotically stable solution with a controller that has a highly undesirable feature.>