Overcoming Limitations of Uncalibrated Robotics Visual Servoing by means of Sliding Mode Control and Switching Monitoring Scheme

Oliveira, Tiago Roux; Leite, Antonio C.; Peixoto, Alessandro Jacoud; Hsu, Liu

doi:10.1002/asjc.899

Cited by 25 publications

(26 citation statements)

References 52 publications

(150 reference statements)

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“…Similar to the situation for the controller in the previous section, when noisy measurements are available, the stopping condition of the adaptive law (11) cannot be realized because the noise renders it impossible to attain zero estimation error. In this case also, a more realistic gain adaptation law is (12), where the size of o has to be selected according to the noise amplitude. Note that because the larger the observer gain, the stronger is the influence of noise in the estimation.…”

Section: Remarkmentioning

confidence: 99%

Second‐order sliding mode output feedback controller with adaptation

Negrete-Chavez

Moreno

2016

Adaptive Control & Signal

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An adaptive second-order sliding mode output feedback controller is developed to deal with the case that the bound of the uncertainty/perturbation is unknown. The control structure consists in a twisting controller and a super-twisting observer to estimate the unmeasured variable. The gains of the controller and observer are parametrized in terms of a scalar gain, such that increasing these two gains, it is always possible to find values to (finite-time) stabilize the closed loop system. Finally, adaptive gain laws are provided to increase the controller and the observer gains until the closed loop has been stabilized. The main technical contribution of the paper is to give a sound and non-trivial Lyapunov analysis of this otherwise intuitively simple idea. We illustrate the performance of the proposed controller by means of experimental results in a laboratory setup.Successful implementation of high-order sliding mode controllers requires the knowledge of the bound of the perturbation, because the appropriate gains for controller and differentiator are functions of these bounds [6][7][8][9]11]. These bounds are usually constants, but they can take also the form of a (known) time-varying signal [11]. When a bound for the perturbation is not known a priori, one is usually led to select an overestimated value, with the consequence of a high chattering amplitude. Recently, some strategies to adapt the values of the gains have been also proposed. For example, for classical sliding modes, Oliveira et al. [12] propose an adaptive approach to overcome limitations of uncalibrated robotics visual servoing. In [13], an adaptive second-order sliding mode controller is presented based on the estimation of the equivalent control by using only output measurements.Recently, for SOSM, two approaches to adapt the gain have been proposed. The first attack is to reconstruct the perturbation and to adapt the gain according to its estimated value. In [14], an adaptive super-twisting controller is presented. The adaptive gain 'follows' the equivalent control, which is obtained by filtering out the control signal by a low-pass filter. This method can track the required values of the gains very closely, but it requires the knowledge of the bound of the derivative of the perturbation. Furthermore, a delay on the control signal is presented because of the filtering process.The second approach to adapt the sliding mode controller gain is to detect the sliding mode: the idea is to increase the gain if the sliding mode is not established and decrease the gain when the sliding mode has been established. For example, in [15], an adaptive twisting controller is presented, assuming that the full state vector is available, while in [16], an adaptive super-twisting controller is developed. In [17], an adaptive super-twisting differentiator is designed for fault detection. In [18], an adaptive twisting controller is used on the joint position tracking control of industrial robots. The adaptation is based on detection of the sliding mode and is use...

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Section: Remarkmentioning

confidence: 99%

Second‐order sliding mode output feedback controller with adaptation

Negrete-Chavez

Moreno

2016

Adaptive Control & Signal

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“…It is worth mentioning that the usual restriction because of the misalignment angle of the camera be less than 2 rad could be removed by using a novel visual servoing strategy via sliding mode control and the monitoring function-based switching scheme proposed in [38].…”

Section: Remarkmentioning

confidence: 99%

“…Conversely, in some cases, full linear parameterization cannot be achieved and, consequently, adaptive control cannot be implemented. However, it is well-known that robust control schemes or adaptive control strategies with robustifying terms can be properly designed, to deal with non-parametric uncertainties (e.g., measurement noise, disturbances, unmodeled dynamics, computation roundoff error and sampling delay) such as, the sliding mode control approach [38,41].…”

Section: Cascade Control Strategymentioning

confidence: 99%

“…Here, we assume that M and p v are chosen so that p vm represents the desired state response of the planar tracking subsystem. Notice that, from the reference models (38) and (49), we can define the vector of the desired image feature as vm WD OE p T vm a vm T . Again, using the Assumption (A2) from Section 2 implies that a v is positive and bounded and, thus, we can define the planar tracking error as…”

Section: Adaptive Planar Trackingmentioning

confidence: 99%

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Passivity‐based adaptive 3D visual servoing without depth and image velocity measurements for uncertain robot manipulators

Leite

Lizarralde

2016

Adaptive Control & Signal

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In this work, we consider the 3D visual tracking problem for a robot manipulator with uncertainties in the kinematic and dynamic models. The visual feedback is provided by a fixed and uncalibrated camera located above the robot workspace. The Cartesian motion of the robot end effector can be separated into a 1D motion parallel to the optical axis of the camera and a 2D motion constrained on a plane orthogonal to this axis. Thus, the control design can be simplified, and the overall visual servoing system can be partitioned in two almost-independent subsystems. Adaptive visual servoing schemes, based on a kinematic approach, are developed for image-based look-and-move systems allowing for both depth and planar tracking of a reference trajectory, without using image velocity and depth measurements. In order to include the uncertain robot kinematics and dynamics in the presented solution, we develop a cascade control strategy based on an indirect/direct adaptive method. The stability and convergence properties are analyzed in terms of Lyapunovlike functions and the passivity-based formalism. Numerical simulations including hardware-in-the-loop results, obtained with a robot manipulator and a web camera, are presented to illustrate the performance and feasibility of the proposed control scheme. and, therefore, the control system could exhibit degraded performance particularly when the robot is driven by direct-drive actuators or it has to perform high-speed tasks. Some exceptions can be found in earlier published works [16][17][18]. However, the proposals are applied only to the planar visual servoing problem, and their robot motion controllers based on the dynamic model require the measurement of the image velocity and, thus, the need of using an extra sensor. In addition, the measurement of the velocity for a moving target object from a sequence of images is impaired by the noisy image data, affecting the accuracy of the result [19,20].In this context, adaptive schemes for camera calibration without using visual velocity were proposed for 2D image-based visual servoing systems, with global stability properties under the assumption of exact knowledge of the robot mechanical parameters [21,22]. Adaptive control strategies for 2D visual tracking with uncertain robot manipulators were also developed, where the estimated Jacobian matrix of the camera-robot systems parameters has been simultaneously updated in an indirect manner [23]. Likewise, the uncertain Jacobian matrices related to the robot kinematics and the camera calibration parameters could be independently updated by using an indirect/direct adaptive schemes, respectively [24].Conversely, the robot motion control problem in a 3D Cartesian coordinate system has been addressed from the development of a 2-1/2-D visual servoing approach by using different choices of camera architectures [25,26]. As a restriction, some of the proposed solutions require the direct estimate of the depth information with respect to the camera frame, resorting to an off-line learning p...

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“…Many adaptive methodologies have been explored to remove the obstacle of considering disturbances of known bounds in the control design. Examples are the use of monotonically increasing gains that, as the name suggests, increases the switching gains, similar to the results of adaptive stabilization, until a sliding mode is achieved. The price to be paid in these schemes is that, when the disturbances reduce to lower magnitudes, the gains keep unnecessarily high, thus increasing the effects of the chattering phenomenon .…”

Section: Introductionmentioning

confidence: 99%

Adaptive unit vector control of multivariable systems using monitoring functions

Hsu

Oliveira

Cunha

et al. 2018

Intl J Robust & Nonlinear

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Summary An adaptive sliding‐mode unit vector control approach based on monitoring functions to deal with disturbances of unknown bounds is proposed. An uncertain multivariable linear system is considered with a quite general class of nonsmooth disturbances. Global stabilization/tracking is demonstrated using either state or output feedback. The proposed adaptation method makes the control gain less conservative, becoming large enough when the disturbance grows and becoming smaller when it decreases, leading to reduced chattering effects. In contrast to previous methods, the new switching scheme is able to guarantee a prespecified transient time, maximum overshoot, and steady‐state error for multivariable uncertain plants. The proposed technique is applied to the trajectory tracking control of a surface vessel subjected to ocean currents, wind, and waves. Simulations are presented to show the performance of the new adaptation scheme in this adverse scenario of possibly growing, temporarily large, or vanishing exogenous disturbances.

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Overcoming Limitations of Uncalibrated Robotics Visual Servoing by means of Sliding Mode Control and Switching Monitoring Scheme

Cited by 25 publications

References 52 publications

Second‐order sliding mode output feedback controller with adaptation

Second‐order sliding mode output feedback controller with adaptation

Passivity‐based adaptive 3D visual servoing without depth and image velocity measurements for uncertain robot manipulators

Adaptive unit vector control of multivariable systems using monitoring functions

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