This paper proposes a novel monocular visual servoing strategy, which can drive a wheeled mobile robot to the desired pose without a prerecorded desired image. Compared with existing methods that adopt the teaching pattern for visual regulation, this scheme can still work well in the situation that the desired image has not been previously acquired. Thus, with the aid of this method, it is more convenient for mobile robots to execute visual servoing tasks. Specifically, to deal with nonexistence of the desired image, the reference frame is craftily defined by taking advantage of visual targets and the planar motion constraint, and the pose estimation algorithm is designed for the mobile robot with respect to the reference frame. Then, an adaptive visual regulation controller is developed to drive the mobile robot to the intermediate frame, where the parameter updating law is constructed for the unknown feature height based on the concurrent learning framework. Stability analysis shows that regulation errors and height identification error can converge simultaneously. Afterwards, the mobile robot is driven to the metric desired pose with the identified feature height. Both simulation and experimental results are provided to validate the performance of this strategy.

An indirect adaptive fuzzy prescribed performance control scheme is developed for a class of uncertain multi‐input and multi‐output feedback linearisable non‐linear systems with unknown control direction and external disturbances. The properties of symmetric matrix and hyperbolic tangent function are exploited and the predefined tracking performance boundary is used to design the adaptive fuzzy prescribed performance controller, which can avoid the possible singularity problem and calculating the inverse of matrix. A robust controller equipped with a Nussbaum‐type function is constructed to compensate for the lumped errors and solve the unknown control direction problem. The proposed scheme guarantees that all the signals in the resulting closed‐loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. Simulation results are used to demonstrate the effectiveness of the proposed approach.

This study presents an observer‐based direct adaptive fuzzy control for a class of uncertain single‐input single‐output non‐linear systems with unknown gain sign. A state observer is designed to estimate the unmeasured state, the direct adaptive fuzzy controller is designed in the presence of only system output is available for measurement, and the parameter adaptive laws are updated by fuzzy basis functions rather than by its filtering. A robust controller is designed to compensate for the lumped errors, and a Nussbaum gain function is incorporated in the robust controller to estimate the unknown gain sign. The proposed scheme guarantees that all of the signals in the resulting closed‐loop systems are bounded and that the tracking errors converge asymptotically to zero. A simulation example is used to demonstrate the effectiveness of the proposed scheme.

Purpose: The purpose of this paper is to present a visual servo tracking strategy for the wheeled mobile robot, where the unknown feature depth information can be identified simultaneously in the visual servoing process. Design/methodology/approach: By using reference, desired and current images, system errors are constructed by measurable signals that are obtained by decomposing Euclidean homographies. Subsequently, by taking the advantage of the concurrent learning framework, both historical and current system data are used to construct an adaptive updating mechanism for recovering the unknown feature depth. Then, the kinematic controller is designed for the mobile robot to achieve the visual servo trajectory tracking task. Lyapunov techniques and LaSalle’s invariance principle are used to prove that system errors and the depth estimation error converge to zero synchronously. Findings: The concurrent learning-based visual servo tracking and identification technology is found to be reliable, accurate and efficient with both simulation and comparative experimental results. Both trajectory tracking and depth estimation errors converge to zero successfully. Originality/value: On the basis of the concurrent learning framework, an adaptive control strategy is developed for the mobile robot to successfully identify the unknown scene depth while accomplishing the visual servo trajectory tracking task.