Vision-based Adaptive Control Research Articles

Vision-Based Adaptive Trajectory Tracking Control of Wheeled Mobile Robot With Unknown Translational External Parameters

In this article, the vision-based trajectory tracking control problem of wheeled mobile robot is addressed, and different from the traditional vision-based adaptive control methods, the case that the camera is not installed in the center of the mobile robot is considered. In this case, the visual model needs to consider the influence of unknown translational external parameters. To solve this problem, a novel adaptive vision-based trajectory tracking control method is proposed, and a two-stage switching trajectory tracking control law is designed, where the adaptive control method is used to compensate the unknown external parameters. The asymptotic convergence of tracking errors are strictly proved by applying the Lyapunov method. Simulation and experimental results show that proposed approach can effectively control the mobile robot to track the desired trajectory in the presence of unknown external translational parameters.

Vision-Based Nonlinear Incremental Control for a Morphing Wing With Mechanical Imperfections

Morphing structures have acquired much attention in the aerospace community because they enable an aircraft to actively adapt its shape during flight, leading to fewer emissions and fuel consumption. Researchers have designed, manufactured, and tested a morphing wing named SmartX-Alpha, which can actively alleviate loads while achieving the optimal lift distribution. However, the widely existing mechanical imperfections can degrade the performance of the morphing wing and even lead to instabilities. To tackle these issues, this article proposes a vision-based adaptive control approach to actively compensate for mechanical imperfections. In this approach, an incremental model is constructed online to identify the system dynamics using servo commands and vision measurements, and then, nonlinear dynamic inversion control is applied based on the identified model. This data-driven control approach with visual feedback has been validated by real-world experiments on the SmartX-Alpha. The results demonstrate that the vision-based system combined with the proposed control methodology can actively compensate for mechanical imperfections with minimal adjustments to the actual system design. Compared to a controller that only uses a feedforward input-output mapping, this proposed approach improves the system performance and decreases the tracking errors by more than 62% despite disturbances. The results collectively demonstrate the effectiveness of the proposed control system, which sets a foundation for realizing morphing in next-generation aircraft.

Vision-Based Adaptive Neural Positioning Control of Quadrotor Aerial Robot

In this paper, a new vision-based adaptive control algorithm is proposed for the positioning of a quadrotor aerial robot (QAR) with an onboard pin-hole camera. First, the transformation between the position tracking error and image projection error is constructed through the spherical projection method, and then the regulation of the position error is achieved indirectly by stabilizing the image projection error. To overcome the challenge that the dynamics of QAR is physically underactuated, a backstepping-based approach that synthesizes the Lipschitz condition and natural saturation of the inverse tangent function is proposed. In the proposed adaptive controller, an optimized adaptive neural network (NN) means is designed, where only the square of the NN weight matrix’s maximum singular value, not the weight matrix itself, is estimated. Moreover, to facilitate practical application, a novel inertial matrix estimator is introduced in the tuning laws, so that the accurate QAR rotation inertial information is not required. By Lyapunov theory, it is proved that the image projection error converges to an adjustable region of zero asymptotically. The effectiveness of the proposed algorithm has been confirmed by the experimental results.

Vision-based adaptive control of a 3-RRR parallel positioning system

The macro positioning stage with high-precision and rapid positioning ability plays a crucial role in the macro-micro combination positioning system. In this paper, we develop a practical method for the control of a 3-RRR planar positioning system using online vision measurement as feedback. In this method, a monocular vision system is established to accomplish high-precision online pose measurement for the 3-RRR manipulator. Additionally, a robust and operable adaptive control algorithm, which incorporates a fuzzy controller and a PI controller, is employed to achieve precise and rapid positioning of the 3-RRR positioning system. A series of experiments are conducted to verify the positioning performances of the proposed method, and a conventional PI control algorithm is utilized for comparison. The experimental results indicate that using the proposed control approach, the parallel positioning system obtains high precision and shows higher efficiency and robustness, especially for the time-varying positioning system.

Vision-Based Human Tracking Control of a Wheeled Inverted Pendulum Robot.

In this paper, a vision-based adaptive control is designed for a wheeled inverted pendulum (WIP) robot to track a moving human target by integration of multisensor data. A new algorithm is employed in the system to combine an OptiTrack camera and a Kinect camera, such that more robust and efficient performance can be achieved for human target detection and tracking. Robust adaptive control has been developed for the WIP robot to maintain its balance on two wheels and to follow the human target using visual feedback. Leader-follower control, dynamic balance control and visual tracking are efficiently combined together to achieved desired tracking and balancing performance. Extensive experiment studies have been performed to test the effectiveness of the proposed control strategies.

Adaptive vision-based control of robot manipulators using the interpolating polynomial

This paper studies the problem of vision-based adaptive control for robot manipulators under a fixed camera configuration, when the camera intrinsic and extrinsic parameters are uncertain. An image-based controller, which requires the image Jacobian for its implementation, is conducted. The image Jacobian is inversely proportional to the depth factor. Hence it is not linearly parameterizable and the depth factor cannot be adapted with uncertain camera parameters. To cope with the inverse dependence of the image Jacobian on the depth factor, the controller employed the polynomial interpolation to parameterize the image Jacobian matrix linearly. The coefficients of the polynomial are obtained by a trained adaptive-network-based fuzzy inference system. A stability analysis for the proposed method is provided by a full consideration of the non-linear dynamics of the robot manipulator. Simulation results are presented to demonstrate the effectiveness of the proposed approach.