A Cascaded and Adaptive Visual Predictive Control Approach for Real-Time Dynamic Visual Servoing

Abstract

In the past two decades, Unmanned Aerial Vehicles (UAVs) have gained attention in applications such as industrial inspection, search and rescue, mapping, and environment monitoring. However, the autonomous navigation capability of UAVs is aggravated in GPS-deprived areas such as indoors. As a result, vision-based control and guidance methods are sought. In this paper, a vision-based target-tracking problem is formulated in the form of a cascaded adaptive nonlinear Model Predictive Control (MPC) strategy. The proposed algorithm takes the kinematics/dynamics of the system, as well as physical and image constraints into consideration. An Extended Kalman Filter (EKF) is designed to estimate uncertain and/or time-varying parameters of the model. The control space is first divided into low and high levels, and then, they are parameterised via orthonormal basis network functions, which makes the optimisation- based control scheme computationally less expensive, therefore suitable for real-time implementation. A 2-DoF model helicopter, with a coupled nonlinear pitch/yaw dynamics, equipped with a front-looking monocular camera, was utilised for hypothesis testing and evaluation via experiments. Simulated and experimental results show that the proposed method allows the model helicopter to servo toward the target efficiently in real-time while taking kinematic and dynamic constraints into account. The simulation and experimental results are in good agreement and promising.

Reference Generator for a System of Multiple Tethered Unmanned Aerial Vehicles

“…The MPC approach was used to maintain the tether taut [18]. The MPC, also known as a finite-horizon optimal control problem (FHOCP) approach, was widely used with single untethered [30,31], single tethered [32][33][34], and multi-tethered [23,25] UAVs due to its capability to deal with nonlinear constraints.…”

Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles

“…In this paper, to address the drawbacks of the advanced control strategies in quadrotors mentioned in [14,32], the real-time implementation is addressed. Here, the computationally efficient control architecture for a multivariable system is obtained, which reduces the complexity of the system dynamics and improves its real-time performance [13,33]. The overall MPC strategy is decomposed into two different schemes [16,34].…”

A High-Certainty Visual Servo Control Method for a Space Manipulator with Flexible Joints

“…The above methods focus on the visual servo motion optimization strategy based on obstacle avoidance and rapidity criteria, but there is still a large uncertainty in the coupling between the motion trajectory and the target trajectory. Some scholars have proposed intelligent algorithm control strategies based on artificial neural networks [ 6 , 7 , 8 ], synovial membrane variable structure control [ 9 , 10 , 11 ], and control strategies based on prediction methods [ 12 , 13 , 14 ]. However, the reliability of these methods may not be suitable for aerospace engineering.…”