Search

This study studies a critical issue that linear velocity and steering angular velocity of the wheels of omni-directional mobile robot are easily affected by the unknown external disturbances such as wind direction and road flatness during the visual servoing stabilization task. To overcome such challenging problems, this study proposes a disturbance observer based visual servoing control strategy. In configuration, the controller is divided into two parts: the quasi-min-max model predictive controller is designed to accommodate the state constraint and input constraint of the system, and complete the visual stabilization task without disturbance. A nonlinear disturbance observer is designed to estimate the external disturbance and feedback compensate the effect of the actual disturbance on the wheel speed. The simulation results and real experiments show that the proposed control strategy can stably accomplish the visual servoing stabilization task even with a large external disturbance. [ABSTRACT FROM AUTHOR]

This paper proposes a new visual servoing quasi‐min‐max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state‐dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state‐dependent linear error system is covered as linear parameters‐varying models which is used to design the visual servoing quasi‐min‐max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results. [ABSTRACT FROM AUTHOR]

This paper proposes a neural network-based robust model predictive control (MPC) strategy for visual serving of autonomous vehicles (AVs) with uncertain payloads, ground friction disturbances, and air resistance effects. Based on the dynamics of visual servoing errors and driving of the AV, the quasi-min–max MPC is adopted to calculate the desired velocity of the AV subject to the constraints on the speed, control, and the visual field. The backpropagation (BP) neural network is then used to learn the ground friction disturbances of the AV, which is adopted to compensate the quasi-min–max MPC of the visual servoing system. Finally, the performance of the proposed controller is evaluated and verified by some comparison simulations. [ABSTRACT FROM AUTHOR]