Finite-Time Composite Learning-Based Elliptical Enclosing Control for Nonholonomic Robots Under a GPS-Denied Environment
研究了无GPS环境下非完整机器人的椭圆包围控制，提出基于轴承测量的相对位置观测器和有限时间复合神经网络学习控制律，实现未知目标的椭圆包围和精确扰动补偿。
This article investigates a finite-time composite learning-based elliptical enclosing control for nonholonomic robots under a global positioning system (GPS)-denied environment. At the kinematic level, following a prediction and innovation architecture, a novel bearing measurement-based relative position observer formulated in a local coordinate is proposed to assure an exponential decaying of estimation errors without the aid of GPS. Utilizing the observation outcomes, an elliptical guidance law with a time-varying enclosing radius and nonorthogonal tangential and axial vectors is established to yield the reference velocity and angular rate to be tracked. At the kinetic level, by constructing filtering operations and auxiliary variables to extract weight errors, a special finite-time composite neural learning driven by weight and tracking errors is devised to reinforce parameter convergences, then an anti-disturbance kinetic control rule is designed to achieve online precise disturbance compensation and finite-time error convergence. The distinct merit is that an elliptical surrounding concerning an unknown target can be fulfilled with the finite-time neural learning capability while eliminating the deployment of GPS, which is nontrivial and challenging than reported circumnavigation alternatives either relying on the accessibility of GPS or neglecting kinetic uncertainties. An input-to-state stable criterion is applied to demonstrate the boundedness of a closed-loop system. Simulations are provided to confirm the utility of the considered strategy.