Finite-Time Learning-Based Optimal Elliptical Encircling Control for UAVs With Prescribed Constraints

This paper addresses an optimal elliptical enclosing problem of Unmanned Aerial Vehicles (UAVs) under prescribed constraints, whose objective is to steer UAVs to fulfill accurate target encirclement while complying with arriving time restrictions and minimum efforts. A novel learning-based approximate optimal control policy including two-stage designs is presented. At the first stage, a steady-state robust control protocol is developed to steer UAVs to precisely travel along a predefined elliptical path based on concise filtering. At the second stage, to address specified-time constraints and gain the online optimization ability, a single-critic based enhanced learning rule is explored to generate an approximate optimal regulator that stabilizes error dynamics and minimizes value functions, wherein specified-time constraints can be handled by encoding inequality conditions as skilled barrier functions, and by making full use of historical data and current information, a finite-time learning mechanism driven by weight errors rather than Bellman errors is proposed to approximate the solution of Hamilton-Jacobi-Bellman (HJB) equation with faster decaying. The distinct merit is that an improved reinforcement learning (RL) paradigm is formulated to prescribe an elliptical circumnavigation with assured time requirements and optimization behaviors, which can greatly outperform non-RL alternatives in maintaining the optimal performance index while exhibiting restriction handling ability via online learning. Lyapunov stability demonstrates that involved error variables are ultimately limited and resultant controller obeys optimality. The feasibility and values of presented algorithm are accessed by comparisons and simulations.