Multi-UAV Trajectory Control for Stable Ground Target Tracking
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Solution Overview
Problem
Current methods for multi-UAV cooperative tracking of ground-moving targets face challenges in maintaining stability and accuracy due to complex flight environments, obstructed line-of-sight, and computational inefficiencies in model predictive control, particularly with slow convergence and low precision in whale optimization algorithms.
Innovation Solution
A system and method combining model predictive control with an improved whale optimization algorithm, incorporating a terrain building module, flight simulation, UAV parameter generation, and flight trajectory prediction to enhance tracking stability and accuracy by dynamically adjusting weight coefficients and optimizing flight trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model predictive control with whale optimization algorithm is used for multi-UAV cooperative tracking, then tracking accuracy is improved, but computational convergence speed deteriorates
Solution Approach 1:
The patent segments the complex optimization problem into multiple cost functions (horizontal distance cost, obstacle avoidance cost, coverage cost, safety distance cost, communication distance cost, energy consumption cost) that can be evaluated and optimized separately. This segmentation allows the whale optimization algorithm to converge faster by evaluating simpler individual cost components rather than a single complex objective function.
Solution Approach 2:
The patent performs preliminary actions by pre-defining cost functions and their weight coefficients before the optimization process. The terrain building module pre-processes environmental data, and the system pre-establishes the cost model structure, which accelerates convergence by avoiding real-time complex model construction during the optimization loop.
2Stability of the object's composition
If multiple cost functions are considered in the total cost model, then tracking stability is improved, but system complexity increases
Solution Approach 1:
The patent merges multiple cost functions (horizontal distance, obstacle avoidance, coverage, safety distance, communication distance, energy consumption) into a single total cost model. This unified model simplifies the control system by providing a single objective function for optimization, while still incorporating all the necessary tracking stability considerations through the weighted combination of individual cost components.
3Measurement precision
If dynamic weight coefficient adjustment is implemented, then tracking precision is improved, but computational load increases
Solution Approach 1:
The patent implements periodic adjustment of weight coefficients based on the tracking state and environmental conditions. Rather than continuous dynamic adjustment, the system periodically updates weight coefficients at key decision points in the optimization process, which maintains tracking precision while reducing the computational burden of constant recalculation.
Data Source
AI summary
Disclosed herein are a system and a method for controlling multi-UAV cooperative tracking of a ground-moving target. The system includes UAVs, a processor, and a storage device. The UAVs are wirelessly communicatively connected to the processor. The processor is configured to generate a UAV command transmitted to the UAVs. The UAVs are configured to transmit tracking data to the processor. The processor includes a terrain building module, a flight simulation module, a UAV parameter generation module, a flight trajectory prediction module, a flight trajectory determination module, and a flight trajectory control module. The system provided in the embodiments of the present disclosure integrates Model Predictive Control (MPC) with an Improved Whale Optimization Algorithm (IWOA) to enhance the algorithm's convergence speed and precision, thereby improving the accuracy and stability of target tracking.