Multi-objective-optimization-based control parameters auto-tuning for aerial manipulators

Abstract

The aerial manipulator has recently attracted much research attention due to its wide applications such as aerial cleaning, aerial transportation, and aerial manipulation. It is important to design a reliable controller for the aerial manipulator to robustly perform aerial tasks with different settings. However, current controllers still employ manual parameters tuning methods, which is mostly limited to a specific setting like a fixed aerial manipulator configuration or an unchanged environment. In fact, there could be diverse configurations of aerial manipulators and uncertain environments in practice, which requires the manual tuning process to be frequently repeated. This repetition is easy to be unavailable due to its significant cost of time and expensive involvements of control-tuning experts. To solve these problems, a novel multi-objective-optimization-based control parameters auto-tuning method is proposed for the aerial manipulator. Based on a conventional proportional–integral–derivative control structure, an evolutionary-algorithm-based optimization is used to automatically find optimal proportional–integral–derivative control parameters to satisfy conflicting objectives such as minimizing the integrated time square error and the control rate. Simulation results prove that the proposed method can achieve better control performances like smaller overshoots and faster stabilization time than manual tuning methods.