Abstract
The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process. For example, the sum of absolute values of the tracking error samples or performance indices, including weighed functions of control signal samples, can be penalized to achieve very precise position control, if required. The proposed method relies on an optimization procedure using Fibonacci-search technique fitted into bootstrap sequences, enabling one to obtain a global minimizer for a unimodal cost function. The approach is characterized by low computational complexity and does not require any UAV dynamics model (just periodical measurements from basic onboard sensors) to obtain proper tuning of a controller. In addition to the theoretical background of the method, an experimental verification in real-world outdoor conditions is provided. The experiments have demonstrated a high robustness of the method to in-environment disturbances, such as wind, and its easy deployability.
Highlights
Controller tuning precision significantly influences in-flight properties of any architecture used to control multirotor unmanned aerial vehicles (UAVs)
For the model of the DJI F550 hexacopter developed in Gazebo, the described FGT algorithm has been implemented under the ROS control for tuning of the two parameters, namely k P and k D, given their ranges and the initial value of the second parameter. These values have been taken on the basis of examination of the results of manual tuning of the UAV in manual flight conditions
The authors do not perform here any stability analysis, making an assumption, that the ranges for sought parameters are ’safe’, in the sense that any combination of them does not cause the closed-loop system to become unstable. This is a realistic assumption, as operators of UAVs usually try to fly their machines selecting a set of controller parameters, and based on them, a smallest set of gains can be formulated, leading to searching the locally-best gains between the stabilizing parameters of the controller
Summary
Controller tuning precision significantly influences in-flight properties of any architecture used to control multirotor unmanned aerial vehicles (UAVs). Effective and precise tracking of a desired state during flight, precise positioning, and autonomous landing can be ensured by nonlinear controllers with complicated structure and adaptation mechanisms [1] They usually require knowledge about UAV dynamics, which results in a large computational burden when calculating a control law, made more severe, by the introduction of optimality criteria. Fixed-parameter controllers of PD (proportional-derivative) or PID (proportional-integral-derivative) types are nowadays widely used [2,3,4,5,6] When these controllers are tuned appropriately, they can ensure sufficient performance in most of the required UAV applications, including demanding active interaction with the environment. The introduction of an appropriate optimality criterion in the form of a cost function streamlines the tuning procedure and synthesis of a controller
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