Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm

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.

Altitude Measurement-Based Optimization of the Landing Process of UAVs

“…To implement this approach, the authors used a software package (Simulink Support Package for Parrot Minidrones) developed by the manufacturer of the Rolling Spider UAV, as well as the motion capture system to fully evaluate the performance of the adopted solution. The gains were tuned with the use of a novel rapid tuning method, based on Fibonacci-search algorithm, outlined in [ 32 ], to achieve optimal results using the data obtained from the motion capture system.…”

“…It should be stressed that the number of iterations and requested number of bootstraps (in the paper equal to 2) imply a deterministic running time of the optimization algorithm, as the function of . The required number of iterations N to achieve selected accuracy satisfies , and the two intermediate points are always selected as below [ 32 ]: where and denotes the j -th Fibonacci number.…”

Integration of CFA 2 FB control scheme with modified shunt active line conditioner ( MSALC ) connected distribution system for mitigation of harmonics

“…The CFA is very latest version of the redesigned metaheuristic algorithms. [48][49][50][51][52][53][54][55] This algorithm represents the color-changing behavior that cuttlefish use for solving global optimization problems of numerical. Reflection and Visibility are the important function in the presented algorithm.…”

“…After that, it will become more and more fundamental to bring together all the optimization systems that they are capable of overcoming these shortcomings and effectively handling indistinguishable problems. The CFA is very latest version of the redesigned metaheuristic algorithms 48‐55 . This algorithm represents the color‐changing behavior that cuttlefish use for solving global optimization problems of numerical.…”

Genetic Algorithm-Based Tuning of Backstepping Controller for a Quadrotor-Type Unmanned Aerial Vehicle

“…Due to their configuration [1], quadrotors can perform tasks that previously had to be performed from a helicopter or airplane by highly qualified personnel, which implies a high cost in terms of the vehicle (helicopter) and personnel (pilot and cameraman); furthermore, the risk to human life is eliminated with UAV. Current UAV can develop different activities such as exploration, transportation, mapping, reconnaissance, environmental monitoring, construction monitoring and surveillance [2][3][4][5][6][7] in civil, police or military activities [8,9]. The advances in robotics, mechatronics, and microelectronics facilitated the development of hardware at a low cost, coupled with the development of real-time processing for navigation.…”