Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

Glida, Hossam Eddine; Abdou, Latifa; Chelihi, Abdelghani; Sentouh, Chouki; Perozzi, Gabriele

doi:10.1177/09544100211025379

Cited by 12 publications

(3 citation statements)

References 39 publications

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“…There has been increasing interest in unconventional control strategies such as neural networks, fuzzy logic, and genetic algorithms (Thomas and Poongodi, 2009;Heidari et al, 2013;He et al, 2015;Bing et al, 2018;Glida et al, 2021). Neural network control is based on learning from the mapping between system input-output data, whereas fuzzy control is based on learning from past experience and expert knowledge to predict and control system behavior (Abouaïssa and Chouraqui, 2019).…”

Section: Data-driven Control Methodsmentioning

confidence: 99%

Model-free control for autonomous prevention of adverse events in robotics

Narayan,

Majewicz Fey

2024

Front. Robot. AI

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Introduction: Preventive control is a critical feature in autonomous technology to ensure safe system operations. One application where safety is most important is robot-assisted needle interventions. During incisions into a tissue, adverse events such as mechanical buckling of the needle shaft and tissue displacements can occur on encounter with stiff membranes causing potential damage to the organ.Methods: To prevent these events before they occur, we propose a new control subroutine that autonomously chooses a) a reactive mechanism to stop the insertion procedure when a needle buckling or a severe tissue displacement event is predicted and b) an adaptive mechanism to continue the insertion procedure through needle steering control when a mild tissue displacement is detected. The subroutine is developed using a model-free control technique due to the nonlinearities of the unknown needle-tissue dynamics. First, an improved version of the model-free adaptive control (IMFAC) is developed by computing a fast time-varying partial pseudo derivative analytically from the dynamic linearization equation to enhance output convergence and robustness against external disturbances.Results and Discussion: Comparing IMFAC and MFAC algorithms on simulated nonlinear systems in MATLAB, IMFAC shows 20% faster output convergence against arbitrary disturbances. Next, IMFAC is integrated with event prediction algorithms from prior work to prevent adverse events during needle insertions in real time. Needle insertions in gelatin tissues with known environments show successful prevention of needle buckling and tissue displacement events. Needle insertions in biological tissues with unknown environments are performed using live fluoroscopic imaging as ground truth to verify timely prevention of adverse events. Finally, statistical ANOVA analysis on all insertion data shows the robustness of the prevention algorithm to various needles and tissue environments. Overall, the success rate of preventing adverse events in needle insertions through adaptive and reactive control was 95%, which is important toward achieving safety in robotic needle interventions.

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Section: Data-driven Control Methodsmentioning

confidence: 99%

Model-free control for autonomous prevention of adverse events in robotics

Narayan,

Majewicz Fey

2024

Front. Robot. AI

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“…The emergence of model-free control (MFC) has garnered attention as a solution to overcome the limitations of model-based control approaches [17]. Although research in MFC is still in the nascent stage, some works on nonlinear control strategies for various industrial systems have been explored [15,[18][19][20][21][22][23][24][25][26][27][28][29][30]. In [18,19], novel MFC control schemes were proposed for quadrotor UAVs using algebraic methods and ultra-local models.…”

Section: Introductionmentioning

confidence: 99%

Optimal Model-Free Finite-Time Control Based on Terminal Sliding Mode for a Coaxial Rotor

Glida,

Sentouh,

Rath

2023

Drones

Self Cite

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This study focuses on addressing the tracking control problem for a coaxial unmanned aerial vehicle (UAV) without any prior knowledge of its dynamic model. To overcome the limitations of model-based control, a model-free approach based on terminal sliding mode control is proposed for achieving precise position and rotation tracking. The terminal sliding mode technique is utilized to approximate the unknown nonlinear model of the system, while the global stability with finite-time convergence of the overall system is guaranteed using the Lyapunov theory. Additionally, the selection of control parameters is addressed by incorporating the accelerated particle swarm optimization (APSO) algorithm. Finally, numerical simulation tests are provided to demonstrate the effectiveness and feasibility of the proposed design approach, which demonstrates the capability of the model-free control approach to achieve accurate tracking control even without prior knowledge of the system’s dynamic model.

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“…This is due to poor performance such as presence of peaks and difficulty of disturbance rejection. Some other methods use complex control algorithms such as fuzzy logic controllers [12]- [16] which is good in performance but that require a lot of processing power. This paper proposes a new algorithm of control using a Lookup table based on Fuzzy PID (LFPID) controller to achieve enhanced stabilization.…”

Section: Introductionmentioning

confidence: 99%

Flight parameters improvement for an unmanned aerial vehicle using a lookup table based fuzzy PID controller

Benbouali

Chabni

Taleb

et al. 2021

IJEECS

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In this paper, a control scheme based on lookup table fuzzy proportionalintegral-derivate (PID) controller for the quadrotor unmanned aerial vehicle (UAV) movement control is proposed. This type of control provides enhanced quadrotor movement control beyond what can be achieved with conventional controllers and has a less computational burden on the processor. The proposed control scheme uses three lookup table based fuzzy logic controllers to control the different movement ranges of a quadrotor (i.e. roll, pitch, and yaw) to achieve stability. The mathematical model of a quadrotor, used to design the proposed controller, is derived based on the Lagrange approach. The processor in the loop (PIL) technique was used to test and validate the proposed control scheme. MATLAB/Simulink environment was used as a platform for the quadrotor model, whereas a low cost and high-performance STM32F407 microcontroller was used to implement the controllers. Data transfer between the hardware and software is via serial communication converter. The control system designed based on simulation is tested and validated using “processor in the loop” techniques.

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Optimal model-free fuzzy logic control for autonomous unmanned aerial vehicle

Cited by 12 publications

References 39 publications

Model-free control for autonomous prevention of adverse events in robotics

Model-free control for autonomous prevention of adverse events in robotics

Optimal Model-Free Finite-Time Control Based on Terminal Sliding Mode for a Coaxial Rotor

Flight parameters improvement for an unmanned aerial vehicle using a lookup table based fuzzy PID controller

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