Fuzzy PID Control Tuning Design Using Particle Swarm Optimization Algorithm for a Quadrotor

Housny, Halima; Chater, El Ayachi; Fadil, H. El

doi:10.1109/icoa.2019.8727702

Cited by 25 publications

(10 citation statements)

References 12 publications

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“…Namely, the provided simulation results show good attitude stabilization and stable reference trajectory tracking for the (x,y,z)-position andyaw rotation (see e.g. [15] [16]). However, the robustness of the proposed controllers was not sufficiently highlighted.…”

Section: Introductionmentioning

confidence: 87%

“…Based on the observation of birds flying, the particle swarm optimization (PSO) metaheuristic algorithm was initiated by J. Kennedy and R. C. Eberhart in 1997 [32]. Thus, the theory of this algorithm is based on the arbitrary choice of particles to design the initial population in a fixed search space [16]. Then, at each new iteration, a different population is selected according to the position and velocity of each particle.…”

Section: Multidimensional Pso Algorithm Descriptionmentioning

confidence: 99%

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Multi Closed-loop Adaptive Neuro-Fuzzy Inference System for Quadrotor Position Control

Housny¹,

Chater²,

Fadil³

2020

Adv. sci. technol. eng. syst. j.

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This paper deals with a multi closed-loop adaptive neuro-fuzzy inference system (ANFIS) design for the under-actuated quadrotor systems. First, the training data set for the fuzzy inference system is obtained using a proportional integral derivative controller. Then, an initial ANFIS controller is designed, where the integral control action is preserved in the multi-closed-cloop ANFIS for each quadrotor system state. Thereafter, scaling gains are added to the controller inputs/outputs, and a multidimensional PSO algorithm is used to tune all the control parameters. Besides, using a simulation example, the aerial vehicle performances are investigated in the presence of an unknown payload mass parameter. Specifically, the position tracking performances of the proposed multi closed-loop PSObased ANFIS plus integral control strategy is compared with the classical PID, conventional ANFIS, and non-optimized ANFIS plus integral controllers. Thus, using the conducted simulation results, it results that the multi closed-loop PSO-based ANFIS plus integral can achieve perfect translational trajectory-tracking and ensure better attitude stabilization despite unknown quadrotor payload mass parameter. Therefore, the proposed new multi closed-loop PSO-based control strategy may be considered as an efficient controller when considering an arbitrary trajectory-tracking problem for the quadrotor system.

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Section: Introductionmentioning

confidence: 87%

Section: Multidimensional Pso Algorithm Descriptionmentioning

confidence: 99%

Multi Closed-loop Adaptive Neuro-Fuzzy Inference System for Quadrotor Position Control

Housny¹,

Chater²,

Fadil³

2020

Adv. sci. technol. eng. syst. j.

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“…It is relevant to affirm that many research papers deal with PD and PID parameters optimisation with fuzzy logic such as in [11][12][13], but not a lot use neural networks approach.…”

Section: Introductionmentioning

confidence: 99%

Neural networks on-line optimized PID controller with wind gust rejection for a quad-rotor

Jabeur¹,

Seddik

2022

IRASE

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In this paper a complete methodology of modeling and control of quad-rotor aircraft is exposed. In fact, a PD on-line optimized Neural Networks Approach (PD-NN) is developed and applied to control the attitude of a quad-rotor that is evolving in hostile environment with wind gust disturbances and should maintain its position despite of these troubles. Whereas PD classical controllers are dedicated for the positions, altitude and speed control. The main objective of this work is to develop a smart Self-Tuning PD controller for attitude angles control, based on neural networks capable of controlling the quad-rotor for an optimized performance thus following a desired trajectory. Many problems could arise if the quad-rotor is evolving in hostile environments presenting irregular troubles such as wind gusts modeled and applied to the overall system. The quad-rotor has to rapidly achieve tasks while guaranteeing stability and precision and must behave quickly with regards to decision making fronting turbulences. This technique offers some advantages over conventional control methods such as PD controllers. Simulation results are achieved with the use of Matlab/Simulink environment and are established on a comparative study between PD and PD-NN controllers founded on wind disturbances application. These obstacles are applied with numerous degrees of strength to test the quad-rotor comportment. Experimental results are reached with the use of the V-REP environment with which some trajectories are tracked and then applied on a BLADE Inductrix FPV+. These simulations and experimental results are acceptable and have confirmed the efficiency of the proposed PD-NN approach. In fact, this controller has fairly smaller errors than the PD controller and has an improved ability to reject troubles. Moreover, it has confirmed to be extremely vigorous and efficient fronting disturbances in the form of wind disturbances.

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“…Mainly, combining the PID control strategy with intelligent techniques improves the quadrotor control performances. Thus, several recent published works have incorporated intelligent algorithms to ensure online PID parameter adjustment, such as fuzzy logic control (FLC) [21], [22] and neural networks (NN) [23]- [25].…”

Section: Introductionmentioning

confidence: 99%

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

Chater

Housny

Fadil

2022

IJECE

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<span>When the controlled system is subject to parameter variations and external disturbances, a fixed-parameter proportional integral derivative (PID) controller cannot ensure its stabilization. In this case, its control requires online parameter adjustment. Specifically, as the quadrotor is a multi-input multi-output, nonlinear, and underactuated system, robust control is necessary to ensure efficient trajectory tracking flights. In this paper, an adaptive proportional integral derivative (APID) controller is proposed to control the quadrotor systems. This APID-based control strategy uses a two hidden layer deep feedforward network (DFN), where the one-step secant algorithm is chosen for initializing the DFN parameters. All the design steps of the proposed adaptive controller are described. The multidimensional particle swarm optimization (PSO) algorithm is used for tuning the DFN parameters. Then, using two simulation efficiency tests, a comparison between the proposed PSO-based APID-DFN, the (non-optimized) APID-DFN, the feedforward network APID, and the fixed-parameter PID controllers proves much efficiency of the proposed adaptive controller. The results illustrate that the proposed PSO-based APID-DFN controller can ensure good quadrotor system stabilization and achieve minimum overshoot and faster convergence speed for all quadrotor motions. Thus, the proposed control strategy could be considered an additional intelligent method-based adaptive control for unmanned aerial vehicles.</span>

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Fuzzy PID Control Tuning Design Using Particle Swarm Optimization Algorithm for a Quadrotor

Cited by 25 publications

References 12 publications

Multi Closed-loop Adaptive Neuro-Fuzzy Inference System for Quadrotor Position Control

Multi Closed-loop Adaptive Neuro-Fuzzy Inference System for Quadrotor Position Control

Neural networks on-line optimized PID controller with wind gust rejection for a quad-rotor

Adaptive proportional integral derivative deep feedforward network for quadrotor trajectory-tracking flight control

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