Optimal Design of Robust Fractional Order PID for the Flight Control System

Kumar, Parvesh; Narayan, Shiv; Raheja, Jetesh

doi:10.5120/ijca2015906759

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…The proposed controllers were applied to the aircraft pitch control. Simulations demonstrated that the FOPID controller using multiobjective bat-algorithm optimization had better performance than others [21]. Ref.…”

Section: Introductionmentioning

confidence: 91%

“…The memory function and stability characteristic make the fractional order PID controller widely applicable in the field of aircraft guidance and control [15,16], such as pitch loop control of a vertical takeoff and landing unmanned aerial vehicle (UAV) [17], roll control of a small fixed-wing UAV [18], perturbed UAV roll control [19], hypersonic vehicle attitude control [20], aircraft pitch control [21], deployment control of a space tether system [22], position control of a one-DOF flight motion table [23], and vibration attenuation to airplane wings [24]. The viscosity of the atmosphere interacting with air vehicles has given the aircrafts the similar aerodynamics to the fractional order systems, thus the fractional order PID control theory is appropriate to design aircraft guidance and control systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Guidance Algorithm against Hypersonic Targets

Chen¹,

Han²,

Ren³

2020

Military Engineering

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This chapter presents a robust guidance algorithm for intercepting hypersonic targets. Since the differential of the line-of-sight rate is more sensitive to the target maneuver, a nonlinear proportional and differential guidance law (NPDG) is given by employing the differential of the line-of-sight rate produced by a nonlinear tracking differentiator. Based on the NPDG, a fractional calculus guidance law (FCG) is presented by utilizing the differential definition of fractional order. On the basis of interceptor-target relative motions, the stability criteria of the guidance system of the FCG are deduced. In different target maneuver and noisy cases, simulation results verify that the proposed guidance laws have small miss distances and the FCG has a stronger robustness.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Robust Guidance Algorithm against Hypersonic Targets

Chen¹,

Han²,

Ren³

2020

Military Engineering

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“…Recently, new techniques have been applied to trajectory tracking of DDMR, like sliding mode control, 10 Fuzzy Output Feedback Stabilization, 11 receding horizon control, 12 Smart PID optimized Neural Networks Approach, 13 distributed Non-Linear Model predictive control, 14 Robust adaptive tracking control, 15 Fractional-Order control, 16,17 output feedback tracking control, 18 Control Lyapunov Function Design, 19 Adaptive neural networks. 20 P. Kumar et al 21 proposed differential evolution technique and Bat algorithm for the multi-objective optimization with fractional-order PID (FOPID) and integer-order PID controllers. The simulation shows that the design of FOPID using a multi-objective bat algorithm gives better results than others.…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

Abed

Rashid

Abedi

et al. 2022

Measurement and Control

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This work proposes a new kind of trajectory tracking controller for the differential drive mobile robot (DDMR), namely, the nonlinear neural network fractional-order proportional integral derivative (NNFOPID) controller. The suggested controller’s coefficients comprise integral, proportional, and derivative gains as well as derivative and integral powers. The adjustment of these coefficients turns the design of the proposed NNFOPID control further problematic than the conventional proportional-integral-derivative control. To handle this issue, an Enhanced Fruit Fly Swarm Optimization algorithm has been developed and proposed in this work to tune the NNFOPID’s parameters. The enhancement achieved on the standard fruit fly optimization technique lies in the increased uncertainty in the values of the initialized coefficients to convey a broader search space. subsequently, the search range is varied throughout the updating stage by beginning with a big radius and declines gradually during the course of the searching stage. The proposed NNFOPID controller has been validated its ability to track specific three types of continuous trajectories (circle, line, and lemniscate) while minimizing the mean square error and the control energy. Demonstrations have been run under MATLAB environment and revealed the practicality of the designed NNFOPID motion controller, where its performance has been compared with that of a nonlinear Neural Network Proportional Integral Derivative controller on the tracking of one of the aforementioned trajectories of the DDMR.

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“…Luo et al [50] interpret that the FOPID implementation provides better performance than traditional methods of tuning classical PID controllers. Several fractional order control methods for aircraft pitch angle control have been proposed [51][52][53]. In Kumar et al [51], a fractional order PID is proposed for the pitch control of the aircraft system using a differential evolution technique for weighted multi-objective optimization.…”

Section: Introductionmentioning

confidence: 99%

Performance improvement of aircraft pitch angle control using a new reduced order fractionalized PID controller

Idir

Bensafia

Khettab

et al. 2022

Asian Journal of Control

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In this paper, a new optimal reduced order fractionalized PID (ROFPID) controller based on the Harris Hawks Optimization Algorithm (HHOA) is proposed for aircraft pitch angle control. Statistical tests, analysis of the index of performance, and disturbance rejection, as well as transient and frequency responses, were all used to validate the effectiveness of the proposed approach. The performance of the proposed HHOA-ROFPID and HHOA-ROFPID controllers with Oustaloup and Matsuda approximations was then compared not only to the PID controller tuned by the original HHO algorithm but also to other controllers tuned by cutting-edge meta-heuristic algorithms such as the atom search optimization algorithm (ASOA), Salp Swarm Algorithm (SSA), sine-cosine algorithm (SCA), and Grey wolf optimization algorithm (GOA). Simulation results show that the proposed controller with the Matsuda approximation provides better and more robust performance compared to the proposed controller with the Oustaloup approximation and other existing controllers in terms of percentage overshoot, settling time, rise time, and disturbance rejection.

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Optimal Design of Robust Fractional Order PID for the Flight Control System

Cited by 9 publications

References 10 publications

Robust Guidance Algorithm against Hypersonic Targets

Robust Guidance Algorithm against Hypersonic Targets

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

Performance improvement of aircraft pitch angle control using a new reduced order fractionalized PID controller

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