Fractional Order PID-type Feedback in Fixed Point Transformation-based Adaptive Control of the FitzHugh-Nagumo Neuron Model with Time-delay ⁎ ⁎This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 679681). Tamás Faitli has been supported by the “New National Excellence Program of the Ministry of Human Capacities”, application number UNKP-17-1-I, for the period 01 September 2017 – 30 June 2018.

Tar, József K.; János, Földesi; Kovács, Levente; Faitli, Tamás

doi:10.1016/j.ifacol.2018.06.108

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…In both refs. [41] and [42], it was shown that this type of fractional feedback can yield better transient response than conventional PID control solution. If α, 1 and 2 parameters are set correctly, the initial overshoot of the system can be reduced or sometimes completely evaded.…”

Section: Fractional Order Computed Torque Controlmentioning

confidence: 99%

“…To tackle this problem, a simple solution was introduced in refs. [41,42], in which the numerical issues were avoided by applying an integer order integral of the fractionally derivated term. The idea is that consider the derivative term in (5) as the integral of the second derivative (ė(t) = ė(t 0 ) + t t 0 ë(ξ )dξ ), in which the derivative can be replaced with a fractional order α ∈ [0, 1] derivative as ė(t) = t t 0 𝒟 α t 0 ė(ξ ) dξ .…”

Section: Fractional Order Computed Torque Controlmentioning

confidence: 99%

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Fractional order inspired iterative adaptive control

Varga,

Tar,

Horváth

2023

Robotica

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Although several studies have revealed that fractional order controllers usually outperform conventional integer-order control solutions, fractional order controllers are not yet widely applied in industrial applications due to their complex mathematical background. In this paper, further improvements of a simple weighted sum feedback design are introduced that imitates the behavior of a fractional order controller but is free from its various formal restrictions. The proposed control solution has the main characteristics of a fractional order controller, such as finite memory length, excellent transient response with no overshoot and robust behavior, but it is placed into a much simpler mathematical framework. In the current paper, a simple derivative term was incorporated in the design which made the controller’s output more stable by completely eliminating output chattering. The proposed control method was developed for a general second-order system. It was tested in a fixed point iteration-based adaptive control scenario, through simulations using a robotic example and on experimental basis as well, utilizing a simple one-degree-of-freedom electromechanical system. The presented experiments are the first systematic investigations of the fixed point iteration-based adaptive control method.

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Section: Fractional Order Computed Torque Controlmentioning

confidence: 99%

Section: Fractional Order Computed Torque Controlmentioning

confidence: 99%

Fractional order inspired iterative adaptive control

Varga,

Tar,

Horváth

2023

Robotica

Self Cite

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“…The PID-based tracking policy still has huge initial signal swinging that should be reduced. In the literature, various fractional order controllers can be found that tackle this and similar problems (e.g., [50][51][52][53][54][55][56]). More general information can be obtained from resources [57,58].…”

mentioning

confidence: 99%

Preliminary Design of a Receding Horizon Controller Supported by Adaptive Feedback

Issa

Tar

2022

Electronics

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Receding horizon controllers are special approximations of optimal controllers in which the continuous time variable is discretized over a horizon of optimization. The cost function is defined as the sum of contributions calculated in the grid points and it is minimized under the constraint that expresses the dynamic model of the controlled system. The control force calculated only for one step of the horizon is exerted, and the next horizon is redesigned from the measured initial state to avoid the accumulation of the effects of modeling errors. In the suggested solution, the dynamic model is directly used without any gradient reduction by using a transition between the gradient descent and the Newton–Raphson methods to achieve possibly fast operation. The optimization is carried out for an "overestimated" dynamic model, and instead of using the optimized force components the optimized trajectory is adaptively tracked by an available approximate dynamic model of the controlled system. For speeding up the operation of the system, various cost functions have been considered in the past. The operation of the method is exemplified by simulations made for new cost functions and the dynamic control of a 4-degrees-of-freedom SCARA robot using the simple sequential Julia language code realizing Euler integration.

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Fractional Order Calculus-Inspired Kinematic Design in Adaptive Control

Varga

Horváth

Tar

2022

Mechanisms and Machine Science

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Cited by 7 publications

References 24 publications

Fractional order inspired iterative adaptive control

Fractional order inspired iterative adaptive control

Preliminary Design of a Receding Horizon Controller Supported by Adaptive Feedback

Fractional Order Calculus-Inspired Kinematic Design in Adaptive Control

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