Fractional order control of conducting polymer artificial muscles

İtik, Mehmet; Şahin, Erdinç; Ayas, Mustafa Şinasi

doi:10.1016/j.eswa.2015.06.033

Cited by 18 publications

(10 citation statements)

References 34 publications

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“…The current position and the velocity of each particle are modified by the distance between its current position and pbest , and the distance between its current position and gbest as given in the following. At each step n , by using the individual best position pbest and global best position gbest , a new velocity for the i th particle can be modeled according to the following equation(–):

V_{i} ((), n) = χ ((), V_{i} ((), n - 1) + φ_{1} r_{1} ((), {pbest}_{i} - P_{i} ((), n - 1)) + φ_{2} r_{2} ((), sans-serif g italicbest - P_{i} ((), n - 1))),

where a potential solution is represented by each particle, and it has a position in the search space represented by a position vector P i , r 1 , and r 2 are random numbers between 0 and 1; φ 1 and φ 2 are positive constant learning rates; χ is called the constriction factor and is defined by Equation .

χ = \frac{2}{|2 - φ - \sqrt{φ^{2} - 4 φ}|}, φ = φ_{1} + φ_{2}, φ > 4

…”

Section: Optimization Algorithms For Parametric Tuningmentioning

confidence: 99%

“…The current position and the velocity of each particle are modified by the distance between its current position and pbest, and the distance between its current position and gbest as given in the following. At each step n, by using the individual best position pbest and global best position gbest, a new velocity for the ith particle can be modeled according to the following equation [22][23][24][25] :…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

“…The main finding of the paper is that a new adaptive PSO algorithm is developed for conducting an automatic setting of parameters of FOPID controller. Itik et al proposed a FOPID controller to improve the positioning ability of conducting polymer actuators. For obtaining the optimal controller parameters, cuckoo search and PSO algorithms were used.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay

Bingül

Karahan

2018

Optim Control Appl Methods

130

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Summary This paper deals with optimization and design of an integer order–based and fractional order–based proportional integral derivative (PID) controller tuned by particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms. These algorithms were used to find the best parameters for the best controller performance. A comparative study has been made to highlight the advantage of using ABC‐based controller over a PSO‐based controller. The validity of the controller tuning algorithms was tested in 2 different systems with time delay and a nonminimum phase zero used commonly in process control. The optimal tuning process of the PID and fractional order PID controllers has also been performed with 3 different cost functions. From the perspectives of time‐domain performance criteria, such as settling time, rise time, overshoot, and steady‐state error, the controller tuned by ABC gives better dynamic performances than controllers tuned by the PSO. Moreover, the results obtained from robustness analysis showed that the parameters of controller tuned by ABC are quite robust under internal and external disturbances.

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V_{i} ((), n) = χ ((), V_{i} ((), n - 1) + φ_{1} r_{1} ((), {pbest}_{i} - P_{i} ((), n - 1)) + φ_{2} r_{2} ((), sans-serif g italicbest - P_{i} ((), n - 1))),

χ = \frac{2}{|2 - φ - \sqrt{φ^{2} - 4 φ}|}, φ = φ_{1} + φ_{2}, φ > 4

…”

Section: Optimization Algorithms For Parametric Tuningmentioning

confidence: 99%

“…The current position and the velocity of each particle are modified by the distance between its current position and pbest, and the distance between its current position and gbest as given in the following. At each step n, by using the individual best position pbest and global best position gbest, a new velocity for the ith particle can be modeled according to the following equation [22][23][24][25] :…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay

Bingül

Karahan

2018

Optim Control Appl Methods

130

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“…The concomitant physical and mathematical models have been profusely checked by experimental results and technological applications. The temptation is high to treat electro-chemo-mechanical devices as singular cases of electro-mechanical actuators driven by electric fields and described by adaptation of electro-mechanical physical models [36,56,[82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101].…”

Section: Persisting Conceptual Discrepanciesmentioning

confidence: 99%

Artificial muscles driven by the cooperative actuation of electrochemical molecular machines. Persistent discrepancies and challenges

Otero

2017

International Journal of Smart and Nano Materials

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Here we review the persisting conceptual discrepancies between different research groups working on artificial muscles based on conducting polymers and other electroactive material. The basic question is if they can be treated as traditional electro-mechanical (physical) actuators driven by electric fields and described by some adaptation of their physical models or if, replicating natural muscles, they are electro-chemo-mechanical actuators driven by electrochemical reaction of the constitutive molecular machines: the polymeric chains. In that case the charge consumed by the reaction will control the volume variation of the muscular material and the motor displacement, following the basic and single Faraday's laws: the charge consumed by the reaction determines the number of exchanged ions and solvent, the film volume variation to lodge/expel them and the amplitude of the movement. Deviations from the linear relationships are due to the osmotic exchange of solvent and to the presence of parallel reactions from the electrolyte, which originate creeping effects. Challenges and limitations are underlined.

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“…These five independent parameters should be optimally tuned based on some pre-defined objectives to improve the controller performance. Metaheuristic algorithms such as differential evolution (Biswas et al, 2009), genetic algorithms (GAs) (Copot et al, 2013; Machado, 2010), particle swarm optimization (PSO) (Bingul and Karahan, 2012; Zamani et al, 2009) and more recently Cuckoo search algorithm (CSA) (Itik et al, 2015) were used to optimally tune the parameters of the FOPID controller. In these studies, it is indicated that the FOPID controller improved the control system performance compared to optimized PID controllers.…”

Section: Introductionmentioning

confidence: 99%

Fractional order based trajectory tracking control of an ankle rehabilitation robot

Ayas

Altaş

Şahin

2016

Transactions of the Institute of Measurement and Control

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Human–robot interaction is inherently available and used actively in ankle rehabilitation robots. This interaction causes disturbances to be counteracted on the rehabilitation robots in order to reduce the side effects. This paper presents a fractional order proportional–integral–derivative controller to improve the trajectory tracking ability of a developed 2-degree of freedom parallel ankle rehabilitation robot subject to external disturbances. The parameters of the controller are optimally tuned by using both the cuckoo search algorithm and the particle swarm optimization algorithm. A traditional proportional–integral–derivative controller, which is also tuned using both of the algorithms, is designed to test the performance of the fractional order proportional–integral–derivative controller. The experimental results show that the optimally tuned FOPID controller improves the tracking performance of the ankle rehabilitation robot subject to external disturbances significantly and decreases the steady-state tracking errors compared to the optimally tuned PID controller.

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Fractional order control of conducting polymer artificial muscles

Cited by 18 publications

References 34 publications

Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay

Comparison of PID and FOPID controllers tuned by PSO and ABC algorithms for unstable and integrating systems with time delay

Artificial muscles driven by the cooperative actuation of electrochemical molecular machines. Persistent discrepancies and challenges

Fractional order based trajectory tracking control of an ankle rehabilitation robot

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