An intelligent controller for ionic polymer metal composites using optimized fuzzy reinforcement learning

Goharimanesh, Masoud; Jannatabadi, Elyas Abbasi; Moeinkhah, Hossein; Sistani, Mohammad Bagher Naghibi; Akbari, Ali Akbar

doi:10.3233/jifs-161211

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Now, using the parameter values in Table 1, the transfer function in (10) can be written as: 𝐺(𝑠) 𝑢(𝑠) = −0.3444 * 10 −3 𝑠 3 + 0.6852𝑠 2 + 11.14 * 10 −2 𝑠 + 0.1725 * 10 −6 (11)…”

Section: Mathematical Modelmentioning

confidence: 99%

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Diabetics blood glucose control based on GA-FOPID technique

Jasim

Mashhadany

2021

Bulletin EEI

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In this paper, an optimized Fractional Order Proportional, Integral, Derivative based Genetic Algorithm GA-FOPID optimization technique is proposed for glucose level normalization of diabetic patients. The insulin pump with diabetic patient system used in the simulation is the Bergman minimal model, which is used to simulate the overall system. The main purpose is to obtain the optimal controller parameters that regulate the system smoothly to the desired level using GA optimization to find the FOPID parameters. The next step is to obtain the FOPID controller parameters and the traditional PID controller parameters normally. Then, the simulation output results of using the proposed GA-FOPID controller was compared with that of using the normal FOPID and the traditional PID controllers. The comparison shows that all the three controllers can regulate the glucose level but the use of GA-FOPID controller was outperform the use of the other two controllers in terms of speed of normalization and the overshoot value.

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Section: Mathematical Modelmentioning

confidence: 99%

“…In [9] two controllers were developed to regulate the insulin level in a type 1 patient, the PID and fuzzy controllers. In [10] a genetic fuzzy PI controller was applied to linearized type 1 diabetes using the gap metric to regulate the patient sugar level. The genetic algorithm was used to tune the fuzzy memberships.…”

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confidence: 99%

Diabetics blood glucose control based on GA-FOPID technique

Jasim

Mashhadany

2021

Bulletin EEI

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“…First, linear control methodologies, such as proportional–integral–derivative (PID) controller (Richardson et al, 2003) and linear quadratic regulator (LQR) controller (Mallavarapu and Leo, 2001), have been applied. To conquer the highly nonlinear and uncertain dynamics of the IPMC actuators, advanced control approaches, such as H ∞ control (Chen and Tan, 2008), adaptive control (Brufau-Penella et al, 2008), robust quantitative feedback control (Ahn et al, 2010; Moeinkhah et al, 2014), operator-based control (Deng and Wang, 2012), and fuzzy logic control (Goharimanesh et al, 2017), have been applied. Yet, now some sliding mode strategies have also been applied to control IPMC actuators.…”

Section: Introductionmentioning

confidence: 99%

Robust super-twisting sliding mode controller with state observer for position tracking of ionic polymer–metal composite actuators

Kolahi

Moeinkhah

2020

Journal of Intelligent Material Systems and Structures

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To date, a novel class of smart materials, known as ionic polymer–metal composites, have been intensively studied because of their huge potential applications in robotics, microelectromechanical systems, biomedical devices, and artificial muscles. The position tracking control of the ionic polymer–metal composite actuators is one of the challenging tasks due to the highly nonlinear, uncertain, and unmodeled dynamics. These inaccurate and unmodeled dynamics cause the unwanted disturbances which reduce the control performance. Therefore, a robust feedback controller for position tracking of the ionic polymer–metal composite actuators is necessary. To address this issue, this article presents a super-twisting sliding mode controller combined with integral-chain differentiator as state observer. The main advantage of the proposed method is its potential to estimate the states of the system continuously. As a result, the controller not only eliminates the parameter uncertainties and external disturbance but also it can overcome the chattering problem. The results confirm the feasibility and efficiency of the proposed method both in simulation and experiment.

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