Development of Pneumatic Networks Soft Robot with Anti-Windup PID Control

Alifdhyatra, Athar Fadlankahlil; Sunarya, Basilius Agung Yason; Hidayat, Egi; Anshori, Isa; Hadi, Yulyan Wahyu

doi:10.1109/icsgrc55096.2022.9845155

Cited by 3 publications

(2 citation statements)

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“…This strategy still relies on model-based cancellations and, as such, it is potentially unrobust to model uncertainties. Remarkably, [14]- [16] investigated experimentally the use of PID regulators for controlling the shape of soft robots. However, these references do not provide stability proof and assume full actuation.…”

Section: Introductionmentioning

confidence: 99%

P-satI-D Shape Regulation of Soft Robots

Pustina

Borja

Santina

et al. 2023

IEEE Robot. Autom. Lett.

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Soft robots are intrinsically underactuated mechanical systems that operate under uncertainties and disturbances. In these conditions, this letter proposes two versions of PID-like control laws with a saturated integral action for the particularly challenging shape regulation task. The closed-loop system is asymptotically stabilized and matched constant disturbances are rejected using a very reduced amount of system information for control implementation. Stability is assessed on the underactuated dynamic model through the Invariant Set Theorem for two relevant classes of soft robots, i.e., elastically decoupled and elastically dominated soft robots. Extensive simulation results validate the proposed controllers.

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Section: Introductionmentioning

confidence: 99%

P-satI-D Shape Regulation of Soft Robots

Pustina

Borja

Santina

et al. 2023

IEEE Robot. Autom. Lett.

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“…Many researchers and engineers have utilized various control schemes to handle the pneumatic systems (Chen et al, 2021). One of the efficient and successful controllers in this field that has made considerable developments in mechanical systems, specifically pneumatic ones, is the proportional–integral–derivative (PID) compensator that has a simple structure and is a well-known control scheme among others (Alifdhyatra et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A new MOPSO algorithm for solving mathematical test functions and control engineering problems

Mahmoodabadi

Rasekh

2023

Transactions of the Institute of Measurement and Control

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In this article, a new multiobjective particle swarm optimization (MOPSO) algorithm is introduced to improve the performance of a sliding mode based robust fuzzy proportional–integral–derivative (PID) controller. In this regard, the non-dominated solution having minimum number of neighbors is considered as the global best position, while the sigma values of the members are employed to determine the personal best position. A modified multiple-crossover operator is combined with the operators of the particle swarm optimization to significantly increase the convergence speed of the algorithm. To limit the size of the archive, a dynamical elimination scheme defined in the Euclidean space is introduced. Besides, iteration-based linear relations are implemented to adaptively compute the inertia weight and learning coefficients. To evaluate the effectiveness of the introduced MOPSO algorithm, the requirements are conducted by means of three benchmark functions with regard to generational distance, spacing, and maximum spread metrics. This analysis demonstrates that the proposed algorithm operates better through comparison with well-known elitist multiobjective evolutionary algorithms. Moreover, the MOPSO algorithm is applied for optimal design of a hybrid robust fuzzy PID controller for a pneumatic system with two bellows. Conflicting objective functions are considered as the normalized values of overshoot and settling time of the displacement between the bellows that should be simultaneously minimized. The feasibility and efficiency of the strategy are assessed in comparison with the conventional controllers.

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