Optimizing the double inverted pendulum’s performance via the uniform neuro multiobjective genetic algorithm

Al-Janan, Dony Hidayat; Chang, Hao‐Chin; Chen, Yeh-Peng; Liu, Tung-Kuan

doi:10.1007/s11633-017-1069-8

Cited by 13 publications

(7 citation statements)

References 18 publications

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“…A nonlinear autoregressive moving average L2 controller which is a family of Neural Network controller is used with Resilient backpropagation and Levenberg Marquardt backpropagation Training Algorithm to improve the stability of the pendulum. Genetic algorithm has been used as a popular optimization algorithm in various models to find the optimal input setting for a reverse pendulum that requires proper input: In [10], inverted pendulum performance optimization has been performed through neural network and multiple genetic algorithms. Compared to the trial and error method, this method is significantly faster.…”

Section: Related Workmentioning

confidence: 99%

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Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

Alimoradpour

Rafie

Ahmadzadeh

2021

Preprint

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One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

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Section: Related Workmentioning

confidence: 99%

“…Based on the parameters in Table 3, the optimization output is shown in Figs. (7) to (10). The output of Fig.…”

Section: Investigation Of Optimization Stage and Parameters Of Genetic Algorithmmentioning

confidence: 99%

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

Alimoradpour

Rafie

Ahmadzadeh

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…These developed methods were an internal stabilizing controller and an observer-based state feedback control that replaced the PID controller. Raffo et al [16] developed a nonlinear H ∞ controller for stabilizing twowheeled self-balanced vehicles. The proposed controller takes into consideration the whole dynamics of the system into its structure, ensuring the stability of the overall system′s closed loop.…”

Section: Control Of Inverted Pendulum-based Systemsmentioning

confidence: 99%

Control of a Two-wheeled Machine with Two-directions Handling Mechanism Using PID and PD-FLC Algorithms

Goher

Fadlallah

2019

Int. J. Autom. Comput.

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This paper presents a novel five degrees of freedom (DOF) two-wheeled robotic machine (TWRM) that delivers solutions for both industrial and service robotic applications by enlarging the vehicle′s workspace and increasing its flexibility. Designing a twowheeled robot with five degrees of freedom creates a high challenge for the control, therefore the modelling and design of such robot should be precise with a uniform distribution of mass over the robot and the actuators. By employing the Lagrangian modelling approach, the TWRM′s mathematical model is derived and simulated in Matlab/Simulink®. For stabilizing the system′s highly nonlinear model, two control approaches were developed and implemented: proportional-integral-derivative (PID) and fuzzy logic control (FLC) strategies. Considering multiple scenarios with different initial conditions, the proposed control strategies′ performance has been assessed.

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“…The other inverted pendulum group, which is also the subject of this paper, is the spatial inverted pendulums with two degrees of freedom. Unlike many studies [9,10] in which the rotational axes are parallel to each other and the rotational motion occurs on a single plane, the spatial inverted pendulum is designed so that two rotational axes are orthogonal to each other and the pendulum is prevented from rotating around its own axis and can easily tilt over any direction in 3D space. Spatial inverted pendulum systems are classified in three basic groups depending on the mechanism used to stabilize the pendulum [11].…”

Section: Introductionmentioning

confidence: 99%

Design and control of spatial inverted pendulum with two degrees of freedom

Bayram

Kara

2020

J Braz. Soc. Mech. Sci. Eng.

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The inverted pendulum systems have inherently unstable dynamics. In order to stabilize the inverted pendulum at upright position, an actuation mechanism should generate fast-reactive motions at the pivot point of the system. This paper addressed the design and control of a spatial inverted pendulum with two degrees of freedom (DOF). The first part of the study consists of designing a novel planar two-DOF (PRRRR) actuation mechanism in order to balance the spatial inverted pendulum. The system is underactuated and has inherently extreme nonlinearity and also the restrictions on the actuators. Then, in the second part, a second-order sliding-mode and a linear quadratic Gaussian (LQG) controller have been proposed to control the pendulum within the equilibrium position. Finally the simulation results evaluated in terms of the robustness, time response and stability show that the second-order sliding-mode controller is more robust and has fast response performances in re-stabilizing the spatial inverted pendulum, while LQG controller is better in terms of keeping the system in equilibrium during the long period of time. Keywords Two degrees of freedom inverted pendulum • Spatial inverted pendulum • Sliding-mode control • Linear quadratic Gaussian * Atilla Bayram

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Optimizing the double inverted pendulum’s performance via the uniform neuro multiobjective genetic algorithm

Cited by 13 publications

References 18 publications

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

Control of a Two-wheeled Machine with Two-directions Handling Mechanism Using PID and PD-FLC Algorithms

Design and control of spatial inverted pendulum with two degrees of freedom

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