Adaptive control of rotary inverted pendulum system with time-varying uncertainties

Chen, Yung-Feng; Huang, An‐Chyau

doi:10.1007/s11071-013-1112-4

Cited by 55 publications

(33 citation statements)

References 32 publications

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“…so thaṫ( , ) = − 2 < 0 for ̸ = 0. Comparing (17) and (20), the first items of and are the same while the second item of contains parameter and does not contain . In Section 4, whether the control law can stabilize Rotary Inverted Pendulum will be verified by experiments, based on which the control performances of , , and a LQR controller will be compared.…”

Section: Design Of Control Laws Via Lyapunov Methodmentioning

confidence: 99%

“…Except these classical control techniques, several advanced control methods are also used to design controllers to stabilize the pendulum in upright vertical position. For example, Chen and Huang proposed an adaptive controller to bring the pendulum close to the upright position regardless of the various uncertainties and disturbances [20]. Hassanzadeh and Mobayen applied evolutionary approaches which include genetic algorithms (GA), PSO, and ant colony optimization (ACO) methods to balance the pendulum in inverted position [21].…”

Section: Introductionmentioning

confidence: 99%

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Stabilizing a Rotary Inverted Pendulum Based on Logarithmic Lyapunov Function

Wen

Shi

2017

Journal of Control Science and Engineering

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The stabilization of a Rotary Inverted Pendulum based on Lyapunov stability theorem is investigated in this paper. The key of designing control laws by Lyapunov control method is the construction of Lyapunov function. A logarithmic function is constructed as the Lyapunov function and is compared with the usual quadratic function theoretically. The comparative results show that the constructed logarithmic function has higher numerical accuracy and faster convergence speed than the usual quadratic function. On this basis, the control law of stabilizing Rotary Inverted Pendulum is designed based on the constructed logarithmic function by Lyapunov control method. The effectiveness of the designed control law is verified by experiments and is compared with LQR controller and the control law designed based on the quadratic function. Moreover, the system robustness is analyzed when the system parameters contain uncertainties under the designed control law.

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Section: Design Of Control Laws Via Lyapunov Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stabilizing a Rotary Inverted Pendulum Based on Logarithmic Lyapunov Function

Wen

Shi

2017

Journal of Control Science and Engineering

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“…Specifically, the control of the rotary inverted pendulum has been analyzed by several authors in the last years producing different control strategies, as for example fuzzy cascade control 22,23 , evolutionary algorithms 24 , adaptive control 25 or the control based on an Takagi-Sugeno models 26,27 . Other intelligent techniques such as neural networks have been widely applied to the identification of nonlinear system, and therefore, the inverted pendulum in general and the rotary one have been chosen as a prime example 28,29 .…”

Section: Co-published By Atlantis Press and Taylor And Francismentioning

confidence: 99%

Control of a chain pendulum: A fuzzy logic approach

Aranda-Escolástico¹,

Guinaldo²,

Santos³

et al. 2016

IJCIS

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In this paper we present a real application of computational intelligence. Fuzzy control of a non-linear rotary chain pendulum is proposed and implemented on real prototypes. The final aim is to obtain a larger region of attraction for the stabilization of this complex system, that is, a more robust controller. As it is well-known, fuzzy logic exploits the tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost when dealing with complex systems. In this case, the control strategy is based on a Takagi-Sugeno fuzzy model of the strongly non-linear multivariable system. Simulation and experimental results on the real plant have been obtained and tested in a rotary inverted pendulum and in a double rotary inverted pendulum. They have been compared to other feedback control strategies such as Full State Feedback or Linear Quadratic Regulator with encouraging results. Fuzzy control allows to enlarge the stability region of control. Indeed, the region of attraction and therefore the stabilization has been enlarged up to over 17% for the real system.

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“…Se diseña el controlador adaptativo y luego se realiza la prueba de estabilidad en lazo cerrado mediante el teorema de Lyapunov. Se realiza experimentación y simulación [25].…”

Section: Control De Péndulos Invertidosunclassified