Controller Design for Rotary Inverted Pendulum System Using Evolutionary Algorithms

Hassanzadeh, Iraj; Mobayen, Saleh

doi:10.1155/2011/572424

Cited by 64 publications

(37 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is satisfied, then the control law (6) ensures the asymptotic stability of the system states and one can obtain F in (6) as F5SX 21 .…”

Section: Theoremmentioning

confidence: 99%

See 1 more Smart Citation

Optimal LMI‐based state feedback stabilizer for uncertain nonlinear systems with time‐Varying uncertainties and disturbances

Mobayen

2015

Complexity

Self Cite

View full text Add to dashboard Cite

This article presents a nonlinear state feedback stabilizer using linear matrix inequalities for a class of uncertain nonlinear systems with Lipschitz nonlinearities. The proposed controller improves the transient performance and steady state accuracy simultaneously. To improve the stabilization performance, a nonlinear function is included in the control law and is optimally tuned using a modified random search algorithm. Simulation results are presented to show the effectiveness of the offered method.

show abstract

“…is satisfied, then the control law (6) ensures the asymptotic stability of the system states and one can obtain F in (6) as F5SX 21 .…”

Section: Theoremmentioning

confidence: 99%

“…To evaluate the performance of the closed-loop system, a multiobjective performance criterion is chosen which includes the settling time T s , and the steady-state error E ss . The proposed cost function is considered as follows [21,22]:…”

Section: Remarkmentioning

confidence: 99%

Optimal LMI‐based state feedback stabilizer for uncertain nonlinear systems with time‐Varying uncertainties and disturbances

Mobayen

2015

Complexity

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…During the last decade, PSO algorithms have gained much attention and wide applications in different fields due to their effectiveness in performing difficult optimization issues, as well as simplicity of implementation and ability to fast converge to a reasonably good solution [14][15][16]. PSO is a population-based heuristic global optimization technique, first introduced by Kennedy and Eberhart [8] and referred to as a swarm-intelligence technique.…”

Section: Overview Of Basic Pso Algorithmsmentioning

confidence: 99%

Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

Şendrescu

2013

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO) techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.

show abstract

Controller Design for Rotary Inverted Pendulum System Using Evolutionary Algorithms

Cited by 64 publications

References 16 publications

Optimal LMI‐based state feedback stabilizer for uncertain nonlinear systems with time‐Varying uncertainties and disturbances

Optimal LMI‐based state feedback stabilizer for uncertain nonlinear systems with time‐Varying uncertainties and disturbances

Control of a chain pendulum: A fuzzy logic approach

Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

Contact Info

Product

Resources

About