All Stabilizing State Feedback Controller for Inverted Pendulum Mechanism

Bitirgen, Rahman; Hancer, Muhsin; Bayezit, İsmail

doi:10.1016/j.ifacol.2018.06.089

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…In [4][5][6][7][8][9] simulation of inverted pendulum has been tested using full sate feedback and the gain of the state feedback has been obtained using linear quadratic regulator and pole placement control. Full state estimator for inverted pendulum on the cart has been simulated and designed.…”

Section: Journal Of Informatics Electrical and Electronicsmentioning

confidence: 99%

Reduced Order Observer Based Pole Placement Design for Inverted Pendulum on Cart

Assefa

2022

J. Infor. Electr. Electron. Eng.

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The inverted pendulum is an under actuated system and unstable system without a controller. In this paper modelling of an inverted pendulum is done using the Euler-Lagrange equation for stabilization of it. The controller gain is evaluated through state feedback and reduced-order observer design techniques and the result for the different initial conditions is compared. The state feedback controller is designed by Pole- placement technique for different desired pole locations. The simulation of the inverted pendulum based on reduced order pole placement design has been done on MATLAB/SIMULINK. It has been observed from the simulation result that the angular velocity and cart speed tracks the system response for different initial conditions by varying the desired pole location for the left-hand plane of the s-plane. In general, if some of the systems are unknown and the other state is known we can design using a re-duced-order observer for any physical system.

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Section: Journal Of Informatics Electrical and Electronicsmentioning

confidence: 99%

Reduced Order Observer Based Pole Placement Design for Inverted Pendulum on Cart

Assefa

2022

J. Infor. Electr. Electron. Eng.

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“…Schematic view of the classification of the meta-heuristic algorithms is as follows: Evolutionary Algorithms: Biogeography Based Optimizer [8], Differential Evolution [9], Evolution Strategy [10], Genetic Algorithms [11], Genetic Programming [12]. Physics-Based Algorithms: Artificial Chemical Reaction Optimization Algorithm [13], Big-Bang Big Crunch [14], Gravitational Search Algorithm [15], Ray Optimization Algorithm [16], Simulated Annealing [17], Small-World Optimization Algorithm [18], Nonlinear Optimization Algorithm [19,20], Constrained Optimization Problem [21], Fractional Gradient Based Algorithm [22], Optimization Problems Based on Hyperbolic Penalty Dynamic Framework [23], Jaya Optimization Algorithm [24,25], Feedback Controller Algorithm [26]. Swarm-Based Algorithms: Ant Colony Optimization [27], Bat-Inspired Algorithm [28], Bee Collecting Pollen Algorithm [29], Cuckoo Search [30], Particle Swarm Optimization [31].…”

Section: Introductionmentioning

confidence: 99%

Differential gradient evolution plus algorithm for constraint optimization problems: A hybrid approach

Tabassum

Akram

Mahmood-ul-Hassan

et al. 2021

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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Optimization for all disciplines is very important and applicable. Optimization has played a key role in practical engineering problems. A novel hybrid meta-heuristic optimization algorithm that is based on Differential Evolution (DE), Gradient Evolution (GE) and Jumping Technique named Differential Gradient Evolution Plus (DGE+) are presented in this paper. The proposed algorithm hybridizes the above-mentioned algorithms with the help of an improvised dynamic probability distribution, additionally provides a new shake off method to avoid premature convergence towards local minima. To evaluate the efficiency, robustness, and reliability of DGE+ it has been applied on seven benchmark constraint problems, the results of comparison revealed that the proposed algorithm can provide very compact, competitive and promising performance.

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“…The works [1][2][3][4] use time optimal control laws for inverted pendulum systems, but with high energy costs, therefore are difficult fit in embedded systems. In the works [5][6][7][8][9], the authors have not introduced the energy saving problem, only interested the quality of the control system. In this paper, the authors presents a quasi time optimal control method and attaches on an inverted pendulum an auxiliary pendulum with elastic coefficient and dissipation coefficient to reduce system energy costs and control quality is not affected.…”

Section: Introductionmentioning

confidence: 99%

An energy saving method of stable control of inverted pendulum system when affected by external interference using auxiliary pendulum

2019

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This research aims to develop a method to reduce energy consumption when controlling an inverted pendulum system that is affected by external interference. In this paper, the authors use the quasi time-optimal control law and add on an inverted pendulum an auxiliary pendulum to absorb the energy of the external interference effects, to reduce the cost of controlling the energy stable inverted pendulum while ensuring system quality. The quality of the method is demonstrated through simulation results. The effectiveness of this method is shown by comparison with the method of no damping.

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All Stabilizing State Feedback Controller for Inverted Pendulum Mechanism

Cited by 5 publications

References 14 publications

Reduced Order Observer Based Pole Placement Design for Inverted Pendulum on Cart

Reduced Order Observer Based Pole Placement Design for Inverted Pendulum on Cart

Differential gradient evolution plus algorithm for constraint optimization problems: A hybrid approach

An energy saving method of stable control of inverted pendulum system when affected by external interference using auxiliary pendulum

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