The stabilization and 3D visual simulation of the triple inverted pendulum based on CGA-PIDNN

Zhang, Xiuling; Fan, Hong-min; Zang, Jiayin; Zhao, Liang; Hao, Shuang

doi:10.1007/s12555-014-0040-5

Cited by 7 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a study by Zhang et al (2015) a PID neural network (PIDNN) controller was optimised using cloud genetic algorithm (CGA) for control of three-stage inverted pendulum. It was observed that CGA provides faster convergence and avoids premature convergence phenomenon of GAs.…”

Section: Introductionmentioning

confidence: 99%

Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

Yuvaraja¹,

Ramya²

2018

IJIMR

View full text Add to dashboard Cite

This manuscript describes the hybrid learning algorithm for training the error optimisation in an MIMO nonlinear system. The automated controller is designed using lenient computation technique with a Levenberg-Marquardt training algorithm. The designed controller is interfaced to a microgrid which has renewable energy sources like solar, wind, fuel cell, or smart battery as input and the output power generated by these sources can be utilised for various grid and atomised applications. The erudition capability and designing methodology of adaptive networks and sturdiness of PID controllers are described. Finally, the study illustrates an offline mode comparison of PID-based ANFIS and neural controllers in terms of settling time, steady state error and overshoot.

show abstract

Section: Introductionmentioning

confidence: 99%

Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

Yuvaraja¹,

Ramya²

2018

IJIMR

View full text Add to dashboard Cite

show abstract

“…Therefore, artificial intelligence (AI) is proposed, and the PID parameters are applied to autotuning such as fuzzy logic (Ahmadreza Abazari & Wu, 2019;Chaiyatham & Ngamroo, 2014), a genetic algorithm (GA; Civelek, Çam, LÜy, & Mamur, 2016;Neath, Swain, Madawala, & Thrimawithana, 2013), a neural network (NN; Jing & Cheng, 2013;Merayo et al, 2017), particle swarm optimization (PSO; Gaing, 2004), and the particle swarm optimization-genetic algorithm (Amir Hossein Fathi,Khaloozadeh, & Shisheie, 2012). Many AI approaches have been proposed and applied to autotune the PID parameter, requiring higher convergence and more memory capacity (Hasanien, 2013;Jing and Cheng, 2013;Xin, Yanghua, Yong, Ran, & Bin, 2013;Zhang, Fan, Zang, Zhao, & Hao, 2015). In a real control system, the high convergence effects the reliability, and memory is limited (Jung, Leu, Do, Kim, & Choi, 2015).…”

mentioning

confidence: 99%

“…Although many AI techniques are successful in autotuning PID parameters, there still exists some suffer experience from the users with the convergence, the execution time, and the experience of designer to obtain the high performance (Hasanien, 2013;Jing & Cheng, 2013;Xin et al, 2013;Zhang et al, 2015). In real applications, the convergence and the execution time are limited (Jung et al, 2015).…”

mentioning

confidence: 99%

A novel optimal PID controller autotuning design based on the SLP algorithm

Pongfai

Zhang

et al. 2019

Expert Systems

View full text Add to dashboard Cite

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

show abstract

Analytical and numerical study of a vibrating magnetic inverted pendulum

2023

View full text Add to dashboard Cite

The current study investigates the stability structure of the base periodic motion of an inverted pendulum (IP). A uniform magnetic field affects the motion in the direction of the plane configuration. Furthermore, a non-conservative force as one that dampens air is considered. Its underlying equation of motion is derived from traditional analytical mechanics. The mathematical analysis is made simpler by substituting the Taylor theory in order to expand the restoring forces. The modified Homotopy perturbation method (HPM) is employed to achieve a roughly adequate regular result. To support the prior result, a numerical method based on the fourth-order Runge-Kutta method (RK4) is employed. The graphs for both the analytic and numerical solutions are highly consistent with one another, which indicates that the perturbation strategy is accurate. The solution time history curve exhibits a decaying performance and indicates that it is steady and without chaos. The resonance and non-resonance cases are found through the stability study by using the time scale method. In all perturbation approaches, the methodology of multiple time scales is actually regarded as a further standard approach. The time history is used to create a collection of graphs. Some graphical representations are used to illustrate how the typical physical values affect the behavior of the discovered solution. It has been discovered that the statically unstable IP can have its instability reduced by raising the spring torsional constant stiffness as well as the damped coefficient. Moreover, the magnetic field has a significant role in the stability configuration, which explains that at higher values of this field, the decaying waves take much more time than the smaller values of this field. Accordingly, it can be employed in various engineering devices that need a certain period of time to be more stable.

show abstract

The stabilization and 3D visual simulation of the triple inverted pendulum based on CGA-PIDNN

Cited by 7 publications

References 14 publications

Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

Lenient computation in controlling the nonlinear system based on adaptive error optimisation in microgrid

A novel optimal PID controller autotuning design based on the SLP algorithm

Analytical and numerical study of a vibrating magnetic inverted pendulum

Contact Info

Product

Resources

About