Robust control of Inverted pendulum using fuzzy logic controller

Tripathi, Sandeep; Panday, H.; Gaur, Prerna

doi:10.1109/sces.2013.6547577

Cited by 11 publications

(6 citation statements)

References 5 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are two types; Mamdani and Sugeno inference systems are defined. 18 The Mamdani-based fuzzy system gives centroid output by integrating the constantly changing input function. In contrast, a singleton spike-based membership function is used by Sugeno fuzzy system.…”

Section: Fuzzy Logic Controller (Flc)mentioning

confidence: 99%

“…PID and fractional order PID 15,16 are presented for the primary speed control and secondary throttle control loops. Moreover, to tackle the vagueness of the system's parameters, a fuzzy logic controller, 17,18 a backstepping sliding mode controller, 19 a model predictive controller, 20,21 a model reference self-learning controller 22 are proposed as single main control loops for longitudinal speed control. However, constructing such controllers requires a good understanding of the system and the associated parametric uncertainty.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

Tripathi,

Shrivastava,

Jana

2023

Optim Control Appl Methods

View full text Add to dashboard Cite

Today's concern about climate change and the country's economic growth have made adopting hybrid electric vehicles (HEV) a worthy solution. Since the gas emissions of HEVs are closely related to their motor performance, developing an effective HEV motor control technique is essential. The performance and acceleration of an HEV motor are determined by the internal combustion engine (ICE) and the electronic throttle plate (ETP), which employ an air‐fuel mixture for vehicle propulsion. In order to deal with this problem, this article presented an optimal fuzzy controller that controls the throttle plate's angular position and motor speed in HEVs. However, the efficiency and reliability of fuzzy controllers depend upon their gains factor. So, a different heuristic algorithm is employed to self‐tuning the fuzzy logic controller (FLC) gain factor. The performance of the suggested ChOA‐fuzzy‐based controller was evaluated utilizing various control error indices and time‐domain stability. A comprehensive controller performance analysis using different metaheuristic techniques has been carried out to validate the proposed scheme. The findings show that the suggested ChOA‐fuzzy‐based controller performs better than other techniques.

show abstract

Section: Fuzzy Logic Controller (Flc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

Tripathi,

Shrivastava,

Jana

2023

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…If Input 1 = x and Input 2 = y, then Output is z = Ax + By + C (10) where A, B, and C are constant. When the output is constant, it means that the values of A and B are zero.…”

Section: Adaptive Neuro-fuzzy Inference Control (Anfis)mentioning

confidence: 99%

“…In general, the inverted pendulum system has two equilibrium points [10], [11]: the stable downward position, which is undesirable as it requires no control input and thus has no value from a control perspective, and the unstable upright position. To stabilize the unstable upright position of the inverted pendulum, an on-going rectifying mechanism is needed to keep the cart moving continuously in a particular way.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

Al-Mekhlafi¹,

Wahid²,

Aziz³

2018

IJECE

View full text Add to dashboard Cite

The inverted pendulum is an under-actuated and nonlinear system, which is also unstable. It is a single-input double-output system, where only one output is directly actuated. This paper investigates a single intelligent control system using an adaptive neuro-fuzzy inference system (ANFIS) to stabilize the inverted pendulum system while tracking the desired position. The non-linear inverted pendulum system was modelled and built using MATLAB Simulink. An adaptive neuro-fuzzy logic controller was implemented and its performance was compared with a Sugeno-fuzzy inference system in both simulation and real experiment. The ANFIS controller could reach its desired new destination in 1.5 s and could stabilize the entire system in 2.2 s in the simulation, while in the experiment it took 1.7 s to reach stability. Results from the simulation and experiment showed that ANFIS had better performance compared to the Sugeno-fuzzy controller as it provided faster and smoother response and much less steady-state error.

show abstract

“…a stable equilibrium state and a unstable equilibrium state. In stable equilibrium the pendulum is oriented vertically downwards whereas in unstable equilibrium, pendulum is oriented vertically upwards and hence a counter force is required to keep it align to this position [2]. The control objective is to bring the pole to upper unstable equilibrium position.…”

Section: Introductionmentioning

confidence: 99%

A Pid Based Anfis & Fuzzy Control Of Inverted Pendulum On Inclined Plane (Ipip)

Kharola

2016

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Abstract-The objective of this study is to present an offline control of highly non-linear inverted pendulum system moving on a plane inclined at an angle of 10° from horizontal. The stabilisation was achieved using three different soft-computing control techniques i.e. Proportional-integral-derivative (PID), Fuzzy logic and Adaptive neuro fuzzy inference system (ANFIS). A Matlab-Simulink model of the proposed system was initially developed which was further simulated using PID controllers based on trial and error method. The ANFIS controller were trained using data sets generated from simulation results of PID controller. The ANFIS controllers were designed using only three membership functions. A fuzzy logic control of the proposed system is also shown using nine membership functions. The study compares the three techniques in terms of settling time, maximum overshoot and steady state error. The simulation results are shown with the help of graphs and tables which validates the effectiveness of proposed techniques.

show abstract

Robust control of Inverted pendulum using fuzzy logic controller

Cited by 11 publications

References 5 publications

Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

Chimp optimization‐based fuzzy controller for hybrid electric vehicle speed control using electronic throttle plate

Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

A Pid Based Anfis & Fuzzy Control Of Inverted Pendulum On Inclined Plane (Ipip)

Contact Info

Product

Resources

About